Topic starter
02/08/2026 12:02 pm
TL;DR: The gap is cohesion: most organisations already have DSPM and DLP capabilities, but risk insights stay in dashboards while enforcement lives elsewhere, creating delays when data moves across cloud, endpoint, SaaS, and AI environments, according to Cyberhaven. That fragmentation now matters more because data is increasingly mobile and GenAI usage is expanding.
Editorial analysis by NHI Mgmt Group, based on content published by Cyberhaven: “Redefining Data Security: From Insight to Action”.
By the numbers:
- 62% of organisations are experimenting with AI agents, while 82% of the top 100 most-used GenAI SaaS applications are medium, high, or critical risk.
- 40% of data breaches involve data spread across multiple environments, including public clouds, private clouds, and on-premises infrastructure.
- By 2026, over 20% of businesses will prioritize DSPM technologies to discover and secure their known and unknown data repositories.
Key questions
Q: What breaks when DSPM and DLP are not connected in practice?
A: When DSPM and DLP are not connected, teams get a visibility-only view of sensitive data and a separate enforcement layer that cannot act with enough context.
Q: Why does data movement across environments make unified controls more important?
A: Because every hop strips away some context.
Q: What are the signs that data security workflows are too fragmented?
A: Common signs include risk dashboards that do not trigger action, policies that behave differently across endpoints and SaaS, and analysts who can identify exposure but cannot stop the transfer in time.
Practitioner guidance
- Map data state transitions end to end Inventory where sensitive data is created, classified, shared, stored, and moved so policy can follow the data across environments.
- Unify detection and enforcement workflows Make sure discovery findings can trigger response actions in the same operating path, rather than waiting for separate review and manual handoff.
- Use lineage to drive policy decisions Base transfer controls on a file’s movement history, destination, and previous access path, not only on its current classification label.
Bottom line: The central problem is not the absence of DSPM or DLP, but the operational gap created when discovery and enforcement are split across tools and teams.
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02/08/2026 1:08 pm
Cohesion, not coverage, is the real data security failure: most enterprises do not lack tools, they lack a control plane that makes discovery and enforcement behave like one system. DSPM tells teams what exists, while DLP tries to stop movement, but separated telemetry and policy execution create a governance gap. The practical conclusion is that data security programmes should be assessed on operational cohesion, not tool count.
A few things that frame the scale:
- 28% of secrets incidents now originate outside code repositories, in Slack, Jira, and Confluence, and are 13% more likely to be categorised as critical than code-based leaks, according to the State of Secrets Sprawl 2026.
- Business leaders plan to spend $124 million on average on AI in 2026, and 91% say data security and risk will shape their AI strategy.
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: Unified data security is exposing the DSPM and DLP gap