Key Takeaways
- Continuous, real-time visibility across datastores eliminates security blind spots.
- Prioritize contextual classification over legacy pattern matching.
- Correlate metadata elements to compounding risks rather than relying on volume-based alerts.
- Govern Data & AI pipelines to reduce attack surface and AI efficacy.
Your security team can list the data stores in your environment. But can it list them all? The gap between the data you have inventoried—especially sensitive data—and the data that actually exists across the estate is precisely why the Gartner-introduced data security category, Data Security Posture Management (DSPM), exists. In fact, 34% of organizations know the location of their data, and only 39% can classify it, according to Thales Cybersecurity's Data Threat Report 2026.
And as the volume and distribution of data continue to grow, the attack surface around them grows as well. More data doesn't just mean higher storage costs; it also means more malicious actors going after it. To add to data security leaders' concerns, fewer gaps are being closed. The Cloud Security Alliance reports that non-human identities outnumber human users by 45 to 1 on average.
The problem becomes even harder as data outgrows the traditional, governed environments. Shadow data, such as unmanaged duplicate files, production data in dev environments, database snapshots, or over-retained old files in some cloud buckets, has always been there. But recently, it has become larger and noisier than ever, largely because of AI. Not only is more data being fed into LLMs, but the AI tools are producing new content at machine speed.
This post discusses the top 5 DSPM best practices that enterprises should focus on to maintain continued visibility, enable precise contextual classification and labeling, and prioritize risk remediation across their data and AI estate.
Top 5 DSPM Best Practices
1. Widen Data Discovery Coverage Across Diverse Data Environments
Consider a DSPM solution that doesn't fall short on coverage. A robust solution should offer extensive data discovery, covering not only the environments you are most confident in but also those you ignore. The coverage should extend to all the public clouds, data clouds, hybrid clouds (GCP, AWS, or Azure), SaaS environments (Slack, Jira, Salesforce, Workday), streaming data systems, AI tools (Vertex AI, Amazon Bedrock, or Mistral), and on-premises data stores. The wider the net, the better the visibility into the estate and the associated risks.
Another imperative to consider is the frequency of scans. Sensitive data discovery shouldn't be a one-time project. On the contrary, it should be continuous. Any change across the data estate, such as the addition of a new data store containing sensitive data or a change to permissions, must be detected as soon as it occurs. This reduces the chances of any potential risk going unnoticed.
2. Prioritize Context-Aware Data Classification
Classification engines that heavily rely on predefined patterns or regex tend to flood teams with high false positives. For instance, a simple regex that detects a nine-digit Social Security Number (SSN) is very likely to treat the same pattern in any transaction IDs as sensitive and consequently flag it as a security risk.
Modern DSPM solutions must prioritize context-aware AI-powered classification that provides better business insight into the data, such as who owns it, which regulatory framework applies to it, or whether it should really matter or is redundant. AI-powered classification goes beyond simple pattern matching by leveraging AI/ML algorithms and customizable mechanisms to extend detection capabilities.
Modern classification engines should adopt AI tuning capabilities to improve classification accuracy without requiring continuous re-scanning. AI-powered classification tuning uses the classification results feedback, such as false positives and false negatives, to continuously improve the accuracy.
3. Prioritize Risks by Correlation, Not by Alert Counts
Individual risk scores do not matter much, as they just lead to alert fatigue for remediation teams. In fact, Google’s Threat Intelligence Benchmark report highlights that 82% of IT and cybersecurity leaders fear missing potential security threats due to the high volume of alerts and data. The challenge becomes even more overwhelming when the alerts mostly turn out to be false positives. The SANS Detection Engineering Survey reports that high false-positive rates (64%) are among the common technology challenges with third-party detection tools.
By scoring risks based on a combination of data sensitivity, access controls, exposure paths, machine identities, infrastructure, AI systems, and regulatory requirements, DSPM tools generate compound risk alerts, enabling better signals, faster response, and lower operational burden. Modern DSPM solutions collect rich metadata to create a comprehensive Knowledge Graph that gives a full scope of the risks that matter most. As a result, security teams effectively prioritize the most critical threats that demand immediate attention.
4. Extend Data Security Posture to AI Pipelines & Workflows
The security posture of any enterprise must cover both data and AI workflows, as data and AI security aren’t separate projects. When an employee feeds sensitive data into an unsanctioned AI (Shadow AI) tool, the enterprise loses control of its data, and it falls under the control of an external entity. IBM highlights that in its Cost of a Data Breach Report 2026, citing Shadow AI as one of the common themes appearing in 43% of security breaches. Another report highlights that seven in ten breached organizations lacked a governance policy for AI workflows.
The challenge of governing AI-associated risks isn’t limited to employees knowingly or inadvertently leaking sensitive data to LLMs. In fact, with the rise of AI agents, the risk has skyrocketed as these agents access data in ways that circumvent legacy security controls. In fact, the latest surveys found that only two in five organizations enforce data and AI access control policies.
Modern DSPM solutions must provide AI intelligence on top of contextual intelligence to strengthen their knowledge graph. A complete inventory of an enterprise’s AI tools, mapped to data systems, sensitive data flows, access policies, and regulatory guidelines, provides crucial insights into which data flows into which AI systems, or which AI agents are accessing data they shouldn’t. Moreover, with effective labeling policies, enterprises can further limit their AI tools, such as Copilot and Gemini, from accessing sensitive data.
5. Enforce Data Minimization to Remove Data You Don’t Need
Deleting data that you don’t need is not only the cheapest data security control but also the most underused capability of DSPM. Duplicate data in production environments, obsolete data sitting quietly in expired backups, or trivial data imported due to a merger two years ago, every instance of ROT data is a ticking bomb that not only affects storage cost but also regulatory fines and increased breach likelihood. In fact, ROT data can undermine the efficacy of AI tools, leading to flawed GenAI output due to outdated or irrelevant data.
DSPM tools are ideal for extensive visibility into redundant, obsolete, and trivial data. Modern solutions can effectively centralize data inventory across environments, flag obsolete data based on metadata attributes such as age and activity, leverage AI to detect duplicate data, and create custom data minimization policies.
The result: a reduced risk surface, minimized storage costs, and an improved compliance posture.
Conclusion
DSPM is not a vanity tool but a necessary component of today’s data security stack. The best practices discussed above should give enterprises a head start, enabling them to gain insights into sensitive data and AI visibility, enhanced risk detection and remediation, and continuous regulatory compliance.
Request a demo to see how Securiti transforms data and AI risks into your source of competitive advantage with the Leading DSPM solution.
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