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07/06/2026 8:48 pm
TL;DR: Gartner’s 2025 Market Guide for Data Security Posture Management says DSPM helps organizations discover, classify, and catalog sensitive data across environments, with AI data visibility and operationalization emerging as the hardest problems, according to Cyera. The governance gap is no longer just data location, but proving who and what can reach it as AI and non-human access expand.
NHIMG editorial — based on content published by Cyera: 2025 Gartner® Market Guide for Data Security Posture Management
Questions worth separating out
Q: How should security teams turn DSPM findings into real risk reduction?
A: Treat DSPM as a workflow into access reduction, not as a reporting layer.
Q: Why do AI systems make DSPM harder to operationalise?
A: AI systems move sensitive data through training, retrieval, and prompt workflows that cross multiple identity boundaries.
Q: What do security teams get wrong about data discovery programs?
A: They often assume discovery alone reduces risk.
Practitioner guidance
- Map data exposure to identity paths Tie DSPM findings to the service accounts, workload identities, and human roles that can reach each sensitive dataset.
- Create remediation owners for every high-risk finding Assign access, policy, or retention owners before publishing DSPM results so each finding has a defined path to reduction.
- Extend classification into AI workflows Include training inputs, retrieval layers, prompt logs, and vector stores in the classification scope.
What's in the full report
Cyera's full article covers the operational detail this post intentionally leaves for the source:
- Gartner Market Guide context on DSPM capability areas and category expectations.
- Cyera's summary of implementation challenges when turning data discovery into sustained governance.
- Representative-vendor positioning and market framing around DSPM adoption.
- Practical guidance for selecting and implementing DSPM in AI-heavy environments.
👉 Read Cyera's analysis of the 2025 Gartner Market Guide for DSPM →
DSPM for AI data governance: what IAM teams need to know?
Explore further
08/06/2026 8:30 am
DSPM has become an identity governance problem as much as a data discovery problem. Once sensitive data is spread across cloud, SaaS, and AI workflows, the real question is no longer only where the data sits. It is which identities can reach it, copy it, train on it, or move it into downstream systems. That makes DSPM findings incomplete unless IAM and NHI controls are part of the response path.
A few things that frame the scale:
- The average organisation believes more than 1 in 5 of their non-human identities are insufficiently secured, according to The 2024 ESG Report: Managing Non-Human Identities.
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, with 46% confirmed and 26% suspected, according to The 2024 ESG Report: Managing Non-Human Identities.
A question worth separating out:
Q: How do organisations know if DSPM is actually working?
A: Measure whether exposure is falling, not just whether inventory is growing. Good signals include fewer datasets with broad access, shorter remediation cycles for high-risk findings, and fewer identities with standing reach to sensitive data. If those measures do not improve, the programme is not changing behaviour.
👉 Read our full editorial: DSPM visibility for AI data is now an identity governance issue
