AI agent runtime drift: are your controls keeping up?

TL;DR: Enterprises often deploy AI agents with policies that look sound on paper but are not enforced in live workflows, allowing permission drift, unsanctioned API use and downstream system access to accumulate unnoticed, according to Singul AI research. The governance problem is no longer documentation quality; it is runtime control over delegated AI behaviour.

Editorial analysis by NHI Mgmt Group, based on content published by Singul AI: “Singulr AI Named in Two Gartner® Reports, Recognized Across AI Agent Supervision and Real-Time AI Governance”.

Key questions

Q: What breaks when AI agents are approved only once at deployment?

A: Point-in-time approval breaks when an agent’s capabilities, integrations, or data access change after review.

Q: Why do AI agents increase identity and access risk in SOC workflows?

A: Because they can chain multiple calls, reuse context, and reach across telemetry sources faster than a human review cycle.

Q: How do security teams know if AI governance is working?

A: Look for evidence that access decisions are reviewable, permissions are revocable, and exceptions are not becoming permanent.

Practitioner guidance

• Define agent permissions as task-scoped entitlements Map each AI agent to a limited set of actions, resources and data paths before it is allowed into production workflows.
• Enforce policy at execution time Require every sensitive agent action to pass a live authorisation check so permissions cannot drift silently after deployment.
• Track control drift across live workflows Measure when an agent begins calling new tools, touching new datasets or reaching new downstream systems outside its approved pattern.

Bottom line: AI governance fails when agent permissions are treated as static artefacts instead of live control decisions.

Explore further

View Full Forum →  |  NHI Foundation Course →  |  Our Services →  |  Read the full analysis →