DEV Community: Aashi Agarwal

Securing AI Memory: The Protection of Information Stored by Intelligent Systems

Aashi Agarwal — Wed, 15 Jul 2026 05:47:28 +0000

The development of artificial intelligence technologies has been impressive during the last ten years. Initially, AI tools operated independently, performing functions without storing any information obtained from the previous work with the user. Nowadays, the situation is changing, and AI is becoming more personalized. Virtual assistants remember preferences, recommendation systems learn about the behavior of users, and language models can maintain contexts throughout multiple interactions. All this allows talking about memory of AI systems that changes drastically the interaction between people and technologies.

On the one hand, the memory makes AI systems smarter and more useful for the person who uses it. On the other hand, it leads to one of the biggest challenges of contemporary cybersecurity – AI memory security.

Knowledge About AI Memory

AI memory is defined as the capability of an AI system to remember and utilize information. It contrasts with regular computer software in that the former is able to learn from past interactions and adjust itself accordingly while the latter handles each request separately.

The various types of AI memory that are currently in use include the following:

Chatbots remembering past conversations.
Virtual assistants learning user preferences.
Recommendation engines analyzing user behavior.
Autonomous agents storing operational data.
Enterprise AI applications storing organizational knowledge.

With the help of AI memory, AI systems can offer users tailored services. A virtual assistant will be able to remember meetings schedule; an e-commerce website – make recommendations based on previous purchase activity; customer support bots – offer faster responses by using information about previous interaction.
The volume of data stored by AI systems is growing rapidly.

Why AI Memory Is Important

Memory is the backbone of intelligence. People use memories for learning, solving problems, and forming connections. Likewise, AI systems require memory in order to make better decisions and offer valuable help.

Lacking memory means that each interaction with an AI system starts from zero. Users will have to repeat themselves, giving the same preferences and instructions each time. Memory makes it possible to be continuous, increasing efficiency and practicality.

Businesses are already utilizing memory in their work. AI systems can be used to study customer history, observe supply chains, automate processes, and help people perform complex actions. In the field of medicine, AI memory can be useful for diagnosing patients using past data. In the finance industry, it may be used to detect frauds and unusual patterns of payments.

Memory has many advantages, but there are also some drawbacks to consider. The more data is stored in an AI system, the more valuable it becomes to hackers.

The Security Threats Posed by AI Memory

There are certain security issues linked with AI memory that are distinct from the ones linked with conventional database management systems or applications.

Data Breaches
Most often than not, the AI systems contain vast amounts of sensitive data like people's preferences, chats, business documents, and other critical data. The attack on such memories would mean that the attacker gains access to some valuable or confidential data.
Since AI systems interact with various other applications and services, a single attack would mean that many people are affected at once.

Unauthorized Access
Due to insufficient controls regarding access, individuals or applications may get the data that is supposed to remain secret. There is a risk of attack on the unprotected memory of an AI system, which can help in gaining unauthorized access to the data stored there.
It is essential for companies to control the access to their AI memories.

Memory Poisoning
One of the most dangerous forms of attacks is memory poisoning, during which attackers inject incorrect data in order to influence the further performance of the AI system.
For instance, the attacker may cause an AI assistant to save incorrect instructions.

Privacy Issues
Most people are not aware of the amount of data that the AI system stores. Conversations, browsing activities, purchasing history, and preferences could all form part of the AI’s memory.

There are some critical issues here to consider:

What kind of data should the AI system store?
For how long should the memories be stored?
Whose property is the stored data?
Can the users erase the memory of the AI?

With the increasing personal nature of the AI, privacy issues will increase too.

Challenges in Securing AI Memories

Securing AI memory is more difficult than protecting a conventional database because an AI system is flexible, adaptive, and continuously learns from new information.

First, the volume of memory that needs to be protected can become immense. In contemporary times, AI applications may be processing information from millions of people, which makes it necessary to have an extensive amount of memory to be protected.

Secondly, the lack of transparency presents a problem for securing AI memory because many models act as "black boxes." Therefore, it is not possible to know how AI retains information and how it uses this data to provide outputs.

Thirdly, there is a challenge of deciding when information should be deleted and whether to retain memories. Retaining the data poses a security risk, but at the same time deleting data quickly diminishes the effectiveness of the AI application.
Lastly, the regulatory environment around AI memory is still under development.

Solutions for Secure Memory in AI

As artificial intelligence becomes more embedded in everyday life, stronger measures need to be taken to protect the information stored in memory.

Strong Encryption
Sensitive information should always be encrypted while being stored and transported in AI memory. In that way, third parties would be unable to access the information.

Access Management
Strong access management systems would lower the risks of accessing AI memory by anyone who is not supposed to. People and applications should have only minimum rights to perform necessary functions.

Audits
Businesses should frequently audit the systems in order to detect vulnerabilities, monitor data usage, and ensure proper functioning of security policies.

User Control
Users should have more control over the AI memories and have opportunities to look through, modify, or erase data.

Ethical AI Management
Firms need to create policies regarding collection, storage, and usage of AI memory.

Looking Ahead

AI memory is poised to revolutionize technological advancements through the ability to make digital technologies adaptive, efficient, and personalized. Intelligent systems that can remember past context and gain learning from previous experiences can increase productivity, provide improved customer satisfaction and facilitate decision-making in different industries.

But memory carries with it responsibility. The information retained in the form of memory by the AI systems could be personal conversations, financial transactions, and other sensitive information. It is important to safeguard the information in order to build trust in artificial intelligence.

Not only will the future of AI depend upon how intelligent the machines can become, but also how secure their memories are. With memory-based systems becoming an integral part of our daily lives, security of AI memory will become an indispensable part of the digital world.

Find more resources on cybersecurity, threat intelligence, digital risk, privacy compliance, and consent management through IntelligenceX CyberSecurity and ConsentX. IntelligenceX helps organizations identify and understand emerging cyber threats through focused digital intelligence analysis and investigations, while ConsentX empowers businesses to achieve global privacy compliance with comprehensive consent management, cookie compliance, and data privacy solutions.

Beyond Humans: The Role of Non-Human Identities in Changing the Future of Trust

Aashi Agarwal — Wed, 15 Jul 2026 05:21:12 +0000

For centuries, identity was an uncomplicated matter. People had a name, face, and documentation for their identity verification. However, in the world of technology, identity has become much wider than that of human beings. Currently, any application, bot, device, system of artificial intelligence, and automated services need their identities too. Collectively referred to as Non-Human Identities (NHIs), this type of entity is one of the most critical elements of modern technologies.

With the use of cloud computing, automation, and artificial intelligence becoming widespread among organizations and governments, the amount of non-human identities is rapidly increasing. Even though they facilitate our life and speed up many technological processes, non-human identities bring another question: How can we manage and protect them?

Non-Human Identities – What Are They?

When it comes to non-human identities, we are talking about any kind of digital identity that does not belong to a human but is used by a computer program or machine. NHIs help programs and machines authenticate themselves and exchange information.

Examples include:

AI agents
Internet of things devices
APIs and microservices
Bots and scripts

Whenever applications connect to databases, smart devices connect to the Internet, or AI assistants perform certain tasks, NHIs are behind the scene.

Unlike user accounts that need a break once in a while, NHIs work 24/7 and sometimes on a very large scale. An organization might hire thousands of employees, but have tens or hundreds of millions of machine identities at the same time.

Why Are NHIs Becoming More Popular?

The growth in the number of NHIs is related to the digitization of the corporate world. Organizations are becoming more dependent on using clouds, automation, and distributed architectures to stay relevant. Nowadays, applications are not developed to operate as a single system on one computer but consist of dozens of connected services.

Furthermore, artificial intelligence is contributing to the problem. Agents use AI to examine financial transactions, create content, identify potential fraudulent activities, perform logistics operations, and help customers. Each of these agents needs some kind of identity and authentication.

Another factor which should be considered here is the Internet of Things. Modern factories, smart cars, healthcare equipment, and household devices have their own machine identities.

Security Risks Related to NHIs

Even though NHIs are more efficient, they present a substantial security risk as well. Machine identities are vastly more numerous than human identities in most companies, but they get way less attention.

There is a problem with credentials management, since applications use API keys, certificates, and tokens to authenticate themselves. If they get compromised, malicious actors can easily access sensitive resources.

The following security risks exist related to NHIs:

Poor Identity Management
As more applications and services are being used in companies, credential proliferation happens very quickly. In some cases, secrets are stored in unprotected places such as repository systems or configurations. Meanwhile, many businesses face problems with tracking what applications have access to important data and what credentials remain active.

Overprivileged Access
Many machine identities are given access beyond reasonable limits. A single breach in an application or bot can lead to revealing too much sensitive information due to lack of access controls.

AI-Based Threats
The development of generative AI brings new challenges with it. Autonomous AI can do much more sophisticated activities – ranging from code writing to business decisions. And as more control is given to these systems, potential risks for compromised AI identity grow.
The problem is not anymore about the ability of machines to operate on their own. It is about the possibility of people to control their creations.

The Identity Crisis of Trust

Fundamentally, the NHI problem is not just a technical issue but rather a philosophical one as well.

Societies of people thrive on trust. We trust doctors based on qualifications, banks based on laws, and government based on legal framework. In the online world, the notion of trust has always been centered on human beings.

Non-human identity creates an anomaly in this paradigm. Machines are capable of making decisions, conducting transactions, and communicating without human interaction. However, machines have neither any intent, nor accountability, nor any sense of morality.

The following question arises here:
To what extent should autonomous machines be entrusted with responsibilities?

These issues will become even more significant as AI begins to penetrate daily life.

This is currently happening in relation to the internet.
Businesses have a heavy reliance on chatbots and other automated services, which affect how users interact online. The distinction between human and machine interaction is becoming blurred.

Organizational Responses to This Issue

In response to the increasing complexity of NHIs, firms are deploying security approaches to tackle the problem.

Zero Trust Architecture
The concept of Zero Trust is straightforward. Nothing should be trusted by default, meaning every identity, whether human or machine, must constantly prove its legitimacy before accessing the system.

Least-Privilege Access
Every machine identity should have the minimum permissions necessary. Access limitations reduce the extent of the damage when credentials get compromised.

AI Governance
Along with the rise of autonomous systems, firms are adopting guidelines for the deployment, oversight, and monitoring of autonomous agents.

This acknowledges that identity management should no longer consider just humans.

Looking Forward

The amount of identities that are not human will keep growing as AI, robots, and automation technology integrate into people’s daily lives. The next generation of digital eco-systems could have trillions of machine identities communicating within the network.

Identity is no longer about verifying the identity of a person. Identity is increasingly about choosing which machines will be allowed to access and have autonomy and control.

The next identity crisis might not be about hacked password or fabricated accounts. The next identity crisis might involve billions of invisible digital entities working in the background and making decisions for us.

Moving from Reactive Defense to Predictive Defense

Aashi Agarwal — Thu, 09 Jul 2026 08:15:56 +0000

Traditionally, the concept of cybersecurity has relied on the basic principle of detection followed by the reaction. The principle is still relevant, however, now it is insufficient. With new sophisticated threats becoming faster, automated, and stealthier, companies are now leaning towards the strategy called predictive defense.
This shift is crucial for a number of reasons. Modern hackers do not give their victims any time to react. They scan, probe, impersonate, automate, and adapt. This means that reacting only to alerts or intrusions will most likely mean being behind in the race. Predictive defense strives to change that equation.

Reasons Why Traditional Security Fails to Succeed

Traditional security assumes that there would be some threat first, and it should be noticed in time by the defender. Such a strategy was applicable back then when there were less obvious attacks and they occurred much slower. In the current environment, most attacks occur hidden in legitimate traffic, distributed via cloud environments or trusted users. An attacker has enough time to move further in the network and steal critical information before any traditional alert triggers.
The other problem is connected to scale. There are lots of alerts and other information that is created daily. Security teams can get distracted by such an amount of data and pay attention only to individual threats without seeing the bigger picture that tells about attack lifecycle and how it occurs. Reactive security tends to solve the problem by paying attention to symptoms while predictive defense pays attention to the conditions for appearance of those symptoms.
Finally, there is the problem of dwell time. Some attackers prefer to stay in the system for a long time and hide. They use a stolen login and wait for the right moment to proceed with an attack.

Predictive Defense: What Is This?

Predictive defense is not about perfect predictions of future events. It is rather the use of existing data to make estimates about the areas that might become the target of attacks in the near future. They can involve such factors as exposure data, identity risk, anomalous behaviors, vulnerability severity, threat intelligence, and the evolution of the attack surface.
This concept helps to find the answers to questions like: what assets are most exposed; what identities behave abnormally; what systems might become targets; which vulnerabilities require special attention right now. These answers will not give certainty, but will help defenders to focus their scarce time and resources on the most critical areas.
At the same time, predictive defense requires contextual information. It makes more sense when an anomaly involves a newly discovered geography, an account with elevated privileges, or an unusual device. When there is a forgotten internet-facing system, its connection to sensitive data makes the situation even worse.

Reasons Why This Shift is Occurring Now

Many factors are driving enterprises away from being responsive. Firstly, attackers are increasingly relying on automation and artificial intelligence to enhance reconnaissance, phishing, and exploitation efforts. Secondly, there are greater numbers of distributed digital spaces, where enterprises use cloud services, remote working, third-party integration, and identify-based workflows, thus increasing the attack surface. Finally, the cost of breaches is constantly growing, which makes prevention more valuable.
There are also other factors at play, such as the realization that all important threats do not always generate alerts. Firstly, some of them can remain hidden and appear as legitimate behavior. Secondly, other types of attacks involve misconfiguration, exposed services, and insecure identity management rather than malware. When enterprises respond only to the occurrence of incidents, it means that it is too late. Predictive defense allows enterprises to mitigate risks before they become problems.

Predictive Defense in Action

First of all, it starts with awareness. Companies must know their assets, understand how they are interconnected, and be aware of the existing dependencies. Otherwise, any prediction is simply an assumption. When the environment is mapped, security experts can look for any changes in behavior which may mean the increased risk.
Behavioral analysis is an example of such an approach. If a user suddenly starts using unfamiliar systems, downloading huge chunks of data, or trying to login during the strange time frame, this may be the sign of a problem. Likewise, the appearance of a new service on the Internet or delay of the important patch may mean something to consider.
Threat intelligence is another example of an input. If there is an active exploitation of some vulnerability in the wild, then the related systems deserve special attention. The same is about the targeted attack against a particular industry.
Mature solutions also integrate security into incident response planning. In the event that an attack vector is predicted, teams must be able to improve their ability to authenticate and protect against attacks prior to attack execution. Predictions are only useful if they drive immediate actions.

Human Judgment is Key

In spite of advances in data analysis and new tools, human judgment is key. Security tools can point to potential issues, but humans must make sense of what they see. What appears to be a risky behavior may simply be an accepted business exception. Or, a potential vulnerability could represent a very high risk in one situation and no risk at all in another. Predictive defense is effective when machine signals can be combined with human insights.
This is why communication is important. For security professionals to help leadership make decisions, security teams must communicate risk effectively. Otherwise, predictions will not be acted on. Predictive defense is both about helping with predictions and helping with decision making. It informs leadership of where to look before it costs them money.
There is also the risk associated with over-confidence. Predictions do not mean certainties. A team may fail to accurately predict the attack path, the level of threat, or overreact to a lot of noise. This is why predictions should be used to enhance judgement and not substitute it.

Why It Will Be Helpful For The Business

Predictive defence will bring value for the business because of its ability to reduce the cost of surprise. If companies can anticipate threats, they can ensure the uptime, minimize costs of recovery, and prevent any harm to their reputation. They also use their security budget more efficiently in terms of addressing only meaningful threats and not treating all alerts equally.
Also, it will enable company's leaders to plan strategically, since instead of asking about incidents of the previous week, they will be able to ask about the possible future exposure sources.

The Larger Picture

From reactive security to predictive defense represents more than a simple shift in defensive philosophy; it speaks to a larger paradigm change with respect to the management of risk. Relying on the threat making itself known through an incident can be a thing of the past. With the nature of modern threats, this simply won’t cut it.
In many ways, predicting defense does not eliminate the necessity of a reaction. Threats will always occur. What prediction enables, however, is the ability to intercept the attack sooner, contain it, and limit its effect.

Patch Fatigue in 2026: All Major Vendors Apple, Adobe, Citrix, and Chrome Have Issued Critical Updates

Aashi Agarwal — Thu, 09 Jul 2026 08:08:28 +0000

Patch fatigue has become an actual operational issue in 2026. In situations where major vendors such as Apple, Adobe, Citrix, and Chrome all issue critical updates at once, security teams face a very difficult decision. The issue is not so much that the systems have to be updated. The issue is prioritization, testing, and implementation without disruption of the business process.
The dilemma is obvious. If the security team does not act quickly enough, they expose themselves to risks. However, if they rush into implementation without preparation, they put themselves at risk of causing operational issues in their processes. Patch fatigue emerges when there is too much of an urgent update to handle.

Why This Happens
Contemporary software environments are large and complex. An individual organization may be reliant on endpoints, browsers, creativity suites, remote access programs, and cloud services offered by different vendors. Once several vendors roll out patches together, the backlog quickly grows.
This is known to the attackers as well. They target the systems which can be exploited most easily and updated least quickly. It means that organizations can no longer leave patching in the background. They have to adopt a process approach to patches.

How To Deal With It
Security groups should prioritize patches by exploitability, business impact, and exposure. Internet facing and identity systems should come first, followed by valuable systems within the internal environment. Security groups will also need to communicate effectively to leadership so that patching is viewed as risk mitigation, not maintenance.

The Larger Point
Patch fatigue is an inevitable outcome of complex digital environments. The solution does not lie in patching less. It lies in patching smarter, faster, and better.

Secrets Management: Why Environment Variables are Not Enough

Aashi Agarwal — Thu, 09 Jul 2026 08:04:51 +0000

Environment variables can be used to keep your secrets such as API keys, database passwords, and service tokens. These are simple and convenient to set up and use, hence the wide popularity of using them. However, convenience is not equal to security. In most cases, environment variables are simply not enough to safeguard your secrets.
The issue here is that environment variables can be leaked through logs, configurations, process listing, debugging or memory dumps. In addition, these variables could be inherited by a child process or could be inadvertently exposed by the improper use of access control mechanisms. Once leaked, the secret can easily be reused by the attacker.

The Problem With This Approach
A secret gives you access, which is why they are so dangerous. Whether a token or a password that provides access to cloud services, databases, internal applications, or external services, if it’s stored unencrypted, the organization is putting itself at risk.
Environment variables present similar challenges to management. Environment variables are difficult to rotate regularly, audit on a large scale, and are easily cloned to other environments if not properly managed.

Features of Improved Secret Management
Effective secret management entails using centralized secret storage, restricting access rights, logging, and regularly rotating the secret keys. It is important that the secrets should be accessed only by authorized services and users. This will help lower risks and improve efficiency.
There are more benefits of improved secret management. For example, it becomes easier to understand why the secret key was used and when. In case there is any misuse, it can be detected and dealt with.

Lesson to Be Learned
Environment variables can help configure your application, but they will never provide you with a full secrets management approach. True secrets management should have proper mechanisms for handling secrets that treat them like valuable resources.

ClickFix Attack Campaigns: New Social Engineering Strategy Threatening Your Network

Aashi Agarwal — Wed, 08 Jul 2026 04:39:53 +0000

The clickfix approach is a type of social engineering where attackers trick users into performing tasks that seem reasonable and legal. Rather than using content that looks harmful to a victim, the attackers create a situation that seems to be the usual part of troubleshooting, verification, or fixing the system.
It becomes possible because of the desire of people to quickly deal with the problems they face. If someone is asked to copy, paste, run or authorize anything to fix a problem, they will do it right away without any doubt.

Why ClickFix Approach Is Working
ClickFix strategy makes it difficult to separate users’ actions from the attacker’s activities. Such attacks usually look like the messages from the help center or browser errors, while users think they resolve an innocuous issue.

How To Protect Against This Technique
There needs to be education for users to take their time if an instruction sounds weird. Educate security personnel on verifying instructions via official channels and refraining from carrying out urgent corrective actions via unreliable sources. There also needs to be technical controls on what happens in case of any user error.

Lesson Learned
The ClickFix attacks show us that social engineering techniques are evolving. The solution lies beyond awareness.

Prompt Leakage: An AI Security Issue That Nobody Discusses

Aashi Agarwal — Wed, 08 Jul 2026 04:36:14 +0000

Prompt leakage is an emerging issue of AI security that usually garners insufficient attention compared to others. This involves the unintentional exposure of secret instructions, data, or inner workings of the AI system by the company itself. It could involve anything from secret prompts, confidential business context, or system instructions on how the AI is employed.
The issue is that there is an assumption that AI software tools are akin to personal assistants without realizing how the data may be stored, logged, processed, or shared without their knowledge. This represents a hidden risk of data exposure.

Why It Is Important

This could lead to the exposure of the company’s strategies, customer information, IP, or security information. Careless use of the AI software tools by the employees will result in the unintended leakage of data that should remain within the organization’s control. That is why it is important to focus equally on governance as well as the tools.

What Organizations Need to Do

Organizations require guidelines on what can and cannot be inputted in the AI model. Companies need to categorize their data, vet their vendors, and train their employees not to over-share. AI usage by companies needs to be monitored and authorized where appropriate.

The Larger Lesson

Prompt exposure is an example of the fact that the security problem associated with AI does not just end with attacks on AI models. It also concerns what gets inputted into the system.

Why Are Hackers Going After CI/CD Pipeline Systems?

Aashi Agarwal — Wed, 08 Jul 2026 04:31:59 +0000

Modern CI/CD pipelines have emerged as one of the most lucrative cyberattack targets because they exist in the heart of software delivery operations. By gaining access to the pipeline, the hackers can make modifications to code, exfiltrate credentials, distribute updates, and cause damage to numerous downstream users at the same time. This makes the pipeline much more valuable than an individual endpoint or account.
The main reason why hackers are increasingly going after CI/CD pipeline systems is because they allow for scaling attacks. Rather than compromising one individual or endpoint, the attack on the build and deployment processes will compromise entire applications, internal systems, and even customers' systems in the case of a software-driven business. This way, the CI/CD pipeline system acts as a trust mechanism.

Why are Pipelines so Valuable?

CI/CD pipelines typically have plenty of sensitive information. The latter includes access tokens, API keys, deployment tokens, source code, build scripts, and configuration files. Moreover, the CI/CD pipelines are directly connected to cloud services, repositories, testing environments, and production systems. The abundance of access options makes it a very high-impact target.
The attacker always looks for a valuable target that allows them to gain some sort of advantage. An infected pipeline will give them access beyond the traditional security measures as the malicious code will be delivered via an update process. This poses much more risk compared to malware delivered to one machine.

Common Issues

One of the most common issues in CI/CD environments is over-trust. Build systems are granted too many permissions so they can operate faster. Passwords can be re-used across multiple environments. Third-party libraries can be included without verification. Logs and artifacts can expose sensitive information that needs to be protected.
Another issue relates to the lack of visibility. While security operations might be keeping an eye on endpoints and cloud workloads, they may neglect the software delivery process itself. Without proper oversight of the pipeline, any suspicious modifications will only be noticed after the malware is distributed. Pipeline security becomes essential to software supply chain protection.

How Organizations Can Help Themselves

Pipeline protection means implementing proper access controls, secure management of secrets, code signing, validation of dependencies, and separation of duties. Build and deployment systems should be given minimum permissions necessary. Credentials should be kept safe and updated regularly. All activities within the pipeline should be logged and analyzed for signs of malicious behavior.
The main takeaway here is treating the pipeline as the core infrastructure. After all, if an attacker gains control over it, he can manipulate everything created by the pipeline.

Patch Window Within Three Days: CISA’s New Directive

Aashi Agarwal — Fri, 03 Jul 2026 07:24:16 +0000

A patch window of three days is definitely an indicator of an accelerated process of vulnerability management. CISA’s directive recognizes the harsh truth that after a vulnerability goes into public domain and is actively exploited, defenders could be left with only a limited time before an attack against the vulnerability takes place. There will be no more room for slow responses in this case.
It doesn’t necessarily mean that all patches should be applied right away in any environment. However, what it means is that certain vulnerabilities are extremely critical and cannot be waited for anymore.

The Reason Why The Timeframe Is So Short
Once an important security flaw is made public, attackers start scanning rapidly. It is now easier for them to detect vulnerable systems using automation, and exploitation usually starts soon after the disclosure. The three-day period acknowledges the fact that delay can raise the risk of compromise.
It means that companies need to improve on their asset management, prioritization, and deployment capabilities. You cannot patch your vulnerable systems unless you first know what they are. You cannot limit exposure until you first identify your critical assets.

Change Is Needed
Organizations should have pre-defined patch processes, ownership, and rollbacks. In addition, there must be a distinction between patching cycles and exploiting events. Time will play a critical role in determining whether the organization will be resilient or breached when dealing with Internet exposed assets, identity platforms, and critical infrastructures.

The Bigger Lesson
It is evident from CISA’s three-day directive that vulnerability management has become a race. Organizations that can act fast will always be better than those with longer maintenance windows.

Explaining Cisco's New Vulnerability Disclosure Approach

Aashi Agarwal — Fri, 03 Jul 2026 07:22:03 +0000

Disclosures are now a strategic rather than a technical issue. The approach by Cisco addresses the need for vendors to disclose effectively and responsibly, which means that there is the need to ensure there is clarity and speed of response in the process of managing disclosure. The aim is to be transparent but ensure that the clients are protected at all times.
This is important because disclosures have implications far beyond the release of a bulletin. It includes aspects like the importance of patches, the dangers associated with exploits, client preparedness, among others. If done inappropriately, it will only complicate things for the defender and make it easier for the attacker.

Disclosure Strategy’s Importance
Vulnerabilities are inherent in all large software environments. The question that matters is not their presence itself, but their speed of discovery and communication to the audience. A good disclosure strategy allows customers to comprehend the level of danger, the scope of affected products, measures of mitigation and time frame for the patches.
This issue is important for Cisco’s disclosure policy, since enterprises use many of its network and security products. Proper disclosure provides a chance to manage risks better, while delayed or confusing information gives an opportunity to the attackers.

What Should a Good Strategy Contain?
It should provide information that is both clear and technically sound, as well as practical advice. Such a disclosure should provide customers with the necessary information regarding the vulnerability, if it can be used for malicious purposes, any possible ways to mitigate the problem temporarily and the urgency of its solution. At the same time, organizations should get some additional information, namely, whether the vulnerability can be exploited remotely, does it require any authentication and what impact on business operations may occur.
Key Aspects of the Disclosures Strategy
A good strategy is not only a warning but also an aid. Therefore, it must simplify decision-making processes and prioritize vulnerabilities.

Lessons Learned
Today, vulnerability disclosures play an important role in defense strategy. Cisco’s strategy is a reflection of this industry tendency.

Find more resources on cybersecurity, threat intelligence, digital risk,
privacy compliance, and consent management through IntelligenceX CyberSecurity and ConsentX. IntelligenceX helps organizations identify and understand emerging cyber threats through focused digital intelligence analysis and investigations, while ConsentX empowers businesses to achieve global privacy compliance with comprehensive consent management, cookie compliance, and data privacy solutions.

Why Do Cyber Insurers Today Value Response over Prevention?

Aashi Agarwal — Fri, 03 Jul 2026 07:19:59 +0000

The criteria according to which cyber insurers assess risks are evolving. In the past, prevention was the key factor. If an enterprise had good controls, insurance providers felt safe in providing their services. These days, however, the new criterion comes into play: response. Cyber insurance providers are getting increasingly interested in knowing how quickly an enterprise can react, mitigate an incident, restore its normal functioning, communicate with relevant parties, and minimize losses once an attack starts.
This is quite natural because even a well-protected enterprise can be hacked. Phishing attacks, password thefts, misuse by insiders, supply chain infiltration, and ransomware can find a way into even a well-protected system. Therefore, insurance providers today have to start considering what happens when the protection system fails.

Why Prevention Is No Longer Enough?
Prevention is still relevant, but it is not a way to avoid risks. The attackers evolve very fast, and human mistakes cannot be avoided. The company may have good prevention methods but end up having its systems breached just due to one compromised account or one vulnerable service. Insurance companies found out that the prevention promises do not always correspond to reality.
Response is important since it defines the extent of the damage. If the company is able to isolate the systems, collect the evidence, and restore from the backups, the losses will be lower.

What Insurers Are Looking For
Increasingly, insurers are looking for incident response plans, testing of backups and recovery, access control, logging, and coordination among executives. These organizations are interested in finding out whether the organization is capable of responding effectively in those initial hours of the attack. More importantly, they expect proof of testing, not just documentation of the plans.
This puts a premium on operational preparedness. Organizations that have drilled their teams in responding to ransomware attacks, privilege escalation incidents, and other such scenarios have become more appealing to underwriters. They have shifted focus from having a good show of security on paper to actually being able to withstand an attack and come through unscathed.

The Bigger Picture
With cyber insurance shifting towards showing what happens after the disaster rather than preventing disasters altogether, response is as important as prevention.

The Next Billions Dollar Data Breach Will Be Different Than The Last Ones

Aashi Agarwal — Thu, 02 Jul 2026 08:15:20 +0000

The upcoming billion-dollar data breach is bound to be different from all the previous data breach incidents. Old data breach tales were characterized by compromised databases, compromised passwords and a single big breach incident. The upcoming one will be about identity theft, misconfigured cloud, compromises via third parties, legitimate data exfiltration, and stealthy data access for extended periods.

This will be important since most people defend against the previous kind of breach rather than the upcoming one. The attackers know this. They will use more tricks based on access, trust, and complexity than loud breaches.

How Breaches Are Evolving?
The current world is highly connected. Information flows through cloud systems, SaaS applications, APIs, endpoints, and partners’ networks. As a result, there are many ways for an attacker to gain access to data. One compromised user credential or vendor might give an attacker an opportunity to access all kinds of assets.

AI technologies are also transforming the way breaches take place because attackers get a chance to conduct reconnaissance, social engineering attacks, and abuse at a high speed. Future breaches might be the result of a combination of small events rather than one large breach itself.

What Makes This Data Breach Likely to Be So Expensive?
It will become costly as a result of its scale, its timing, and its complexity. With attackers able to stealthily navigate cloud environments and internal processes for weeks on end, they could steal even more data before being detected. Organizations that lack effective security tools and identity management will take longer to respond.

The next $1 billion data breach will certainly be expensive since it will combine data loss, operations interference, and damage to reputation. The incident in itself may turn out to be uneventful, but its consequences surely won't be.

The Big Takeaway
It’s time for companies to stop relying on the idea that the next attack will look like the previous one. They have to think about identity, cloud security, third-party risk management, and quick containment. The greatest damage will come from those attacks that target the digital supply chain of today.