Data breaches are rapidly increasing, creating an uncomfortable reality for organisations. They cannot efficiently mitigate breaches without visibility into their data. An organisation can discover and classify these risks early on through a comprehensive Data Discovery Management program, which can help to comply with regulations and avoid future liabilities.
For IT and security leaders, a data discovery management programme is essential; it is no longer optional.
The Rising Risk of Shadow Data in Modern Enterprises
Data sprawl has increased dramatically due to digitisation, remote work and the adoption of multiple cloud platforms. Files, logs, backups, and datasets are stored on email, collaboration platforms, unmanaged endpoints, and in development environments, and a large portion of these documents lack policies or procedures.
The increasing number of shadow data breaches creates several different risks:
1) Storing sensitive data in unsanctioned SaaS applications.
2) Leaving PII/PHI in temporary storage, staging environments and shadow IT resources.
3) Duplicating datasets across multi-clouds.
4) Having orphan files with outdated permissions.
Regulatory bodies do not distinguish between willful negligence and lack of visibility. If your organisation maintains sensitive data, you are responsible for its security, whether or not you are aware of it.
Why Automated Data Discovery Tools Are Now Business-Critical
Enterprise-level manual discovery efforts have proven unsuccessful due to the exponential growth of both structured and unstructured data across platforms and environments. AI-based automated discovery tools can provide security teams with the visibility they require.
Through the use of advanced discovery engines, it is possible to:
• Scan various file systems, endpoints, email servers, cloud buckets and databases.
• Identify sensitive data patterns - PII, financial data, credentials, and health records.
• Find misconfigurations, open access and violations of Policy.
• Track data movement and identify high-risk propagation paths.
Other platforms, such as Seqrite, created by Quick Heal Technologies, leverage resources from Seqrite labs (intelligence, behavioural analysis, and threat Intelligence) to supplement this automation. These additional resources increase accuracy, reduce false positives, and speed up the time to resolve issues.
Building a Strong Data Classification Framework
Just discovering the data is not enough. Companies need to understand what they have and its significance. Data classification provides the framework to determine what should be prioritised, how to govern access, and how to align with compliance.
An effective classification model often consists of:
(1) Sensitivity Levels
- Public
- Internal
- Confidential
- Restricted (2) Data Categories
- Personally Identifiable Information (PII)
- Financial and transactional data
- Intellectual property, source code and research material
- Customer, vendor and employee information (3) Mapping to the Business Context
- Connects data sets to business processes, risk owners and regulatory entities (i.e., Digital Personal Data Protection Act (DPDPA), HIPAA and PCI-DSS.) (4) Automation-First Strategy
- Use of tools to automate the classification process as the data is created, shared, or moved, therefore minimising human error while maintaining compliance through continuous and consistent application of the classification model.
Having a well-documented classification framework enables companies to manage and allocate their resources in the areas of greatest risk exposure.
Designing a Proactive Remediation Plan
After you identify and categorise your data, you must mitigate vulnerabilities quickly and consistently.
The key actions for remediating vulnerabilities include:
Encrypting sensitive datasets
Restricting user access to data by role and using a Zero Trust approach
Removing duplicate, redundant, and unnecessary data (ROT)
Applying retention policies that comply with relevant laws and regulations
Masking or tokenising sensitive fields for use in testing/analytics
Consistently monitoring for violations of policies, excessive access permissions, and data drift.
The security team will get the greatest benefit (and the most significant risk reduction) from discovery, classification, and remediation operating as a single process (workflow) rather than three separate processes (silos).
Strengthening Audit Readiness with Continuous Data Governance
Now, regulatory audits are no longer just performed each calendar year - they are being done continuously. They are subject to heightened scrutiny regarding documentation and evidence, as well as clear evidence of your ability to control.
Data Discovery Management has a critical role in making sure you are ready for an audit by providing:
Continuous visibility into where your sensitive data resides
The ability to report your compliance with evidence-based reporting
Automated logs of the activities occurring with your data
Continuous enforcement of your policies
The ability to respond quickly to a regulator's inquiry
As more and more scrutiny is being placed on companies by the DPDPA Act in India and other global privacy laws, the ability to say at any time, "I am ready for an audit," has now become the new competitive advantage - and no longer simply a requirement for compliance.
Conclusion: Protect Yourself Before a Regulator Finds Your Gaps
Enterprises that invest in data discovery management will reduce the size of their attack surface, improve their governance, and reduce their risk of non-compliance. The rise in data volume requires automated discovery, strong classification, and intelligent remediation as the building blocks of the future of cybersecurity.
To have a proactive regulator-proof data protection framework become the new standard for your organisation, now is the time to strengthen your understanding of data discovery and classification capabilities.
To learn how we can help your enterprise identify, protect and govern your sensitive data across a complex IT landscape with our advanced data security solutions, please contact us. Together, we will build a foundation for a future-proof data protection framework.
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