UADP and AI agent governance: what does data-first security change?

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 →

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