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03/06/2026 5:15 pm
TL;DR: AI security has moved past deterministic controls, with identity, data, and intent needing to be correlated as agents act at machine speed, according to Cyera. The governance break is that traditional IAM and DLP assumptions were built for predictable software, not autonomous execution paths that can change behaviour mid-session.
NHIMG editorial — based on content published by Cyera: SACR names Cyera an innovator in the 2026 UADP Technoscope
Questions worth separating out
Q: How should security teams govern AI agents that can change actions at runtime?
A: Security teams should govern runtime AI by correlating identity, data, and intent before trusting an action path.
Q: Why do traditional IAM and DLP controls fail for autonomous AI systems?
A: Traditional IAM and DLP controls fail because they assume predictable workflows and stable access patterns.
Q: What do security teams get wrong about AI-driven insider risk?
A: They often treat insider risk as a matter of user intent alone.
Practitioner guidance
- Map agentic workflows to identity, data, and intent signals Identify which AI workflows require all three signals before policy decisions can be trusted.
- Review controls that assume deterministic software Inventory firewalls, CASBs, and static DLP rules to find where they depend on fixed execution paths.
- Separate synthetic insider risk from ordinary misuse Treat a legitimate agent manipulated into exfiltration as a distinct governance case.
The article’s framing points toward a future where runtime context matters more than provisioning logic, especially for sensitive data and privileged workflows?
👉 Read Cyera's Technoscope analysis of unified agentic defence platforms →
Explore further
03/06/2026 5:19 pm
Data-centric security is becoming the operating assumption for agentic governance. The article’s central claim is that AI security cannot be reduced to perimeter controls or isolated model protection because agents move through data, identity, and intent in one runtime loop. That means data is not a downstream asset to protect after identity is sorted. It is the control surface that makes the rest of the governance model intelligible. Practitioners should treat that as a change in architecture, not a tooling preference.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI, even as 53% expect AI to run major portions of their infrastructure autonomously within three years.
A question worth separating out:
Q: How can organisations tell whether their AI security model is actually working?
A: They should test whether the control stack can explain who acted, what data was touched, and what purpose the action served. If those three signals cannot be correlated in one incident view, the model is likely monitoring access without governing behaviour. That is a visibility gap, not a complete AI security posture.
👉 Read our full editorial: Cyera’s UADP framing shows data-first AI security gaps
