DEV Community: EzSecure

Who Has Access to Your Cloud? Why It Matters for Security

EzSecure — Thu, 10 Sep 2026 08:02:36 +0000

The cloud platforms have made it very convenient for businesses to store files, work with teams and get information from almost anywhere. Share a document with a colleague in seconds, add a new employee to a workspace quickly and teams can work together without being in the same office.

But this convenience can also lead to a simple problem: too many people may have access to more information than they actually need.

An employee can change roles and retain their former permissions. A former employee’s account may stay active. A file can be shared with the whole team but only 2 people need it. External users can also have access to folders containing confidential business information.

The bigger problem is that access can change all the time. Without regular visibility and review, businesses may not know who is accessing their cloud data.

Why Cloud Access Matters

Cloud security is about more than just securing the cloud platform itself. It’s also about who can access the information stored there, and what they can do with it.

Think of a shared folder with customer data. If you need 10 employees to have access, you are exposing yourself unnecessarily by giving access to 50. The same can be said for financial documents, employee records, contracts, business plans, and other confidential information.

The more accessible information is , the more difficult it is to control where that information can go.

This is the reason why access management is a key aspect in keeping a secure cloud environment.

Employees Should Only Have the Access They Need

Access management is based on a simple principle: people should have access to the information they need to do their jobs, and not much more.

For example, a member of the marketing team may require access to campaign documents and brand assets. They probably don’t need access to payroll or sensitive HR files, for example.

Similarly, a finance employee may require financial reports but may not need access to all the customer support folders.

This limits the exposure to only what is necessary for the user to do.

This approach is often referred to as least privilege.

It sounds simple, but it can become difficult to stay on top as organisations grow.

Employee Roles Change

People don’t remain in the same job forever.

A person might switch from sales to marketing, take on management responsibilities or transfer to another department. They may require different access for their new role but sometimes their old permissions can be grand-fathered in.

This can accumulate permissions over time.

Permissions are often never reviewed, so an employee who initially needed access to five folders may end up with access to twenty.

That’s why access should be reviewed when employees change roles, not just when they join or leave the company.

Former Employees Can Leave Access Behind

Another key area is offboarding employees.

When someone leaves an organization, their account should be disabled and their access reviewed as part of the offboarding process.

The problem is compounded when employees have access to multiple cloud applications, shared folders, documents or outside collaboration spaces.

An abandoned account can be an unnecessary access point for company information.

Therefore, businesses should include cloud access in their employee onboarding and offboarding processes.

External Sharing Can Create Another Risk

Cloud collaboration enables the effortless sharing of information with people outside the company.

A document can be shared with:

Customers
Consultants
Business partners
Contractors
Suppliers
Different external email addresses

Sharing to external entities is great, but should also be kept track of!

The document holding secrets might have the external person as its viewer, still be active and available long after the initial work has been completed.

Regular external sharing checkups should take place and access removal done if no permission is granted.

Not All Data Needs the Same Level of Access

Yet another critical aspect is the necessity for access rules to be decided by the nature of information.

Public marketing brochures do not require the same degree of access controls that employee payroll records do for instance).

So the first thing is that you really need to know what information you’ve got stored in the cloud.

Businesses should therefore be able to identify and classify types of information such as:

Customer data
Employee personnel files
Company financial statements
Identity details
Legal agreements
Future company strategies
Creative materials
Secret documents that are only shared internally

With the sensitive data identified it is possible for access reviews to be directed at the data that is most important.

Shared Folders Can Become Difficult to Manage

Shared folders are quite nice to have, especially for teams working on shared projects.

However, after a certain point folders can become messy, having lots of files, users

groups and settings.

If a folder has originally been

set up for a project, the folder can continue to survive in the years to come.

The workers of the initial project can still have permission to use it

even though they may have

changed teams.

As a consequence, you end up with a situation in which it is impossible for anyone to know completely who has access to which resources.

Frequent access

reviews will allow companies identify users or roles that no longer need permission and

miscellaneous or obsolete sharing agreements.

Cloud Access Should Be Reviewed Regularly

Access management is never a once and done job.

If companies are to maintain effective access control, they need to set up a process of periodically reviewing who has got how:

Who is granted access?
What kind of access are they allowed?
Has this level of access become unnecessary?
Do people outside of the organization need to have their access reviewed?
Has there been an instance where the former employees are still listed as having access?
Do privileged accounts still get the right level of control?
Do sensitive files get unnecessarily widespread sharing?

The interval at which these reviews will be conducted can vary from one organization to another and can be influenced in part by the latter’s size, the type of data being managed, and the compliance with regulations.

The most important thing is that the reviews should be made an integral part of the normal business activity rather than be reserved for the time when an incident has occurred.

Visibility Comes Before Better Access Control

One of the biggest issues is that organizations are unable to evaluate that which is hidden from view.

Knowledge of the cloud platform that the organization is using, for example, cannot help if confidential business data, sensitive customer information or intellectual property have already been copied or uploaded into various storage locations, such as cloud storage, shared folders, documents, spreadsheets or any other business applications.

Organizations need the ability to see what sensitive data they have, how is it protected, and who can view or modify it.

If that is not possible or is difficult, then they will never really understand the risk, nor will they be able to control it, especially in a digital world where sensitive digital assets are becoming more and more common and the threat of loss or misuse has a devastating impact.

By way of illustration, if a folder is discovered that contains customer information, it will be up to the company to decide if all the employees with access to the folder need access in fact or whether access should only be permitted through special procedures.

Sometimes even in the most secure IT environments you may come across information that should not have been left there by accident or intentionally.

A Simple Cloud Access Checklist

Organizations can start with a basic review:

☐ Identify all major cloud platforms used by the business

☐ Review user accounts and access permissions

☐ Remove access that is no longer required

☐ Review former employee accounts

☐ Check permissions when employees change roles

☐ Review external users and shared links

☐ Identify folders containing sensitive information

☐ Classify important information based on sensitivity

☐ Review privileged accounts

☐ Schedule regular access reviews

☐ Monitor changes to access and sharing permissions

The Goal Is Not to Remove Access

Good cloud security does not mean making information difficult for employees to access.

People need access to information to do their jobs.

The goal is to make sure the right people have access to the right information for the right reasons.

That requires more than simply creating user accounts and assigning permissions. Organizations need ongoing visibility into their cloud environment, the information stored within it, and how access changes over time.

As businesses continue moving more information to cloud platforms, understanding who can access that information becomes increasingly important.

The question is not simply “Is our data in the cloud?”

It is:

“Do we know who can access it, what they can access, and whether they still need that access?”

That visibility can make it much easier to identify unnecessary exposure and maintain better control over sensitive information.

Unstructured Data Security: Why Visibility Matters More Than Storage

EzSecure — Wed, 09 Sep 2026 05:48:17 +0000

Most conversations about data security start from an assumption that sensitive information lives inside systems built to protect it: a database with access controls, an application with an audit log, an identity platform that records who touched what and when. Unstructured data does not follow that pattern. It accumulates in file servers, cloud drives, and collaboration platforms that were designed to make sharing easy, not to make oversight possible, and it does so gradually enough that no single event marks the point at which an organization loses track of what it holds.

This is the actual difficulty behind unstructured data security. Files are not inherently more dangerous than database records. The systems holding them were simply never built to answer the questions that security, privacy, and compliance teams eventually need answered: what sensitive information exists, where it sits, who can reach it, how old it is, and whether there is still a reason to keep it. A database schema forces a certain amount of discipline on what gets stored and where. A shared drive imposes almost none, which is exactly why it becomes useful for everyday work and difficult to govern at the same time.

Storage Is Not the Same as Visibility

Storage capacity and data visibility are often treated as the same problem, and they are not. A file server can have ample capacity, current backups, and a properly patched operating system, and still be a source of real exposure if nobody can say what is stored on it. Visibility is a separate capability. It requires knowing not just that a folder exists, but what is inside it: whether a spreadsheet contains payroll figures, whether a scanned document includes an identification number, whether an old contract lists banking details that were never redacted. Folder names and directory structures rarely answer these questions on their own. A folder labeled Finance Archive 2019 could contain routine budget summaries, or it could contain unmasked account numbers, and there is no way to know which without looking inside.

How the Gap Widens Over Time

This gap tends to widen with time rather than close on its own. A contract signed for a vendor relationship that ended years ago stays in a shared folder because nobody is responsible for removing it. An HR file for an employee who left the company sits alongside current employee records because archiving is inconvenient and deletion feels irreversible. A financial spreadsheet gets copied into several project folders during a single reporting cycle, and each copy inherits whatever permissions its folder happens to have, regardless of whether that access still makes sense. A one time data export, created for a migration or an audit, gets left in place because removing it was never assigned to anyone. A batch of identification documents collected for a compliance check years ago remains in a subfolder that predates the current team, unnoticed because nobody currently working there has a reason to open it.

None of this reflects carelessness so much as the ordinary behavior of people working inside systems that make copying, sharing, and storing effortless, while leaving review, restriction, and deletion as someone else's problem. Access control, in this context, is not the same thing as data discovery. A folder can have properly restricted permissions and still contain content nobody has reviewed in years. Permissions describe who is allowed to open a folder. They say nothing about what the folder actually contains or whether that content still belongs there.

Why File Exposure Rarely Requires an Attacker

A meaningful share of the risk connected to unstructured data does not depend on a sophisticated attack. The UK Information Commissioner's Office, drawing on Verizon's 2023 Data Breach Investigations Report, has noted that the large majority of confirmed breaches in that dataset involved some form of human element, and that misconfiguration accounted for a substantial share of breaches caused by error rather than by deliberate attack technique. A folder shared too broadly during a project and never restricted afterward, or a spreadsheet exported to a location with wider access than intended, creates exposure before any attacker is involved. Once a credential is compromised through an unrelated event such as a phishing message, the resulting damage often depends less on the sophistication of the attacker and more on how much sensitive, unreviewed content happens to sit within reach of that one account.

Where This Accumulates

In practice, this exposure concentrates in a small number of environments: a Windows File Server that has been in continuous use for years, a SharePoint deployment that expanded site by site as teams needed a place to collaborate, OneDrive folders attached to individual employees, and Google Drive for organizations built on Google Workspace. Each of these was designed around collaboration and search, helping a person find their own files quickly, rather than giving a security or compliance function visibility into what sensitive information exists across the environment as a whole. Permissions can remain unchanged long after the original business need has changed, particularly when repositories grow over time without regular access reviews.

EzSecure is designed to help organizations identify sensitive content across Windows File Server, SharePoint, OneDrive, and Google Drive, rather than relying only on folder names or assumptions about where sensitive information resides. That capability addresses a genuine gap, but it addresses only the discovery portion of the problem. What happens afterward, in terms of classification, access review, and retention decisions, remains an organizational responsibility that no scanning tool can complete on its own.

What Discovery and Classification Do, and What They Do Not

Sensitive data discovery identifies where regulated or confidential information exists within file repositories, typically by examining content rather than relying on file names or folder locations. Classification labels what is found, usually by sensitivity or data type, such as personal information, financial records, or confidential business documents, so that different content can be handled according to different rules.

The National Institute of Standards and Technology treats this kind of inventory as a foundational security activity rather than an optional one. The NIST Cybersecurity Framework's Identify function calls for organizations to maintain inventories of data and to prioritize assets according to classification, criticality, and business value. Without that inventory, security effort tends to default to whichever systems are already well understood, which is rarely where the least reviewed and most exposed content actually sits.

It is worth being precise about what discovery and classification accomplish, because the distinction matters in practice. Finding and labeling sensitive files does not make an organization compliant with any specific law or standard. Compliance depends on a wider set of obligations: a lawful basis for handling personal data, appropriate technical and organizational safeguards, breach notification procedures, documented retention schedules, and governance that assigns responsibility for decisions about the data once it has been found. What discovery and classification provide is information that can help organizations make more informed decisions about those areas. They are a prerequisite for a privacy or governance program, not a substitute for one.

Retention Turns Visibility Into a Legal Question

Once an organization knows what sensitive information it holds, retention decisions become an important part of its legal, privacy, and governance responsibilities. The GDPR's storage limitation principle, set out in Article 5 of Regulation (EU) 2016/679, requires that personal data be kept in a form permitting identification of the individual for no longer than is necessary for the purpose it was collected for, subject to narrow exceptions for archiving, research, and statistical purposes. India's Digital Personal Data Protection Act, 2023 reflects a comparable position: storage limitation is one of the Act's core obligations, and a data fiduciary is expected to retain personal data only for as long as a legitimate purpose for holding it continues to exist.

Neither law was written with file servers or cloud drives specifically in mind, and neither one requires a particular technology to achieve compliance. Meeting these obligations requires organizations to understand what personal data they hold and to apply appropriate retention practices. That becomes more difficult when files are copied, moved, and left across multiple repositories. A signed employment contract, a customer application form, or a scanned identification document does not stop being personal data simply because it now sits in a folder nobody actively manages. The legal obligation follows the content, not the tidiness of the storage location.

A related but separate principle applies in healthcare under HIPAA. The Privacy Rule's minimum necessary standard, described by the U.S. Department of Health and Human Services, requires covered entities to limit access to protected health information to what is actually needed for a given purpose. That standard can become difficult to apply when patient records or billing exports are stored in shared folders with access broader than the task requires.

Age Changes the Risk Even When the Content Does Not

Sensitivity and age are separate variables, and treating them as one leads to poor assumptions. A payroll spreadsheet from five years ago is not dangerous because it is old. It is dangerous if it still sits in a folder with broad access, has not been reviewed since it was created, and nobody remembers it exists. The passage of time does not make a file safer. It generally makes the file's original access decisions less relevant, because the project, the team, and the business justification that shaped those decisions have usually moved on, while the file itself has not.

You can read the complete detailed version of this article on the official EzSecure blog here:👉Unstructured Data Security: Why Visibility Matters More Than Storage

A Workable Starting Point

None of this requires cataloging every file an organization has ever created. It requires a defined sequence: identify sensitive information across file repositories rather than assuming it is confined to well known systems, classify what is found so that different types of content can be handled according to their actual sensitivity, review access against current need rather than historical convenience, and connect the results to an actual retention decision instead of a general intention to address it eventually. Each step depends on the one before it. Classification without discovery is guesswork. Access review without classification treats a folder of routine documents the same as one containing unredacted identification records. Retention decisions made without either amount to deleting or keeping data on instinct rather than evidence.

Unstructured data security is often framed as a problem of scale, as if the difficulty were simply that there is a great deal of file based information to manage. The more accurate framing is that these files sit in environments built for convenience rather than oversight, and the distance between what an organization stores and what it actually knows about storage widens every year nobody closes it. Storage will keep expanding regardless of what any organization decides to do about it. Visibility does not expand on its own. It has to be built, deliberately, into how file repositories are reviewed, classified, and eventually cleared of information that no longer has a reason to be there.

AI Agents Are Becoming Employees. But Who Manages Them?

EzSecure — Sat, 05 Sep 2026 07:43:36 +0000

For years, companies have treated software as something employees use to get work done. That assumption is starting to change.

AI agents can now do more than generate an answer or summarize a document. They can interact with applications, retrieve information, execute tasks, make decisions within defined workflows, and hand work from one system to another. As enterprises move from experimenting with AI to putting agents into everyday operations, a new question is becoming difficult to ignore: who manages an AI agent when it starts behaving more like a worker than a tool?

The answer is not as simple as assigning ownership to the IT team.

AI Agents Are Moving Into the Workforce

The shift from AI assistants to AI agents is important because the level of independence changes.

A chatbot generally waits for a person to ask something. An agent can be given a goal and carry out multiple steps to achieve it.

For example, an AI agent could:

Review incoming customer requests
Retrieve information from internal systems
Create or update records
Route tasks to another department
Generate reports
Trigger business workflows
Work across several applications

This makes an agent part of an operational process rather than simply another productivity tool.

Recent enterprise research describes this transition as AI moving from assistance to execution, with agents spreading across knowledge work.

And once an AI system can act, questions about ownership become much more important.

An AI Agent Doesn’t Have a Job Description

An employee has a defined role, manager, access level, responsibilities, and escalation path.

An AI agent usually doesn’t.

A company may deploy an agent for customer support, another for finance, another for software development, and another for internal research. Different teams may configure them independently and give them access to different systems.

Without clear governance, nobody may have a complete picture of:

Which agents exist
Who created them
What each agent is allowed to access
What systems they can interact with
What decisions they can make
Who reviews their actions
What happens when they make a mistake

That creates a management problem before it becomes a technical one.

Who Should Manage AI Agents?

There probably won’t be one universal answer.

IT may manage the infrastructure. Security teams may manage access and technical risk. Legal and compliance teams may define requirements. Business teams may own the workflows where agents operate.

Leadership, however, still needs to establish who is ultimately accountable.

This is why AI governance cannot sit entirely inside one department. Gartner has warned that applying the same governance approach to every AI agent can itself create problems because agents operate with different levels of autonomy and access.

A simple document saying “AI is approved” isn’t enough.

Companies need to understand what each agent is actually doing.

The Real Issue Is Access

The biggest management question may not be what can the agent do?

It may be:

What can the agent access while doing it?

An agent connected to a public knowledge base presents a very different risk from one connected to customer records, financial systems, employee information, source code, or confidential business documents.

The more systems and information an agent can reach, the more important it becomes to understand its boundaries.

This is where traditional approaches can become difficult. Companies have spent years managing access for employees and applications. AI agents introduce another type of actor that can operate quickly, continuously, and sometimes across multiple systems.

PwC’s 2026 research similarly highlights the need for AI agents to have verified identities, defined roles, task-specific permissions, and auditable records.

What Happens When an Agent Makes a Mistake?

Imagine an AI agent sends the wrong information to a customer.

Or updates the wrong record.

Or accesses information it wasn’t supposed to use.

Or makes a decision based on outdated information.

Who is responsible?

The agent cannot take responsibility. The organization deploying it ultimately has to answer for the outcome.

That means businesses need clear escalation and review processes before agents are given significant autonomy.

Human oversight also shouldn’t necessarily mean someone watches every action. The level of human involvement should depend on the potential impact of the task.

A low-risk administrative task may need little intervention. A decision involving financial, legal, employment, or personal information may require a human checkpoint.

Companies Also Need to Know What Their Agents Can See

There is another issue that can easily be overlooked: data visibility.

Before an organization can decide whether an AI agent should have access to information, it needs to understand what information exists in the first place.

A company may have sensitive information spread across databases, cloud storage, business applications, documents, and other environments. If that information hasn’t been properly discovered and classified, creating sensible access boundaries becomes much harder.

This makes data discovery and classification relevant to AI governance as well.

Platforms such as EzSecure can help organizations discover and classify sensitive information across their environments, giving teams a clearer understanding of what information exists before deciding how it should be accessed or used.

AI Governance Is Becoming an Operating Model

The bigger change is that AI governance can no longer be treated as a policy document created once and forgotten.

As agents become part of everyday workflows, organizations need an operating model around them.

That means answering practical questions:

Who approves an agent?

Who owns it?

What data can it access?

What actions can it take?

When does a human need to intervene?

How are its actions recorded?

What happens when its role changes?

These questions become increasingly important as organizations deploy agents across departments and platforms. BCG’s recent enterprise guidance points to the need for centralized visibility, identity, policy enforcement, and governance as AI agents scale.

The Future May Have Humans Managing a Digital Workforce

The idea of AI agents as part of the workforce may sound futuristic, but organizations are already discussing where these systems belong within operating structures. KPMG recently framed the issue directly: as agents enter the workforce, companies need to reconsider who manages them and how responsibility should be structured.

The important point isn’t whether we should literally call AI agents “employees.”

It is whether companies are prepared to manage them with the same seriousness they apply to other actors that can access information, perform work, and affect business outcomes.

AI agents may not need an employee ID card.

But they increasingly need an owner, defined permissions, clear boundaries, oversight, and an audit trail.

And the companies that establish those foundations early will be in a much stronger position to scale AI without losing control of what their systems can access or do.

What Information Is Most Dangerous If Exposed?

EzSecure — Tue, 01 Sep 2026 08:16:09 +0000

When people hear about a data leak, their first thought is passwords or credit card numbers. But the danger of exposure depends on what information is about, what it can be combined with, and what someone can do with it.

One piece of information might not seem particularly dangerous on its own. But when several details are revealed in combination, they can paint a much clearer picture of an individual, provide access to an account or expose sensitive business information.

Understanding these differences will help companies know where to direct their efforts.

Why Some Information Is More Dangerous Than Other Information

The sensitivity of information is not only determined by the format. Potential impact is also important.

For example, an employee’s work email address is typically less sensitive than their password. An email address does not usually get you right into an account, but a password does.

Information is especially dangerous when it can be used to:

Sign in to an account or system
Steal someone’s identity
Engage in financial crimes
Impersonate a person
Give away private personal information
Reveal sensitive business information
Combine with other information to create increased security or privacy risk

That’s why businesses need to look beyond individual files and consider the context and sensitivity of the information they hold.

Passwords and Credentials Can Give Direct Access

Passwords, authentication tokens, API keys and other credentials can be some of the most immediately dangerous information to leak.

The simple reason is that these details can be a key to an existing account or system .

For example, an employee password exposed in a data breach could give access to email, cloud applications, internal documents, or other services. The risk is even higher if the same password is used in several accounts.

And that’s why credentials have to be treated differently than regular contact information.

Financial Information Can Enable Fraud

Disclosing bank account numbers, payment card details, financial statements, transaction histories and other financial information can be very risky.

Depending on what is revealed, criminals could use financial information for fraud, unauthorised transactions, targeted scams or social engineering.

Financial information may also be made more valuable when combined with personal details such as a person’s name, address or identification information.

Identity Information Can Enable Impersonation

Apart from names and phone numbers, more extensive identity records are much more sensitive.

In addition, government ID numbers, passport details, driver’s license details, date of birth, or copies of identity documents can give a person enough information to engage in impersonation or identity theft activities.

One main thing is that personal information does not have the same degree of privacy. A company needs to identify and separate ordinary contact details from the sensitive details that can be used in a reliable way to confirm a person’s identity.

Health Information Can Reveal Private Details

In medical records, diagnostics, medication, therapy records, medical insurance data, and other health-related information, highly private information about a person can be revealed.

It won’t only affect a person economically. Information may also raise privacy issues, lead to discrimination, emotional upset, or regulatory actions against the organization in control of the information.

Healthcare facilities are not the only places where such data is handled. Employers, insurance companies, benefits providers, and other businesses may also handle medical records.

Confidential Business Information Can Be Just as Valuable

Sensitive Information isn’t just restricted to that of individuals.

In addition to personal information, businesses may possess other sensitive information like confidential business contracts, pricing strategies, sales figures, financial forecasting, intellectual property rights, product roadmaps, development or source code documents, customers’ lists, and strategic internal documents.

If such secrets are divulged, they may lead to competitive advantage gains by rivals, be used against negotiation parties, harm the relationships with customer base, or cause financial and public-image loss.

For some organizations, the loss of a single confidential business document would be much more harmful than the loss of thousands of regular documents.

Why Combining Information Makes Exposure More Dangerous

One of the major concepts about data exposure is that certain sets of information can have different privacy implications.

Lets illustrate the concept with a simple and easily understood example:

On its own, an email address may only be mildly harmful. If, however, the attacker has also obtained the person’s full name, home phone number, day of birth, and login info, those items can be combined for a much more plausible impersonation or targeted email attack.

Exactly for that reason, companies should not just analyze data on a document or document set level. They should also be aware of what various sets of data might disclose if presented as such to an adversary.

How Data Classification Helps Identify Sensitive Information

Mixing the data sources, as the business records spread to various places like databases, cloud storage, spreadsheets, applications, and shared folders, the companies can’t be sure of which data should be considered as priority.

Using a data classification system is a method to group data by the type and the level of sensitivity of the data. In the example, a classification could be that customer contact data is low sensitivity, and financial records and identity documents are at medium sensitivity, with confidential business documents at high or very high sensitivity.

This will help the team to know better what kind of information they have and which ones should be treated more strictly.

Why Sensitive Data Discovery Matters

Classification can really bring value when an organization clearly knows its sensitive information locations.

Sensitive data discovery assists companies with finding where the private and confidential information is spread across different systems. The process can unearth information, which might have been missed, such as sensitive data stored in aging databases, shared drives, cloud platforms, or business apps.

Being aware of such risks enables businesses to channel a lot of their energy on the information that poses a greater risk rather than assuming that each piece has the same level of exposure.

What Should Businesses Do With This Information?

There is no common list of details that can bring equal dangers to different organizations. The extent of risk is determined by the nature of data, whose data it is, where it is located, and if it leaks, what damage could result.

The first, and probably the most effective, thing you can do is:

Learn and recognize what is considered confidential information by your business.
Detects where that data is in different systems.
Group data based on its level of risk.
Check out who is authorized to access the database and whether that access is justified.
Keep those pieces of information which are still relevant and discard or destroy those ones which are not needed anymore.
Keep up-to-date and review sensitive data at regular intervals as the enterprise progresses.

Having said all that, minimizing data collection is just one part. The whole point is to figure out how much damage any disclosure of a given dataset could potentially do and then, be able to, if it so happens that the dataset gets out, at the very beginning of any breach, minimize the impact of such disclosure.

By locating the spots where confidential information is kept and knowing its degree of privacy at those spots, companies can enhance their visibility levels, privacy and compliance management functions significantly.

How Businesses Can Reduce the Risk

Guarding your confidential data starts with realizing what you own.

Companies need to keep track of how and where sensitive data is identified, who can get to it, and whether it is still necessary for retaining such data as time goes on.

By means of finding and defining the kind of sensitive data that the company is handling, they shall get a more explicit picture of their information landscape. When businesses know what sensitive information is available they are better placed to set up security access and retention policies, monitoring, and compliance.

This is not the matter of collecting only less data. Rather it’s a process which leads to knowing the data that the company already has and providing sensitive information with the most appropriate level of care and attention.

What Customer Information Do Online Stores Collect?

EzSecure — Wed, 26 Aug 2026 08:54:32 +0000

When you shop online you probably think mainly about what you are buying, how much it costs and when it will arrive. But behind every online purchase is a lot of customer information that helps the store process your order and provide its services.

It’s all about the data. Different types of online store data can be collected at different points in the customer journey, from your name and delivery address to your payment details and shopping preferences.

It is important for both businesses and customers to know what customer information online stores collect, why they collect it, and how it is managed.

What Customer Information Do Online Stores Collect?

An online store can gather a lot of data about its customers. The specific information depends on the website, products, services and features being used.

Some of the common examples are:

Name and Contact Information
E-mail address and phone number
Billing & Delivery Addresses
Login information for an account
Purchase & order history
Payment information
Customer support chat
Product Reviews & Comments
Shopping preferences information
Device and website use information

Not all stores collect all of this information and some information may only be collected when it is necessary for a particular service.

Why Do Online Stores Collect Customer Information?

We don’t pull customer data for data’s sake.

Stores require some information to perform routine activities such as processing orders, delivering products, processing returns, responding to customers and managing customer accounts.

One example is a delivery address which is required to send an order whereas an email address can be used to provide an order confirmation or shipping update.

Businesses can also use information to learn about consumer tastes, improve their website, personalise experiences, or communicate with customers about products and services.

The main thing is to know what is being collected, and why it is needed.

What Payment Information Do Online Stores Handle?

Online shopping itself is financial information.

Depending on the payment processing by a store, it may process information like transaction details, billing information, payment status or other payment related records.

Many online stores rely on third party payment providers, rather than holding full payment card details themselves.

But payment information can be sensitive, too. Businesses need to know where this information is, what systems are processing it, and what third parties may have access to it.

This is one reason why understanding the data environment of the organization is critical.

How Do Online Stores Collect Customer Information?

I think there are many different sources for customer info.

A customer can provide information directly when:

Sign up for an account
Command
Subscribe to the newsletter
Contact Support
Write a review
Ask for a Return or Refund
Complete a survey

Information can be automatically generated through website activity, applications, cookies, devices and other technologies.

This means that a customer’s information for an online store could reside in multiple databases or applications. It is scalable across multiple systems.

Where Is Customer Information Stored?

And one of the hardest things for growing online businesses is that customer data can end up in all sorts of places.

For example, information can be found in:

E-commerce sites
Customer Relationship Management (CRM) System
Payment gateways
Cloud storage…
E-mail systems
Software for customer support
Marketing channels
Databases
Excel sheets.
Back-up

With growth comes more tools, employees, vendors and customers, which can make tracking this information increasingly difficult.

This is where data visibility comes into play. Companies need to know exactly what data they have and where it is.

Why Is Customer Data Privacy Important for Online Stores?

A company may possess individuals’ personal data and financial records, therefore putting privacy as a major concern for companies engaged in doing business on the Internet.

Customers anticipate businesses to follow strict procedures regarding the use of their information. Different privacy considerations should be taken into account, depending on the location of the company and information it collects.

The consequences of limited visibility are very serious.

For example, if a company cannot locate its own customers’ data, stored in a legacy database or shared file of employees, it can make it virtually impossible to study and classify the information and to make decisions, whether to keep or destroy it.

How Can Hackers Misuse Customer Information?

Exposed or wrongly accessed customer information can be critical for hackers and scammers, as even simple information can serve as the basis for an online scam.

The information gained from customers can be misused in several ways, as follows:

Identity fraud – Customer information is used to impersonate individuals.
Phishing scams – Scammers can create messages of the customers by utilizing their information.
Financial fraud – Payment or transaction-related customer information can be misused to perform fraud.
Account takeover – Gaining access to customer accounts by utilizing stolen user ID and/ or password.
Targeted scam – Implementing fake scams using the client’s shopping
information.

When customer information is kept in different databases, spreadsheets, applications, backups, and third-party systems, it is impossible for companies to know exactly what data is in their possession.

Why Data Classification Matters for Customer Information

There are varying degrees of sensitivity in customer information.

While a customer’s name may be treated differently than credit card numbers, identification information, or any other sensitive information, data classification helps organizations categorize information based on its nature and sensitivity.

In the case of an internet retailer, classification can help firms know what the sensitive data is and where it is stored.

The combination of classification and sensitive data discovery can provide firms with a better understanding of what sensitive information it has instead of regarding all data the same way.

What Happens to Customer Information After a Purchase?

The journey of the customers may never come to a complete end when the order is delivered.

The information concerning a particular buy may be stored within a company’s working systems.

Some information might be kept because of some company’s operative or legal matters and the other information must no longer be preserved after a certain period of time.

This is why the problem of data keeping is very important for online shops.

It is important for the businesses to know what information they keep, why they keep it, and for how long they should store it.

How Can Online Stores Better Manage Customer Information?

In managing customer data, it is vital to achieve visibility.

An online shop must know:

Which personal data is collected
What purposes drive the collection of this data
Where the data is stored
Which systems and vendors deal with this data
What kind of data needs to be classified as sensitive and secure
How long every type of data will be stored

Ongoing data discovery allows organizations to determine what data they have and where it resides. The classification that follows lets companies define the type of information and its vulnerability degree afterward.

EzSecure and similar solutions enable organizations to classify the sensitive data they discover through ongoing discovery.

Final Thoughts

The collection of customer information is an essential requirement for online stores as it is crucial for their smooth functioning. It’s important for orders to be processed, products to be sent to different destinations, customers to receive adequate assistance, and businesses to keep up with their functioning.

The challenge starts when customer information is scattered among various systems and becomes complicated to manage.

For online businesses, managing customer information is more than just collecting information. What is necessary, however, is to have the insight of what information is available, how sensitive it is, where it is stored, and how to handle it wisely.

Better visibility gives businesses a solid basis for data protection, classification, retention, and compliance, which, in its turn, helps them win trust among their clients.

Why Is Customer Information Important in Banking?

EzSecure — Tue, 25 Aug 2026 07:01:02 +0000

A woman walks into a branch to open a savings account. Before she leaves with a debit card, the bank has already collected her name, date of birth, address, government-issued ID number, phone number, email address, occupation, and a signature. A few weeks later she applies for a personal loan, and the file grows: income statements, bank statements from her previous account, a credit report, and a form describing what she plans to do with the money. When she later calls the bank because a transaction on her statement looks unfamiliar, the file grows again this time with notes from a customer service representative and a record of the investigation that follows.

None of this is unusual. It's simply what it takes to open an account, extend credit, process a transaction, or resolve a problem. A bank cannot identify who it's dealing with, decide whether to lend money, move funds between accounts, or investigate suspicious activity without customer information. This is true for a small community bank and a multinational one alike.

The more interesting question and the one this article is really about is what happens after that information is collected. How much customer information does a typical bank actually hold, in how many different forms, and where does all of it end up? The answer turns out to matter almost as much as the information itself.

What Is Customer Information in Banking?

Customer information in banking is any data a bank collects, creates, or stores about a person or business it serves, in the course of opening accounts, providing financial products, processing transactions, or meeting regulatory obligations. It is a broader concept than "personal information" alone, because it also includes information created by the bank itself, such as risk ratings, transaction histories, and internal case notes.

It helps to separate customer information into a few distinct layers, since each one is collected for a different reason and often lives in a different system:

Personal / identity information — who the person is (name, date of birth, government ID number, nationality)

Financial information — the person's financial position (income, assets, liabilities, employment)

Account information — the relationship itself (account numbers, product types, balances, opening dates)

Transaction information — the activity in that relationship (deposits, transfers, payments, card usage)

Credit information — creditworthiness and repayment history (credit scores, past defaults, loan performance)

Customer interaction information — records of contact (calls, emails, chat logs, complaint notes)

Supporting documents — the evidence behind all of the above (ID scans, proof-of-address, signed applications, tax documents)

Regulators tend to describe parts of this differently depending on the framework. Under the U.S. Gramm-Leach-Bliley Act (GLBA) and its implementing rule, Regulation P, banks handle what is defined as "nonpublic personal information" (NPI) information a consumer provides to get a financial product, information resulting from a transaction, or information a bank otherwise obtains in connection with providing a financial service.

What Types of Customer Information Do Banks Collect?

The list below is illustrative rather than exhaustive, the exact fields vary by institution, product, and jurisdiction but it reflects the categories that show up across most retail and commercial banking relationships.

A single customer relationship typically touches most of these categories at once. A mortgage application, for example, combines identity information, financial information, credit information, and supporting documents into one file which is part of why banking files tend to be information-dense in a way that a simple retail purchase record is not.

Why Is Customer Information Important to Banks?

Customer information is important in banking because it is the raw material banks need to identify customers, deliver financial products, process transactions, assess risk, detect fraud, and meet regulatory obligations. Without reliable customer information, a bank cannot confidently tell one person's money from another's, decide who qualifies for credit, or demonstrate to a regulator that it knows who it is doing business with.

Customer Identification and KYC
Know Your Customer (KYC) is the process by which a bank identifies and verifies who a customer is before establishing a relationship, and continues checking periodically afterward. In the United States, the Financial Crimes Enforcement Network's (FinCEN) Customer Due Diligence (CDD) Rule sets out four core obligations for covered financial institutions: identifying and verifying customer identity, identifying and verifying beneficial owners of legal entity customers, understanding the nature and purpose of the relationship, and conducting ongoing monitoring.

In India, the Reserve Bank of India's Master Direction on KYC requires regulated entities to maintain a documented Customer Acceptance Policy and Customer Identification Procedure, and to keep KYC records including through the Central KYC Records Registry for a minimum retention period following account closure.

Internationally, the Financial Action Task Force (FATF) the global standard-setting body for anti-money laundering sets out customer due diligence as Recommendation 10 of its 40 Recommendations, requiring financial institutions to identify customers and beneficial owners, understand the purpose of the relationship, and monitor transactions on an ongoing basis. FATF standards are implemented into national law rather than applying directly, so the specific rules a bank follows depend on its jurisdiction.

Anti-Money Laundering (AML)
It's worth being precise here: KYC and AML are banking processes and compliance obligations, not data privacy laws. They exist to prevent banks from being used to move the proceeds of crime or finance terrorism, and they depend entirely on the bank holding accurate, current customer and transaction information. A bank that cannot connect a transaction to a verified customer profile has a much harder time spotting the kind of pattern that a suspicious activity report is designed to flag.

Account Management and Payments
Every deposit, withdrawal, transfer, and card payment depends on the bank correctly matching an instruction to an account and a customer. Contact information keeps statements and time-sensitive alerts reaching the right person, and account information keeps balances accurate across every channel a customer uses.

Lending and Credit Decisions
Income, employment history, existing debt, and credit history let a bank estimate whether a borrower can realistically repay a loan, and at what price. Underwriting decisions to approve, decline, or price at a certain interest rate are only as good as the customer information behind them.

Fraud Detection and Investigation
Fraud detection relies on being able to compare a new transaction against a customer's established pattern: typical transaction sizes, usual locations, familiar payees. When that pattern is missing, incomplete, or scattered across systems that don't talk to each other, unusual activity is harder to catch quickly and harder to investigate once it's flagged.

Customer Service
When a customer calls with a dispute, a service representative needs quick, accurate access to the account history, prior communications, and relevant documents to resolve the issue without asking the customer to repeat information already on file.

Regulatory Reporting
Banks are required to report certain activity to regulators and financial intelligence units for example, suspicious activity reports under AML frameworks. Producing accurate reports depends on the underlying customer and transaction records being complete and traceable.

Financial Products and Services
Beyond compliance, customer information also lets a bank match products to genuine need offering a mortgage refinance to a customer whose rate is above market, for instance, rather than pushing products that don't fit the relationship.

How Banks Use Customer Information

Customer information isn't collected once and filed away. It moves through a bank's processes continuously, picking up new detail at each stage. A few common examples show how this plays out in practice.

Scenario 1: Opening a Bank Account
A new customer provides identity documents, proof of address, and a signature. The bank verifies the identity documents, screens the applicant against sanctions and watch lists as part of its KYC process, and opens an account once due diligence is complete. This is illustrative of a typical account-opening flow exact steps vary by institution and jurisdiction.

Scenario 2: Applying for a Mortgage
A borrower submits income statements, tax records, a property valuation, and details of the property being purchased. The bank cross-references this against credit history and existing liabilities to decide on approval, loan amount, and interest rate. The resulting file, a mix of financial, credit, and supporting-document information, is often one of the largest and longest-retained records a bank holds on a customer.

Scenario 3: Investigating a Suspicious Transaction
A transaction pattern trips an internal alert, an unusually large transfer, or activity inconsistent with the customer's normal behavior. An investigator pulls together the account's transaction history, the original KYC file, and any prior customer service notes to assess whether the activity warrants a suspicious activity report. This is a fictional, illustrative example rather than an account of any real case.

Why Is Customer Information Sensitive?

Banking customer information is sensitive because it typically combines several categories of personal and financial data in one place, and that combination reveals far more about a person than any single data point on its own.

A name alone is not very revealing. A name next to a government ID number, a home address, an account number, and a record of recent transactions is a much more complete and more exploitable picture of someone's financial life. This is sometimes called aggregation risk: individually unremarkable pieces of information become more sensitive when they are combined, because the combination can be used to impersonate someone, access their accounts, or infer things about them they haven't chosen to share.

It's also worth noting that not every financial detail is treated the same way under every privacy law. Under the EU General Data Protection Regulation (GDPR), for instance, ordinary financial data such as an account number or salary is personal data and must be handled lawfully and securely but it does not automatically fall into GDPR's narrower "special category" of data (which covers things like health, biometric, or religious data and carries extra restrictions under Article 9). That distinction matters for compliance teams, even though from a practical security standpoint, financial and identity information combined still deserves careful handling.

What Happens When Customer Information Is Inaccurate?

Inaccurate or outdated customer information creates friction rather than catastrophe in most cases, but the friction is real and compounds over time.

Customer service — representatives waste time verifying identity or resolving confusion caused by mismatched records

Account management — statements, alerts, or cards may be sent to an old address

Lending decisions — outdated income or debt figures can lead to an inaccurate affordability assessment

Compliance processes — periodic KYC updates depend on current information; stale records can delay re-verification

Communication — fraud alerts or urgent notices may not reach the customer in time

Fraud investigations — investigators may need to reconcile conflicting versions of the same customer's details across systems

None of this means a bank with imperfect data is failing at its job, customer information changes constantly, and keeping every field current across millions of relationships is a genuine operational challenge, not a sign of neglect.

Where Can Customer Information Exist Inside a Bank?

This is where the picture gets more complicated than most people expect. Customer information rarely lives in just one place. Depending on how a particular institution is organized, it can exist across a wide range of systems and file types, including as general, illustrative examples

rather than a description of any specific bank's architecture:

Core banking systems and account databases
Loan origination and servicing records
Account opening documents and signed applications
KYC and identity verification documents
Internal reports and audit files
Spreadsheets used for tracking, reconciliation, or analysis
Scanned or photographed documents
Shared team folders
Cloud-based file storage
Documents created by individual employees drafts, working files, exports
Archived files retained for record-keeping or legal requirements

Every bank's technology environment is different, and the exact mix of systems above varies widely by institution size, age, and history of mergers or system migrations. What tends to be consistent is that customer information doesn't stay confined to the system it was originally entered into, it gets exported, copied, attached to emails, and saved into working files as part of everyday business processes.

Why Knowing Where Customer Information Exists Matters

There is a real difference between a bank knowing that it holds customer information, and a bank knowing exactly where every copy of that information currently sits. The first is almost always true. The second is much harder to be confident about, even at institutions with mature security programs.

This gap tends to open up gradually rather than all at once. A KYC document gets attached to an email during a compliance review. A loan officer exports a spreadsheet of applicant details to build a report. A customer service team keeps a shared folder of case files for reference. None of this is malicious or even unusual, it's simply how work gets done. But over years, it produces data sprawl: duplicate copies of the same customer record, older versions that were never deleted, unstructured files sitting in shared drives, and documents created by individual employees that were never centrally tracked.

It's important to be clear about what this challenge is, and isn't. A bank can have strong access controls, encryption, and security policies in place and still not have a complete, current inventory of exactly which files across every shared folder and cloud drive contain sensitive customer information. That's not a security failure in the traditional sense; it's a visibility and data management challenge, and it's common across large, long-running organizations of many kinds, not just banks.

Customer Information and Unstructured Data

A useful distinction here is between structured and unstructured data. Structured data lives in defined fields inside a database, an account number in an "account_number" column, a balance in a "balance" column which makes it straightforward to search, filter, and monitor. Unstructured data is everything else: PDFs, Word documents, Excel spreadsheets, scanned images, exported reports, and email attachments, where sensitive information is embedded somewhere in the body of a file rather than in a labeled field.

A large share of the customer information a bank creates day to day KYC documents, loan applications, correspondence, internal reports falls into the unstructured category. That makes it harder to know, at a glance, which files actually contain sensitive customer information and which don't, since the answer depends on the content of each file rather than a predictable field in a database.

What Is Sensitive Data Discovery?

Sensitive Data Discovery is the process of locating and identifying sensitive information across an organization's data repositories determining which files exist, and which of them contain sensitive categories of information such as identity details, financial data, or account numbers. It answers the question "where is this information, and what does it contain?" rather than the question "how do we secure it?"

That distinction is worth making explicit, because Sensitive Data Discovery is often confused with or assumed to include the technologies that come after it. It does not replace them.

Discovery and protection are complementary, not interchangeable. Knowing where sensitive information is located is a precondition for making good decisions about how to protect it but discovery itself does not encrypt a file, restrict who can open it, or stop a threat in progress.

Why Sensitive Data Discovery Matters in Banking

Given how much customer information banks hold, how sensitive it is in combination, and how much of it lives in unstructured files, discovery becomes a practical starting point rather than an abstract exercise. It can help a team understand:

Where sensitive customer information exists across supported file-based sources
What types of sensitive information are present in a given file or folder
Which files may contain identity, financial, or account information that needs closer attention
How widely a particular type of information is distributed across an organization's repositories

This is deliberately modest framing. Discovery can provide visibility and support better data governance and security decisions, it does not, by itself, make an organization compliant with any regulation, and it isn't a substitute for the security controls that come after visibility is established.

A Practical Banking Example

The following is an illustrative scenario, not an account of a real institution or event.

Picture a mid-sized financial institution where customer information has accumulated across internal reports, loan officer spreadsheets, scanned KYC documents, and shared team folders over several years, following a system migration and a couple of acquisitions. Compliance and IT teams know, in general terms, that sensitive customer data exists in these locations. What they don't have is a clear, current answer to which specific files contain identity numbers, account details, or financial information, or how many duplicate copies of a given customer's file exist across the environment.

A sensitive data discovery process, applied to the file-based sources in scope, can scan these repositories, flag files that likely contain sensitive customer information, and give the compliance and IT teams a working inventory to act on informing decisions about access, retention, and cleanup that were previously based on guesswork.

Illustrative Example: What One Document Can Contain

A single customer document can carry several categories of sensitive information at once. The example below is entirely fictional.

One PDF like this combines identity information, account information, financial information, and a government identifier which is exactly why a single misplaced or over-shared file can carry more risk than its file size would suggest.

A Simple Data Visibility Framework

It can help to think about sensitive customer data in five stages. The first four describe what discovery is concerned with; the fifth is the decision-making step that follows it.

Find — Where does the data exist across the organization's file-based sources?
1. Identify — What type of information is present in a given file?
3. Understand — What specific sensitive information does that file contain?
5. Review — Where are copies or related versions of that file located?
7. Act — Use the findings to inform appropriate privacy, data governance, and security decisions.

Privacy and Compliance Considerations

Several regulatory frameworks shape how banks are expected to handle customer information, though which ones apply depends entirely on where a bank operates and who its customers are. None of the frameworks below applies universally to every bank everywhere.

GLBA and Regulation P (United States)
The Gramm-Leach-Bliley Act requires U.S. financial institutions to explain their information-sharing practices to customers and to give customers the right to opt out of having their nonpublic personal information shared with certain nonaffiliated third parties. The Consumer Financial Protection Bureau administers this requirement for most institutions through Regulation P (12 CFR Part 1016), while other federal regulators including the FTC, SEC, OCC, and FDIC enforce equivalent requirements for the institutions under their respective jurisdiction.

GDPR (European Union)
Where GDPR applies, the General Data Protection Regulation governs the processing of personal data, including customer financial and identity information, and requires processing to have an appropriate lawful basis. Its territorial scope depends on the circumstances described in Article 3. Most ordinary financial information is not automatically considered a "special category" of personal data under GDPR, but it remains personal data subject to the regulation's general requirements.

DPDP Act 2023 (India)
India's Digital Personal Data Protection Act, 2023 establishes a framework for processing digital personal data. The DPDP Rules, 2025 provide the implementation framework, with different provisions taking effect according to the notified enforcement timeline. Financial institutions should assess their obligations based on the Act, the Rules, and the provisions currently in force.

KYC and AML
As covered earlier, it's worth restating: KYC and AML frameworks including FinCEN's CDD Rule in the U.S., the RBI's KYC Master Direction in India, and FATF's global recommendations are financial crime prevention and banking due-diligence obligations, not data privacy laws. They sit alongside privacy regulation rather than inside it, and they are a major reason banks collect as much customer information as they do.

How EzSecure Helps Businesses Discover Sensitive Data

Everything covered so far points to the same practical problem: banks and other financial institutions can have solid policies, security controls, and compliance programs in place, and still lack a clear, current picture of exactly where sensitive customer information sits across their file-based systems.

EzSecure focuses specifically on Sensitive Data Discovery. It helps businesses discover and identify sensitive data across supported file-based sources, including:

Google Drive
Microsoft SharePoint
OneDrive
Windows File Server

For a bank or financial institution working through the kind of scenario described earlier customer information scattered across reports, spreadsheets, KYC documents, loan files, and shared folders EzSecure is built to help teams find where sensitive information exists within those supported sources and identify what it contains, giving compliance, IT, and data governance teams a clearer starting point for their own decisions.

You can read the complete detailed version of this article on the official EzSecure blog here:👉Why Is Customer Information Important in Banking?

Conclusion

Customer information is central to banking because nearly everything a bank does opening an account, approving a loan, processing a payment, investigating fraud, filing a regulatory report depends on knowing who a customer is and having reliable data to act on. That much is well understood across the industry.

What's less often discussed is that understanding customer information isn't only about knowing it exists. It's also about knowing where every sensitive copy of it currently lives across core systems, shared folders, cloud storage, and the countless working files that accumulate over years of normal business activity. A bank can be diligent, well-controlled, and still not have complete visibility into that second question.

That's the gap Sensitive Data Discovery is built to address: not replacing security or compliance work, but giving teams a clearer, evidence-based starting point for it beginning with a straightforward question. Where does our sensitive customer information actually exist, and what does it contain?

A Simple Guide to Cybersecurity Compliance Requirements

EzSecure — Thu, 20 Aug 2026 08:36:32 +0000

Cybersecurity compliance can be confusing, especially when organisations are faced with a glut of regulations, standards, audits and technical requirements. But the heart of the concept is fairly straightforward: businesses need to understand what information they have, how they handle it, and whether their processes satisfy the requirements that apply to them.

For many organisations, the most difficult part is not knowing the regulations. It’s knowing their own environment first of all. With data spread across databases, cloud platforms, apps, shared folders and employee systems, it’s hard to know exactly what needs your attention.

That is why a good compliance process begins with knowing what you own.

What Is Cybersecurity Compliance?

Cybersecurity compliance means adhering to the rules, regulations, and industry norms that govern how an organization manages information and technology.

The requirements may vary based on the industry, location and type of information dealt with by the business.

For instance, a healthcare organization might have requirements for patient information, and a financial organization might have requirements for financial and customer records.

Most compliance efforts have something in common, though the specific requirements vary: Businesses need to understand their information and demonstrate they are managing it correctly

Why Cybersecurity Compliance Matters for Businesses

Compliance is more than just passing an audit.

Good compliance practices help businesses to understand their responsibilities, improve internal processes and build trust with customers and partners.

It can also help organisations spot gaps before they turn into bigger problems.

For growing businesses, this becomes especially important. The amount of information that an organization manages can grow quickly as new employees, applications, cloud services, customers and business processes are added.

Without regular reviews companies can lose sight of what information they have and where it is.

What Are the Main Cybersecurity Compliance Requirements?

There’s no universal checklist that applies to all businesses.

But many compliance frameworks focus on areas such as:

Detection and handling of sensitive data
Regulating access to sensitive systems and records
Maintaining proper policies and procedures
Business process monitoring and analysis
Maintaining proper records
Data retention handling
Periodic assessments
Reporting and resolving compliance issues

The specific requirements depend on the regulation or standard in question.

That’s why businesses should first understand which requirements apply to them, rather than trying to follow every available framework.

Which Regulations and Standards Should Businesses Know?

Compliance requirements vary by industry and geography.

Depending on the organization, you may encounter some of the following regulations and standards: GDPR, HIPAA, PCI DSS, SOC 2, ISO 27001, and India’s DPDP Act.

These frameworks don’t all cover the same things. Some are around privacy, some are around information security controls, some are around showing that an organization has the right processes in place.

The point is to determine what requirements are applicable to your business, and not to impose compliance in a one size fits all manner.

What Information Should Businesses Identify First?
To handle compliance requirements, companies must first know what information they actually hold.

This may include:

Customer details
Records of employees
Financial data
Health data
Identification information
Business and contracts documents
Payment Information
Company confidential information

The problem is that this information may not be kept in one place.

It might be in databases, cloud storage, SaaS applications, email, shared folders, spreadsheets, and other business systems.

Data discovery helps organisations find where this information lives and data classification helps them understand what type of information they are working with.

EzSecure, for example, can assist organisations in identifying and classifying sensitive information across multiple data environments, providing teams with improved visibility into where sensitive data is located.

Why Data Visibility Matters for Compliance

You can’t effectively manage information you are unaware of.

Now imagine a company that is trying to meet regulation standards but customer data is spread out in different cloud platforms, old spreadsheets, shared directories, and databases.

Although the company may have sound policies, it is likely still having a hard time verifying that these policies are implemented uniformly throughout the rest of the company’s data.

Data visibility is exactly the kind of tool you’d need here.

Being able to see clearly exactly where confidential data resides can help organizations pinpoint areas that need attention, grasp the overall data situation, and finally, make informed choices as to how they should handle the information.

Common Cybersecurity Compliance Mistakes Businesses Make

Sometimes compliance problems aren’t caused by ignoring regulations but by doing your regular business without thinking how it relates to the law.

Common mistakes:

Considering compliance as the completion of one project
Compliance work must not be done once and then forgotten. It requires continuous monitoring as external business conditions keep varying.

Inability to identify the stores of sensitive information
Organizations may have implemented various controls, but they are still unaware of where exactly their sensitive data is.

Storing redundant data
Organizations continue to have irrelevant files and data in their systems after the data’s usefulness has expired. It is therefore important to check on the data on a continuous basis to know whether it is still relevant.

Complete reliance on manual processing
To manually scan through large volumes of documents and data is extremely time-consuming and would not only cause a bottleneck but also hinder the ability to track things if the company grows.

Only concentrating on audits
If an organization prepares only close to the time when it receives an audit, there will probably be a very high level of stress involved. It is far preferable to take a continuous approach as it is easier to handle.

How Businesses Can Build a Better Compliance Process

Besides technology, you can also take advantage of a practical compliance solution.

One simple step is for companies to identify all applicable laws and regulations. Once they know that, they can see what kinds of information the requirements cover and where that information is located in the company:

Continuous data discovery, classification, and review will then allow companies to maintain visibility in face of business development.

Compliance doesn’t mean just collecting paper. On the contrary, having a solid understanding of the relationship between your requirements, and the data stored in your company, would be your ideal situation.

Final Thoughts

Cybersecurity compliance can be a regulation and standard game. It also includes audits and technical requirements. However, it is possible for organizations to find an easy way to do it and not see it as a complex, difficult or daunting task.

Get grounded.

Clarify for yourselves the requirements you have to follow, the data you own, the location of that data, and the parts of that data which are the most delicate and require the highest degree of protection.

Once you have a clear picture of your data environment, the task of compliance becomes more manageable. You will not only benefit from having your company’s data under surveillance, but also being able to prepare yourselves better with business growth so that instead of always struggling to keep up, you can focus on being ready.

In other words, starting with getting a clear understanding of the scope is the first step.

SafePal Data Breach: Exposes Nearly 40,000 Customers Personal Information

EzSecure — Mon, 17 Aug 2026 08:05:19 +0000

The first thing people usually think about when they hear about a crypto wallet data breach is stolen cryptocurrency, private keys, or wallet credentials.

The recent SafePal case presents a different picture.

Additionally, the company was hit by a data breach on August 16, 2026, compromising customer order data. The incident has reportedly affected 39,798 customers and exposed personal and purchase information.

SafePal said the incident did not affect customers’ private keys, seed phrases, wallet passwords, payment card information, bank details or government IDs. There was also no evidence that customer funds were accessed.

So why is this matter important?

It shows that the most sensitive data of a company is not always stored where you expect it to be.

What Happened in the SafePal Data Breach?

SafePal detected unauthorised access of customers’ order information through a vulnerability in an order tracking plugin.

The bug reportedly enabled unauthorised access to order information of other customers in some conditions. SafePal has investigated the incident and resolved the issue, and has implemented further measures to mitigate the issue.

Customers who ordered during the relevant time period were affected by the incident.

The information reportedly included details like:

Call names
Email addresses
Phone numbers
Delivery Addresses
Order info
Details of Order

Although these details might not seem as sensitive as the private key of a cryptocurrency wallet, together they can give useful information about a person.

The Crypto Wallet Wasn’t the Main Problem

The interesting thing about the SafePal case is that no one accessed customers’ cryptocurrency wallets during the incident.

What was leaked instead was information about customer orders.

And this points to a very real fact of modern businesses.

A company may have rigid controls around its mainline product, but customer information may be flowing through other systems.

For example, in an e-commerce transaction information may flow through:

Website > Ordering System > Database > Plugin > Shipping > Customer Support

Every stage can generate or hold information.

The most readily available customer data may not be in the core product.

Why Customer Order Data Matters

Information like a name, phone number, address, or purchase history is easy to underestimate.

But put together, these details can paint a much more complete picture of a customer.

For example, revealing a shipping address provides a real-world location.

A phishing email address can be aimed at.

A phone number can be used to perform social engineering.

Purchase data can reveal what someone bought and when they bought it.

For a crypto wallet company, learning that someone bought a hardware wallet might make them more vulnerable to scams that impersonate customer support.

So customer information can be very valuable even if wallet credentials and funds are untouched.

Third-Party Tools Can Become Part of Your Data Environment

The SafePal case also indicates the need to look beyond an organization’s primary systems.

Modern businesses depend on plugins, APIs, SaaS platforms, integrations, analytics programs, shipping systems and other third-party technologies.

These tools often make business operations easier, but they can also be part of the path taken by customer information.

That means that organisations need to understand not only what tools they are using, but also:

What data does each system work on
Where that information’s kept
Type of information involved
How long the information stays there
What systems can get there

Without the visibility enabled by this process, businesses often have an incomplete picture of their actual data environment.

Data Retention Is Another Important Lesson

In addition to data retention, SafePal data breach also highlights another issue related to the storage of customer records.

According to media reports, SafePal discovered a configuration fault that was allowing them to keep older-order records longer than the expected retention time. The business has then decided to limit the duration their order-data was retained in their system to 90 days.

One of the most significant questions the SafePal incident brings to our attention, besides data safety, is as follows:

What is the volume of old customer data currently at our disposal?

Keeping data doesn’t necessarily equate to being wrong. Some information must remain with you due to business, legal, regulatory, or other contractual reasons. So keeping data for those purposes is fine. The main problem comes when a business lacks these knowledge and abilities:

First, what are the pieces of information kept?

Second, the purpose of the information that is kept?

Third, what is the location where the information is stored?

Fourth, the period during which the information needs to be retained?

A thorough review at least once every while of information that has been archived can assist businesses in their decision making about what to retain.

What Businesses Can Learn From the SafePal Data Breach

The events of SafePal serve as an example providing not only crypto enthusiasts but also others with valuable lessons.

Search for Sensitive Information Outside Core Systems
Secret data can hide not only in core applications but in many other places. Order systems, plugins, shared files, databases, and customer support platforms can all include customer data.

Improve the Visibility of Your Data
Organizations should be capable of figuring out where their most sensitive information resides in the company’s ecosystem. It is one thing to know that a company is using ten softwares, and totally another thing if, say, one of them contains the customer’s personal details.

Clarify Your Knowledge of Available Information
Not every type of information is equally sensitive.

Classifying and labeling data can be really helpful. It can show a company what type of personal details it handles customer records, financial data, company employees’ information or any other kind of confidential data.

Regularly Reassess the Storage of Data
Information that is obsolete is not supposed to be kept just in case.

Companies shouldn’t overlook the question of keeping records. They need to constantly check if their need for data is still relevant and if the keeping of it complies with their business requirements.

Be Aware of Your Ecosystem
Plug-ins, third-party platforms and system integrations might all get connected to a company’s data flow.

Mapping how information passes through those systems is a crucial step to being in control of data visibility.

The Bigger Lesson From SafePal

The SafePal breach was not about a cryptocurrency theft.

According to the SafePal announcement, private keys, mnemonic seed phrases, wallets’ passwords, payment details, and government IDs were not accessed, and customer assets were never reported missing.

Still, nearly 40, 000 customers’ personal and order-related information had been exposed.

This is a key difference.

A company generally puts effort and resources towards protecting the assets that they think are their most valuable. However, information about these assets may be as crucial from the privacy and regulation aspect as the assets themselves.

Customer order may look like an ordinary business file.

However, it can also show the customer’s name, postal address, telephone number, and shopping history.

In addition, a failure by the company to track where such information is stored makes the management of it quite difficult.

Probably the most significant takeaway from the SafePal incident is simply this:

Ask not only what data your business is shielding. Inquire also what kinds of data you own, where you keep them safe, and for how long.

Certainly, in some cases, the primary data-problem may not be the data directly related to the business but a data by-product of the business.

Read the official SafePal security update here:

Unauthorized Access to a Subset of Customer Order Information — SafePal

How Does a Business Know What Data It Has?

EzSecure — Wed, 12 Aug 2026 07:40:08 +0000

Most businesses gather and store information every day without much thought. A spreadsheet here, an email attachment there, a customer form, employee records, files in cloud storage. It all accumulates.

The problem starts when a business can’t answer a simple question, “What data do we actually have?”

Good data management and compliance involves knowing what information is out there, where it is stored and who uses it. But for many businesses, that clear picture is easier to say than to do.

Why Is It Difficult to Know What Data a Business Has?

Data rarely sits still.

As the business grows, employees begin to use different applications and storage systems for their daily work. Information can be in:

Spreadsheets
Email inboxes
Shared folders
Cloud storage
Databases
Business applications
Employee devices
Archived files

Some of these locations may be managed by the IT team and others may have been created by individual employees or different departments.

This can create a dispersed data environment over time in which no one has the full picture of what is being stored.

Start by Listing Where Business Data Is Stored

The first thing you want to do is find out where your business stores data.

It doesn’t mean opening each file individually. Look at the big picture first.

Think about all the tools and systems you use in your business every day. Where do workers write documents? Where do records go? Which cloud platforms are used? Do you have systems with information that are old?

A simple list of these locations can make your data landscape more immediately understandable.

Know What Kind of Data You Have

Knowing where the data is stored is half the battle. A business has to get a handle on what sort of information is in those systems.

For example a company could have:

Employe Details
Financial Books
Business documents.
Contact Details
Customer files
Contracts
Identification cards
Internal communication

Not all files are equal in weight or sensitivity.

Knowing what is different types of information helps businesses to know what data is more important and where compliance requirements may be needed.

Don’t Forget About Old and Unused Data

Data that no one is currently using is one of the easiest things to overlook.

An old spreadsheet might be sitting in a shared folder. Archived drives may still have some previous employee records. Documents may be duplicated across multiple systems.

These files aren’t part of someone’s day-to-day work, so they can easily be overlooked.

By regularly reviewing old information, businesses can understand what they still have and if some data is still needed or not.

Ask Who Has Access to Business Data

Knowing what data you have is not enough for companies, they need to know who can see it as well.

For example, an employee may be granted access to a shared folder simply because they were given access months ago for a temporary project. Someone else might have switched jobs but still have access to information from their previous one.

Regular access reviews help businesses identify unnecessary access and keep information organised.

This is especially true when handling sensitive or confidential information.

Data Discovery Can Make the Process Easier

For a small business that only has a few files, manually checking data might appear feasible. However, with a growing number of systems and files, searching information manually gets progressively more complicated.

To find the information you are looking for, consider data discovery.

When you discover the data, the main job is to find and locate information through various systems and environments so that the business owners, management, and employees have clear insight into the data they are sitting on and how it is distributed through the company.

Solutions like EzSecure will enable your company to spot and classify sensitive information across environments. Rather than depending merely on manual searches, companies can see more clearly where sensitive data is located and what categories of data they are handling.

The ultimate goal is not just to locate more data but to understand the data that is already there.

Create a Simple Data Inventory

Once an organization is aware of data locations and information stored, creating a basic data inventory can help the company to easily control data storage and management.

An example minimal inventory contains:

The initial setup of the process shouldn’t be complicated. Teams will get a fair idea of their data through a simple inventory.

Apart from the growth of the business, the list of the products in the inventory can be extended and refreshed.

Review Your Data Regularly

Data continues changing.

Employees are hired, clients are added, software is released, outdated systems are removed, documents are duplicated and created.

Hence, you must know your data to some extent. That’s not a one off job.

Companies are recommended to periodically check their data to locate what’s changed, outdated, doubled, and where it’s stored recently.

Routine checking helps in preparation for compliance rules as well because by then the business is already familiar with the information it has been holding through the time.

Why Knowing Your Data Matters

If a business is not aware of the data that they have or what kind of data exists at least, it is almost impossible for them to make accurate decisions regarding which kind of data to retain for long-term purpose, how to handle different data types, what to do concerning confidentiality, and how to ensure their company complies with all relevant privacy laws.

Better visibility can also help reduce time wastage. Rather than an employee needing to go through several different systems or applications, if he or she wants to find a particular record, he or she knows where and how to find it because of increased visibility in data.

In addition to this, having data awareness means understanding your data which enables the Data Management function to be turned from being a guessing game to becoming a well-defined process.

Final Thoughts

Learning where your business’s data is stored doesn’t have to be rocket science.

You can begin by finding out where data is stored, recognizing what different kinds of data your business owns, checking out paper and/or electronic files that haven’t been touched for a while, identifying access level, and constantly updating the database of information sources.

In fact, as a company increases its operations, tracking all the data manually can get very difficult. That is why having good data discovery and classification procedures can help your team better understand what kinds of data they possess.

Literally, the very first thing on the path of improved data management and compliance is: just get to know the data that you have.

Why Is Healthcare Data Targeted by Hacker?

EzSecure — Mon, 10 Aug 2026 07:40:15 +0000

Healthcare has been the most expensive industry for data breaches for fourteen consecutive years, and the reasons go well beyond weak passwords or outdated firewalls. IBM's Cost of a Data Breach Report 2025 puts the average healthcare breach at $7.42 million, still the highest of any sector even after a rare year-over-year decline. In the United States specifically, breach costs climbed to a record $10.22 million per incident, according to the same report.

This guide explains, in plain business terms, why hospitals, clinics, health plans, and their vendors remain a preferred target for cybercriminals and, increasingly, nation-state actors. It walks through the mechanics of medical record value, the operational and regulatory pressure that makes healthcare organizations easier to extort, and the specific structural weaknesses, including legacy medical devices, shadow IT, fragmented vendor ecosystems, and merger-driven complexity, that widen the attack surface. Six verified breach case studies illustrate how these risks play out in the real world, from the 2017 WannaCry attack on the NHS to the 2024 ransomware attack on Change Healthcare that exposed the protected health information of roughly 192.7 million people, according to HHS Office for Civil Rights records.

The article closes with practical checklists for executives and security teams, a comparison of healthcare compliance frameworks, and a look at how knowing precisely where sensitive patient data lives, across file servers, cloud drives, and unmanaged shares, shapes the speed and accuracy of a post-incident investigation.

Introduction: A Sector Under Sustained Attack
Ask a hospital CISO what keeps them up at night, and ransomware usually tops the list. But ransomware is a symptom of a deeper problem: healthcare organizations sit on some of the richest, longest-lived, and least protected data in any industry, and they run that data through an unusually complex web of legacy systems, third-party vendors, and connected medical devices.

Verizon's 2025 Data Breach Investigations Report recorded 1,710 security incidents in healthcare, with 1,542 confirmed data disclosures. System intrusion, driven largely by ransomware, overtook miscellaneous errors as the leading cause of healthcare breaches for the first time. Ninety percent of these attacks were financially motivated, but Verizon also flagged a sharp rise in espionage-driven activity, jumping from about one percent of healthcare breaches in the prior year to sixteen percent, a signal that state-linked actors are taking a growing interest in clinical and research data, not just patient billing records.

The HHS Office for Civil Rights breach portal tells a similar story from the regulatory side. In 2024, 725 large healthcare data breaches were reported, exposing an estimated 289 million records, the worst year on record, driven overwhelmingly by the single Change Healthcare ransomware attack. Hacking and IT incidents accounted for 81 percent of all reported breaches and 99.45 percent of affected individuals that year, according to OCR's own report to Congress.

Why Healthcare Data Is So Valuable to Criminals
The simplest explanation for why hackers target healthcare data is durability. Financial data is valuable but perishable: a stolen credit card number can be cancelled within a day, and a compromised bank account can be frozen almost as quickly. A medical record cannot be reissued. A patient's diagnosis history, insurance identifiers, Social Security number, and treatment records remain accurate and usable for fraud for years, sometimes for the rest of that person's life.

That durability is compounded by breadth. A single stolen medical record commonly bundles together a patient's full name, date of birth, Social Security number, insurance member ID, home address, and clinical history in one file, everything a criminal needs to open fraudulent credit lines, submit fake insurance claims, or obtain prescription medication under someone else's identity. Financial records, by comparison, tend to hold a narrower set of reusable fields.

Healthcare Data vs. Financial Data

Types of Medical Records Criminals Target
Not every field in a patient record carries the same resale value or fraud utility. Understanding which data elements are most attractive helps security and compliance teams prioritize where discovery and classification efforts should start.

Healthcare Threat Actors
Healthcare organizations do not face a single type of adversary. Financially motivated ransomware crews, opportunistic data brokers, and, increasingly, state-linked espionage groups all have distinct reasons to be interested in a hospital network.

The Structural Risk Factors Most Healthcare Leaders Overlook

Beyond the raw value of patient data, healthcare organizations carry a set of structural weaknesses that are specific to how the industry operates. Executives who understand these factors are better equipped to ask their security teams the right questions.

Why Healthcare Records Remain Valuable Years After Theft
Unlike a payment card number, a medical record cannot be reset. A diagnosis, a Social Security number tied to a patient chart, or a family medical history remains accurate indefinitely. This means stolen healthcare data continues to circulate on criminal marketplaces long after the original breach has faded from headlines, and victims can face fraud attempts years later with no clear link back to the original incident.

How Healthcare Supply Chains Increase Attack Surface
A modern hospital does not operate as a single, self-contained IT environment. It relies on claims clearinghouses, laboratory partners, medical billing companies, staffing agencies, imaging vendors, and software providers, each with its own access into hospital systems or its own copy of patient data. The Change Healthcare attack demonstrated this vividly: a single vendor's compromise disrupted claims processing for thousands of downstream providers across the country. HHS OCR's own reporting notes that business associates were involved in roughly a third of major healthcare breaches in 2024, and those vendor-related incidents accounted for about seventy-five percent of all individuals affected that year.

Why Mergers and Acquisitions Create Hidden Security Risks
Healthcare consolidation is common, and every merger or acquisition brings together previously separate networks, file shares, electronic health record systems, and data governance practices. In the months following a merger, security teams are often still mapping which systems exist, who has access to them, and where sensitive files were left behind by the acquired organization. Attackers are aware of this integration window and frequently target newly merged entities where visibility is weakest.

The Impact of Legacy Medical Equipment
Connected medical devices, from infusion pumps to imaging systems, often run on operating systems that can no longer be patched, and many were never designed with modern network security in mind. Replacing this equipment is expensive and clinically disruptive, so hospitals frequently continue operating known-vulnerable devices for years, creating a persistent foothold that attackers can use to move laterally once inside the network.

Shadow IT Inside Hospitals
Clinical staff under pressure to move quickly will sometimes adopt unsanctioned tools, personal cloud storage, messaging apps, or file-sharing services, to get their work done, especially when approved systems feel slow or cumbersome. Each of these unmanaged tools can become a repository of unencrypted patient data that IT and security teams do not know exists, and therefore cannot protect or monitor.

Cloud Collaboration Risks
The same collaboration platforms that improve care coordination, shared drives, messaging tools, and telehealth platforms, also multiply the number of places sensitive files can end up. A spreadsheet containing patient identifiers can be copied into a shared drive, forwarded by email, or synced to a personal device in seconds, often without anyone tracking that a sensitive file now exists in a new location.

Third-Party Healthcare Vendors
Billing services, transcription companies, cloud EHR hosts, and IT support contractors routinely need some level of access to patient information. Each vendor relationship is a potential entry point, and a breach at any one of them can expose data belonging to every healthcare organization that vendor serves, as the Change Healthcare and Synnovis incidents both illustrated at very different scales.

Insider Risks
Not every incident originates outside the organization. Employees with legitimate access can misuse patient records out of curiosity, financial motive, or simple negligence, such as sending a file to the wrong recipient. Verizon's 2025 DBIR found that internal actors were involved in roughly thirty percent of healthcare breaches, a notably higher share than in most other industries.

Nation-State Targeting of Healthcare and Research Data
Hospitals affiliated with universities, and standalone research institutions, hold clinical trial data, vaccine and drug research, and genomic datasets that can be commercially or strategically valuable to a foreign government. Verizon's 2025 report noted a sharp jump in espionage-motivated healthcare breaches, underscoring that state-linked groups are no longer focused solely on financial institutions and defense contractors.

Business Email Compromise in Healthcare
Healthcare finance departments manage large volumes of vendor and insurer payments, making them attractive targets for business email compromise schemes, where an attacker impersonates a known vendor or executive to redirect a payment. These attacks do not always involve a data breach in the traditional sense, but they exploit the same trust relationships that make the healthcare supply chain hard to secure.

How Stolen Healthcare Data Turns Into Fraud

Identity Theft Using Medical Records
Because a medical record typically contains a patient's full identity profile in one place, it gives criminals everything needed to open credit accounts, file fraudulent tax returns, or take out loans in the victim's name. Unlike a stolen card number, this kind of identity theft can be difficult for the victim to detect until the damage is well underway.

Medical Insurance Fraud
Stolen insurance identifiers can be used to submit fraudulent claims for services never rendered, or to obtain medical care under someone else's coverage. This form of fraud can also corrupt the victim's own medical record with someone else's treatment history, creating downstream risks to patient safety if the erroneous information influences future care decisions.

Prescription Fraud
Patient and prescriber information stolen from a healthcare organization can be used to obtain controlled substances fraudulently, either by forging prescriptions or by using stolen identities to obtain refills. This is one of the more clinically dangerous forms of medical record misuse, since it can also mask patterns of drug diversion.

Clinical Trial and Research Data Theft

Pharmaceutical and academic research partners generate enormous volumes of trial data, much of which has significant commercial value long before a drug or therapy reaches market. Theft of this data can undermine years of investment and, in cases involving state-linked actors, has clear strategic implications beyond ordinary financial crime.

Six Verified Healthcare Breach Case Studies

The following incidents are among the most consequential healthcare cyberattacks on record. Each illustrates a different combination of the risk factors described above, and each carries lessons for how visibility into where sensitive data resides can change the course of an investigation.

1. Change Healthcare (2024)
Timeline: The BlackCat/ALPHV ransomware group gained access to a Change Healthcare Citrix portal on February 12, 2024, using compromised credentials on an account that lacked multi-factor authentication. The company disclosed the attack on February 21, 2024. HHS OCR was later notified, in July 2025, that the breach ultimately affected approximately 192.7 million individuals, according to OCR's own incident FAQ.

Attack Method: Ransomware deployed after initial access through a single unprotected remote access account, followed by data exfiltration before encryption.

Business Impact: UnitedHealth Group reported the cost of the incident had reached approximately $2.457 billion by its Q3 2024 earnings report. Claims processing was disrupted nationwide for weeks, affecting pharmacies, hospitals, and physician practices that relied on Change Healthcare's clearinghouse services.

Regulatory Impact: HHS OCR opened a HIPAA compliance investigation, an unusually early move for the agency given what it described as the unprecedented scale of the incident. Multiple state attorneys general, including Nebraska, filed suit, and the case was consolidated in multidistrict litigation.

Lesson: The scale of this breach was driven not just by the intrusion itself but by how much sensitive data the organization did not know it needed to review. A clear, current inventory of where PHI resided across systems would have accelerated the months-long process of determining exactly whose data was affected and what it contained.

2. CommonSpirit Health (2022)
Timeline: Attackers had access to CommonSpirit Health's network between September 16 and October 3, 2022. The organization detected the ransomware attack on October 2, 2022, and confirmed patient data theft in December 2022.

Attack Method: Ransomware attack that led to data theft from file servers before systems were taken offline. More than 164 facilities across 13 states were affected.

Business Impact: CommonSpirit estimated the total cost of the attack at approximately $160 million, contributing to a $1.4 billion operating loss for fiscal year 2023.

Regulatory Impact: CommonSpirit reported the breach to HHS OCR as affecting 623,774 individuals and faced a class-action lawsuit alleging negligent security practices.

Lesson: The attackers stole data from only two file servers rather than the full medical record system, but pinpointing exactly what those servers held, and which patients across a sprawling, multi-state health system were implicated, still took months. Faster, more precise data discovery on those specific servers could have shortened the notification timeline considerably.

3. Universal Health Services (2020)
Timeline: UHS was hit by Ryuk ransomware on September 27, 2020, forcing more than 250 US hospitals and behavioral health facilities to shift to manual, paper-based operations.

Attack Method: Ryuk ransomware, typically delivered through prior Emotet or BazarLoader infections, encrypted systems across the enterprise network. UHS did not pay the ransom and restored operations from backups.

Business Impact: UHS reported an aggregate pre-tax impact of approximately $67 million for the year, driven mainly by lost patient activity during the recovery period, according to its own financial disclosures.

Regulatory Impact: The incident drew scrutiny from federal agencies and became a widely cited case in FBI and CISA warnings about ransomware targeting the healthcare sector during the COVID-19 pandemic.

Lesson: Because electronic health records were largely unaffected, the incident centered on operational disruption rather than a confirmed large-scale PHI exposure. It illustrates why discovery efforts should extend beyond the primary EHR to the surrounding systems, workstations, and file shares that also touch patient information.

4. Synnovis / NHS (2024)
Timeline: The Qilin ransomware group attacked Synnovis, a pathology services provider for several London NHS trusts, on June 3, 2024. The NHS declared its first-ever critical incident for a cyberattack the following day.

Attack Method: Attackers gained access through a service account that lacked multi-factor authentication, then exfiltrated data before encrypting Synnovis systems.

Business Impact: More than 10,000 outpatient appointments and over 1,700 operations were cancelled or postponed. London hospitals faced a critical shortage of O-negative blood as a direct result of the disruption to pathology services.

Regulatory Impact: King's College Hospital NHS Foundation Trust confirmed in 2025 that the cyberattack was a contributing factor in a patient's death, one of the first formally documented cases linking a ransomware attack to a patient fatality. Synnovis took roughly eighteen months to complete its forensic review before notifying affected organizations, citing how unstructured and fragmented the stolen data was.

Lesson: Synnovis itself described the stolen data as unstructured, incomplete, and fragmented, which is precisely why the investigation took so long. This case is a direct illustration of why knowing, in advance, where sensitive files live and what they contain is not just a compliance nicety but a factor that can directly affect how quickly patients and partner organizations learn they were affected.

5. Scripps Health (2021)
Timeline: Attackers accessed Scripps Health's network beginning around April 29, 2021, and deployed ransomware that crippled systems for close to a month.

Attack Method: Ransomware attack that resulted in theft of unencrypted files containing patient health information, Social Security numbers, and driver's license numbers.

Business Impact: Scripps reported approximately $113 million in lost revenue for May 2021 alone, along with the operational burden of reverting to paper-based patient records during the outage.

Regulatory Impact: The breach was reported to HHS as affecting 147,267 individuals; a related consolidated class action was later settled for more than $3.5 million.

Lesson: The fact that stolen files were stored in non-encrypted form was central to the litigation against Scripps. Locating and classifying sensitive files that are stored without adequate protection, before an attacker finds them, remains one of the highest-value steps a healthcare organization can take.

6. WannaCry and the NHS (2017)
Timeline: The WannaCry ransomware worm spread globally starting May 12, 2017, and infected at least 81 of 236 NHS trusts in England along with 603 primary care organizations, according to the UK National Audit Office.

Attack Method: A self-propagating worm that exploited a known Windows vulnerability for which a patch had already been available for weeks before the attack.

Business Impact: The Department of Health and Social Care estimated the total cost to the NHS at approximately £92 million, including roughly £20 million in lost output and £72 million in IT recovery costs. Around 19,000 appointments and operations were cancelled.

Regulatory Impact: The National Audit Office concluded the attack could have been prevented through basic IT security practices, and the UK Parliament's Committee of Public Accounts described the incident as a wake-up call for the health service.

Lesson: NHS Digital stated it believed no patient data was stolen in this incident, but the operational impact alone was severe. WannaCry remains the clearest illustration that healthcare's exposure is not limited to data theft; unpatched, unmanaged systems can bring patient care to a halt even without a single record being exfiltrated.

Healthcare Compliance Comparison
Healthcare organizations operating internationally, or handling data for patients in multiple jurisdictions, must navigate overlapping and sometimes inconsistent compliance regimes. The table below summarizes the frameworks most relevant to healthcare data protection.

Business Impacts of Healthcare Breaches

Post-Incident Investigation: Why Data Discovery Matters

Every case study in this article shares a common thread: the length and difficulty of the investigation was directly tied to how well the organization understood where its sensitive data actually lived. Synnovis needed roughly eighteen months to determine which patients were affected because the stolen data was unstructured and scattered. Change Healthcare's individual notification process stretched across most of a year. These are not failures of intent; they are the predictable result of not having a current map of sensitive data across file servers, cloud drives, and endpoints before an incident occurs.

What Many Healthcare Leaders Overlook

Sensitive data discovery is treated as a one-time compliance exercise rather than an ongoing practice.
File shares and cloud drives created for a single project often outlive their original purpose and are forgotten.
Departing employees and completed vendor contracts frequently leave sensitive files behind in shared locations.
Data classification is applied inconsistently across on-premises servers, SharePoint, OneDrive, and Google Drive.

Questions Every Hospital Should Ask

Do we know, right now, every location where files containing protected health information are stored?
Could we tell a regulator, within days rather than months, roughly how many patient records were present on a specific compromised server?
Are our file servers, SharePoint sites, and cloud drives classified consistently, or does each system use its own ad hoc labeling?
When a vendor relationship ends, do we verify that sensitive files shared with that vendor have been located and removed?
If a single laptop or file share were compromised tomorrow, how long would it take to determine what sensitive data it contained?

Sensitive Data Discovery Workflow

Investigation Workflow

Healthcare Breach Response Timeline

Checklists

Incident Checklist (Security Team)

Isolate affected systems and preserve logs before making changes
Identify the initial access vector and whether it remains open
Determine which repositories the attacker could have reached
Run sensitive data discovery against affected and adjacent repositories to scope PHI exposure
Coordinate with legal and compliance on notification obligations and timelines
Document findings for regulators, cyber insurance carriers, and executive leadership

Executive Checklist (Leadership Team)

Confirm the organization has a current inventory of where sensitive patient data is stored
Verify that third-party vendors with access to PHI are contractually required to notify you promptly of their own incidents
Review cyber insurance coverage against realistic breach cost benchmarks for the healthcare sector
Ensure legacy and connected medical devices are included in the organization's risk register
Confirm a communication plan exists for patients, staff, media, and regulators before an incident occurs

Expert Insight: Executive and Compliance Perspectives

Executive Perspective

Boards increasingly ask not just whether an organization was breached, but how quickly it could tell patients and regulators what was taken. That answer depends less on the sophistication of the attacker and more on whether the organization already knew where its sensitive data lived.

Compliance Perspective

HIPAA's 60-day notification clock does not pause for a complicated investigation. Organizations that can quickly scope which files and which patients were affected are in a far stronger position to meet that deadline, and to avoid the kind of open-ended regulatory scrutiny seen in the Change Healthcare case.

You can read the complete detailed version of this article on the official EzSecure blog here:👉Why Is Healthcare Data Targeted by Hacker?

Key Takeaways

Healthcare data is targeted because it is durable, cannot be reissued, and enables multiple types of fraud from a single stolen record.
Healthcare has recorded the highest average data breach cost of any industry for fourteen consecutive years, reaching $7.42 million globally and $10.22 million in the United States in 2025, per IBM.
Structural factors, including vendor sprawl, legacy medical devices, shadow IT, and merger-driven complexity, widen the attack surface well beyond the hospital's own network perimeter.
Every major case study in this article shows that investigation speed and accuracy depend heavily on how well the organization understood where sensitive data was stored before the incident occurred.
Sensitive data discovery is a distinct capability from prevention or detection. It supports investigation, compliance reporting, and remediation, and works best as an ongoing practice rather than a reactive one.

Why Understanding Your Data Is the First Step to Better Business

EzSecure — Thu, 06 Aug 2026 09:36:37 +0000

Every day every business produces more data. Information such as customer details, internal staff records, invoices, contracts, emails and reports are all part of the day-to-day activities. As businesses grow, so will their amount of information that is collected and stored.

The challenge is not really businesses having too much data but businesses not knowing what to do with their data. The information is fragmented across different systems, circulated to different departments and eventually gets lost to time.

Without knowing exactly what your data contains, it becomes impossible to make informed decisions, be organized and comply with regulations.

Why Understanding Your Data Matters for Every Business
Although most businesses are aware that they have data, few of them know exactly what data they possess, where it is stored, or who has access to it.

Pause for a moment to consider your own business. Customer information might be kept in your CRM, invoices in accounting software, contracts in cloud storage, employee records in HR systems, and significant conversations in your email inboxes. Various departments tend to save their own copies of files, which results in several versions of the same information.

When data is spread across different locations, it becomes

difficult to answer simple questions like:

What files contain sensitive information?
Are we keeping the same data in more than one place?
Is information that is out of date still being retained?
Who can access the important business files?

It enables businesses to function more efficiently and to make better decisions.

Data Visibility Helps You Understand Your Business Better
You can’t manage information that you can’t see.

Many think that their data is structured until they need to locate a specific document, answer an audit, a customer’s request or an in-house review. This is when they discover that their data is spread across multiple systems.

Data visibility provides businesses with a full view of the data’s location. Teams don’t have to go through several folders, databases and cloud platforms to find where the sensitive data is and what it is being used for.

This kind of visibility allows you to run business operations more easily and speeds up the search time for information.

Sensitive Data Discovery Starts with Knowing What You Have
Not all files contain sensitive data, but it’s important to identify those that do.

Companies have a tendency to keep financial information, customer contact details, contracts, employee records and other confidential documents in a way that is not thought about.

This is where sensitive data discovery comes in handy. As opposed to manually inspecting thousands of files, companies can determine the location of sensitive data and obtain more control over its management.

At EzSecure, we assist organizations in finding and categorizing sensitive data in cloud storage, databases, file servers and business applications. This provides teams with a sense of visibility and an understanding of their data before making decisions regarding compliance, governance and retention.

Better Data Management Leads to Better Business Decisions
Not every file will have sensitive information, but it is important to identify files that do have sensitive information.

Businesses will tend to hold money, customer contact details, contracts, employee records and other confidential documents in a manner that is not considered.

At EzSecure, we assist organizations in finding and categorizing sensitive data in cloud storage, databases, file servers and business applications. This gives teams visibility and a knowledge of the data prior to making decisions on compliance, governance and retention.

Customer Trust Depends on How You Manage Data
Not all files will contain sensitive information however it is important to recognize the files that do contain sensitive information.

Businesses will keep money, customer information, contracts, employee records and other confidential information in a way that is not taken into consideration.

This is where sensitive data discovery comes in handy. Companies are able to identify where sensitive data lies, and gain greater control over its management, instead of manually reviewing thousands of files.

Whether storing in cloud solutions, databases, file servers or business applications. This gives teams visibility and a knowledge of the data prior to making decisions on compliance, governance and retention.

Understanding Your Data Makes Compliance Easier
Compliance is one of those issues which is considered by many businesses just after the changes in the regulations or the advent of an audit on the horizon.

But in fact, compliance begins way before that with knowing your data.

If you are aware of the information you have, its location and which information is sensitive, compliance can be much easier. You already have visibility to meet compliance needs without having to scramble around to find files or sensitive information.

This is one of the reasons why businesses are prioritizing data discovery and visibility over a more comprehensive compliance program.

Build a Stronger Business by Understanding Your Data
Every business wants to be more efficient, make better informed decisions, and foster customer trust. It is these objectives that will seem easier if only your data is clearly understood.

Familiarization with the data type you collect, the storage location, the kind of data which is confidential and the people having its rights will enable a much more powerful platform for company development.

With companies producing more and more data every year, visibility is no longer a feature but a necessity. Today’s insight into your data is a way to tomorrow’s business decisions.

What Every Organization Should Know About AI Compliance

EzSecure — Sat, 01 Aug 2026 11:10:48 +0000

Artificial Intelligence is no longer a technology that companies are just trying out. It’s becoming woven into the fabric of how business gets done, from analysing documents and enhancing customer experiences to automating workflows and empowering critical decisions.

As AI adoption accelerates, compliance is entering a new phase. Organisations are no longer dealing with only traditional compliance requirements. Now, they must learn how data is used in AI systems, how decisions are made and whether their AI practices align with regulatory expectations.

Gartner predicts that by 2026, over 80% of enterprises will be using generative AI applications or deploying AI-enabled applications. The swift embrace indicates AI is turning into a business imperative – but it also underscores why organisations need a stronger approach to AI compliance.

Artificial intelligence can help companies to operate more efficiently, but without governance it can also create new compliance headaches.

Why AI Compliance Is Becoming Essential for Organizations

Earlier methods of compliance focused on reviewing a relatively small amount of data manually, regularly revising policies, and conducting periodic audits at fixed intervals only.

Rapidly evolving technology, particularly AI, has revolutionized how companies collect and process digital information. Businesses have learned the lessons and are leveraging artificial intelligence at an unprecedented scale and speed to keep pace with a digital age.

For instance, companies use AI for:

Review huge amount of documents.
Dive into customer communications.
Find hidden risks.
Turn regulatory reporting into automated compliance
Check what regulations are updated
Lend an organizational hand in decision-making

Financial sectors are adopting AI more frequently for fraud detection, transaction monitoring, and reporting in compliance with regulations. Likewise, medical service providers want to test AI-based methods that allow for faster documentation and operational work.

On the other hand, if AI is to be part of these day-to-day operations, business owners or managers should also consider:

Where do the AI systems get the data from?
If it’s a personal or sensitive matter?
Can the company clearly see how that data has been sourced and used?
Can compliance officers explain the use of AI?

With time, these aspects have turned AI compliance into a necessity for practically all businesses at once.

How AI Is Changing Traditional Compliance Processes

Compliance is a key business function that ensures organizations follow laws, regulations, and other standards, such as internal policies. With the help of artificial intelligence technology, compliance efforts can now be taken from the last moment or after the fact type of approach to the one that’s more anticipatory and proactive. Before, compliance teams would come across a problem only during the audits or after poring over masses of documents. But now with artificial intelligence, businesses are able to study the data without any breaks and spot possible issues before they become problems. One typical example is that some of the major companies are employing AI-based software in screening the terms of agreements and policies so compliance staff can locate the missing elements or obsolete information right away. Another great use is in sectors where the rules are very rigid, for instance, banking and insurance in these sectors, companies use AI in order to detect patterns and spot activities that may be questionable and thus need to be investigated. At the same time as a powerful tool, it should be remembered that AI cannot take the role of skilled human compliance officers. Rather, they get the assistance of these more powerful resources so that not only are their decisions quicker but also better-informed.

How AI Adoption Is Changing Enterprise Compliance

The use of AI assistants in offices clearly demonstrates the need for more solid compliance systems by companies.

For example, as soon as many companies started using products like Microsoft Copilot, AI agents that can instantly provide a user with facts about his or her current rights and responsibilities, several organizations raised important compliance concerns.

One of the compliance questions was:

Are companies really aware of the different pieces of information scattered across the whole company and who is allowed to find and use each of them?

The issue was not only the AI technology. It was the data foundation underneath it.

In a case where very sensitive documents or internal records already had open permissions, AI made it very easy to find and access the information, which was the opposite of the intended effect.

This case is a good example illustrating that AI compliance is done before even introducing AI tools, i.e., getting an organization’s data structure right and having data governance in place are what AI compliance really consists of.

AI Compliance Depends on Data Visibility

AI systems rely on a steady supply of data to produce results. If an organization has no idea what data it possesses, managing the compliance risks related to AI would be quite challenging.

Sensitive data is distributed across the following mediums in most businesses:

Cloud storage
Various kinds of databases
Software-as-a-Service
File-sharing platforms
Staff-generated documents

This eventually leads to the loss of visibility.

The following issues may not be clarified by the organizations:

Locations with confidential information
Files containing personal or confidential data
Authorized users of significant files
Availability of outdated files
Which files will be safe for AI processing

Data discovery and classification play critical roles in an AI compliance strategy.

It is only after a company knows clearly what its data holdings are that it might allow AI system handling of that data.

The Growing Challenge of Shadow AI

One of the biggest compliance challenges related to AI isn’t enterprise AI adoption per se, but rather employee use of AI which is the real issue.

Many workers are now using AI to perform their daily activities such as:

Writing emails
Summarizing documents
Creating reports
Analyzing information

Although these AI tools make work more efficient, the sharing of sensitive business data by employees who don’t know that they’re sharing their data could lead to compliance issues.

Organizations require understanding of AI use and well-defined policies to address these kind of challenges. As this trend is often called “shadow IT” and it was indeed a big problem in the earlier days.

There is no other way than to get the picture on how the AI is being used and to set clear policies to manage this risk.

Key AI Compliance Challenges Organizations Should Prepare For

Data Privacy and Responsible AI Usage
AI models typically operate on vast volumes of data. Companies are required to follow the data privacy rules and protect the confidential information.

Lack of Data Classification
If data segregation is not properly done, the company might not recognize which type of data needs special treatment before going into machine learning models.

Limited Transparency
Decision-makers need an account of the ways AI uses data and how it contributes to conclusions, particularly when such outcomes affect customers, staff, or partners.

Changing Regulations
Governments worldwide are introducing AI-related guidelines and regulations. Organizations need flexible compliance programs that can adapt as requirements evolve.

Building an Effective AI Compliance Strategy

One does not become compliant with AI by merely picking an AI solution or tool.
A true compliance path with AI starts with gaining insight into the data of an organization and developing solid governance structures.

Organizations should work on:

Finding out where sensitive data is located
Putting in categories the types of confidential information
Forming standards for AI use on-site
Keeping track of AI operations
In depth checks on external AI solutions
Keeping the necessary data to evidence the compliance work done

Such an initial phase enables businesses to implement AI while retaining oversight of their data assets.

Companies that choose to work in compliance with EzSecure will get an overview of their data and be able to easily identify sensitive elements of data across their operations. With this knowledge, compliance staff are better informed about the existence of valuable data, and consequently, the company can benefit from data-driven choices when integrating AI compliance tools into their workflow.

The Future of AI Compliance

The use of AI will further transform the way organizations handle compliance. It will speed up compliance procedures, make them smarter, and, as a matter of fact, turn them more proactive.

Yet, no matter how great the AI is, the organizations can’t just depend on it to get them out of regulatory dilemmas alone.

Rather, the development of AI compliance is going to require the integration of AI strengths with solid data governance, well-defined policies, and full visibility into organizational information.

Enterprises that are data-aware will not only be in a position to responsibly deploy AI, but also to face compliance challenges and foster relationships with customers and other stakeholders.

AI is definitely a revolution for compliance work, but it is the organizations who have deep knowledge of their data that will be able to respond most competently to future changes.