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22/06/2026 10:16 am
TL;DR: Agentic AI exposes a gap in IAM-era authorization because an agent can act within its permissions while still pursuing an unsafe purpose, according to Zenity. The critical failure is that identity can confirm what an agent may do, but not whether its runtime intent is aligned with the sanctioned objective.
NHIMG editorial — based on content published by Zenity: Beyond Authorization, Why Intent-Aware Detection Is the New Control Plane for Agentic AI
Questions worth separating out
Q: How should security teams govern AI agents that can act within permission but outside purpose?
A: Teams should govern AI agents with both entitlement controls and runtime intent checks.
Q: Why do traditional IAM controls fall short for agentic AI?
A: Traditional IAM controls were built for principals whose behaviour is relatively bounded and predictable.
Q: What do security teams get wrong about prompt filtering for AI agents?
A: They treat prompt filtering as if it were full AI security.
Practitioner guidance
- Inventory every agent by sanctioned purpose Write a single sentence for each agent that states its approved objective, then map tools, datasets, and escalation paths to that purpose.
- Add runtime intent checks to existing detection flows Compare input intent against observed tool sequences, memory access, and output destinations so that benign-looking prompts do not mask harmful execution.
- Separate pre-production baselines from live monitoring Use sandboxed replay and behavioural profiling to build reference patterns, then reserve runtime controls for deviations that matter operationally.
What's in the full article
Zenity's full analysis covers the operational detail this post intentionally leaves for the source:
- The vendor's breakdown of the five intent-evaluation approaches and where each one breaks in production
- Examples of how encoder-only classifiers, sandboxing, and anomaly detection are applied to agent telemetry
- The article's discussion of real-world agent incidents and the taxonomy work behind the detection models
- The practical trade-offs between inline blocking, triage scoring, and behavioural baselining
👉 Read Zenity's analysis of why intent-aware detection is the new control plane for AI agents →
AI agent intent detection: are IAM controls keeping up?
Explore further
22/06/2026 11:06 am
Authorization is no longer a sufficient control plane for agentic AI. IAM was designed to answer whether a principal could perform an action, not whether the action sequence served an approved purpose. That assumption holds for fixed service accounts and human jobs, but it fails when an agent can reason, chain tools, and redirect execution mid-session. The implication is not that authorization disappears, but that it stops being the decisive control for agentic risk.
A few things that frame the scale:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
- Only 5.7% of organisations have full visibility into their service accounts, which means most identity programmes cannot reliably see machine access sprawl.
A question worth separating out:
Q: How can organisations tell whether AI agent intent detection is working?
A: Intent detection is working when it can separate sanctioned behaviour from behaviour that looks permitted but serves an unsafe objective. Useful indicators include fewer false negatives on multi-step abuse, clearer alerts on unexpected tool chains, and a lower rate of agent actions that cross into unapproved data domains.
👉 Read our full editorial: Intent-aware detection is becoming the control plane for AI agents
