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20/08/2026 5:28 pm
TL;DR: Most DLP failures come from overloading enforcement with discovery and classification, which drives false positives, user friction, and blind spots across SaaS, cloud, and AI, according to Sentra. The practical answer is to pair DSPM-driven classification and labels with DLP policy enforcement, so control decisions key off context rather than brittle pattern matching.
NHIMG editorial — based on content published by Sentra: Supercharge Your DLP by combining DLP and DSPM
Questions worth separating out
Q: How should security teams reduce false positives in DLP without weakening protection?
A: Start by separating content matches from business context.
Q: Why do DLP programmes fail when they rely on pattern matching alone?
A: Pattern matching cannot reliably separate genuine sensitive content from lookalike text, especially in SaaS, cloud, and AI workflows.
Q: What breaks when DSPM and DLP are treated as separate projects?
A: You get accurate discovery without usable enforcement, or enforcement without trustworthy classification.
Practitioner guidance
- Define one measurable DLP objective Set a 90-day target such as reducing false positives by 50 percent or eliminating unknown PHI exposure in specified platforms, then measure against that outcome rather than against generic compliance language.
- Move classification ahead of enforcement Use DSPM to discover and label sensitive data across cloud, SaaS, warehouses, and AI pipelines before tightening DLP rules, so policies key off trusted labels rather than brittle content matching.
- Rewrite policies around labels and context Block or quarantine based on labels such as PCI, PHI, or Highly Confidential, then layer in identity, destination, time, and channel so the same policy adapts across email, collaboration, and SaaS.
What's in the full article
Sentra's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step guidance for pairing DSPM with endpoint, cloud, and collaboration DLP controls.
- Specific examples of label-driven policy design for regulated data, confidential files, and AI workflows.
- Practical rollout guidance for reducing false positives without disabling protection.
- Examples of how to tune DLP feedback loops with business and compliance stakeholders.
👉 Read Sentra's guide to making DSPM and DLP work together →
DLP and DSPM together: can labels finally fix false positives?
Explore further
20/08/2026 6:16 pm
DLP without data intelligence is a governance anti-pattern. The article is right to separate discovery and enforcement because one control cannot reliably infer sensitivity, business context, and actionability at the same time. In practice, this is the same failure mode that appears when identity systems try to make access decisions without trustworthy asset labels. Practitioners should treat DLP as an enforcement layer fed by better classification, not as the source of truth.
A question worth separating out:
Q: How should organisations govern access to data used by AI systems?
A: Treat AI data access as an identity governance problem, not just a data storage problem. Define who or what can use each dataset, what purpose is allowed, and what runtime restrictions apply. Then review humans, service accounts, and AI agents separately so entitlement scope matches actual behaviour rather than a generic AI policy.
👉 Read our full editorial: Supercharging DLP with DSPM: why context beats noisy rules
