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06/09/2026 5:18 pm
TL;DR: Runtime AI detection monitors agent actions as they happen to limit data leaks, privilege escalation, and unauthorised system changes, according to LEVO. Because agents operate across APIs, databases, and workflows with dynamic decision-making, access review processes assume a stable actor where the runtime window is already gone.
NHIMG editorial — based on content published by LEVO: Runtime AI detection for autonomous agents
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
Questions worth separating out
Q: What breaks when AI agents are given broad standing access?
A: Broad standing access breaks governance because the agent can move from one task to another without a fresh authorization check.
Q: Why do AI agents need runtime controls instead of only pre-approved access?
A: Pre-approved access cannot tell you what the agent will do once prompts, tools, memory, and sub-agents start interacting.
Q: How can organisations tell whether an AI agent is acting outside its intended scope?
A: Organisations should look for behaviour that crosses expected tool boundaries, generates unusual credentials, or chains actions across systems that are not part of the original task.
Practitioner guidance
- Define runtime boundaries for every agent Inventory the exact databases, APIs, SaaS tools, and workflow actions each AI agent can invoke during execution, then compare that list to the business task it is actually meant to perform.
- Replace broad tokens with task-scoped credentials Issue short-lived credentials tied to a single workflow step or task, and remove any default write permissions that are not required for the agent’s immediate function.
- Monitor behavioural drift across sessions Flag new tool selection, unexpected persistence, spawned sub-agents, and cross-system calls that expand the agent’s effective privilege beyond the initial approval.
What's in the full article
LEVO's full blog covers the operational detail this post intentionally leaves for the source:
- Concrete examples of runtime guardrails for read-only versus write-enabled agent workflows
- Detailed breakdowns of how delegated tokens, workload identities, and MCP interfaces are enforced behind the scenes
- Use-case examples for data exfiltration, scope creep, and persistent agent behaviour in production
- Operational detection patterns for behavioural drift, sub-agent inheritance, and cross-system action chains
👉 Read LEVO's analysis of runtime AI detection for autonomous agents →
AI agent runtime detection: are your controls keeping up?
Explore further
06/09/2026 5:19 pm
Runtime AI detection is really NHI governance with a faster clock. The article is not describing a new class of security problem so much as a compressed execution window in which identity, access, and data controls must still work. For IAM and NHI teams, the issue is whether permissions are enforced at the moment of action, not whether they were issued correctly at onboarding. The practitioner conclusion is that runtime has become the decisive control plane.
A few things that frame the scale:
- The average organisation believes more than 1 in 5 of their non-human identities are insufficiently secured, according to The 2024 ESG Report: Managing Non-Human Identities.
- Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, which shows that compromise is often repeated rather than isolated.
A question worth separating out:
Q: Should organisations treat AI agent detection as an IAM, PAM, or monitoring problem?
A: They should treat it as all three, but with runtime identity as the anchor. IAM defines the grant, PAM constrains high-risk actions, and monitoring verifies behaviour as it happens. The right model is not a choice between disciplines. It is a single control loop that can stop unsafe execution before completion.
👉 Read our full editorial: Runtime AI agent detection is now an identity control problem
