Topic starter
16/04/2026 6:41 pm
TL;DR: Traditional security models do not fit systems where prompts change behaviour, identities are non-human, and autonomous agents act with real permissions, according to Wing Security, which is why OWASP’s GenAI Security Project now spans LLM and agentic application risks, governance checklists, and an AIBOM concept. Existing IAM and AppSec assumptions break when execution is probabilistic and identity becomes an active control surface.
Editorial analysis by NHI Mgmt Group, based on content published by Wing Security: “What Every CISO Should Know About OWASP for AI Security”.
Key questions
Q: How should security teams manage permissions for AI agents?
A: Security teams should regularly assess and update the permissions granted to AI agents to ensure they align with their intended scope.
Q: Why do autonomous AI agents create more access risk than task bots?
A: Autonomous AI agents create more access risk because they can combine persistence, reactivity, and initiative.
Q: What are the signs that an AI agent may be running out of control?
A: Look for unusual consumption patterns rather than a single spike.
Practitioner guidance
- Map agent permissions to an active identity inventory Identify every AI agent, delegated service account, token, and API key that can initiate actions across systems, then assign ownership and purpose for each one.
- Treat prompts and tool access as one control surface Review where prompt injection could alter downstream actions, especially when the agent can call internal tools, modify records, or trigger workflows.
- Separate human approval from autonomous execution paths Define which actions require review, which can execute automatically, and which must never be reachable by an AI agent regardless of confidence or context.
Bottom line: AI security work is now an identity governance problem because autonomous systems can hold delegated access and act in ways human IAM was never designed to supervise.
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This topic was modified 4 days ago by NHI Mgmt Group
08/10/2026 4:15 pm
Prompt injection is now an identity issue, not only an application issue: when inputs can change model behaviour, the old separation between data and logic stops holding. That breaks a core assumption behind classic AppSec review, because the control surface now includes what the system reads and how it reasons about it. The practitioner implication is that AI governance must treat input handling as part of authorisation, not as a purely content-filtering problem.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should IAM teams govern humans, NHIs, and AI agents in one programme?
A: Start by separating the control assumptions for each actor type. Humans need authentication and access review, NHIs need lifecycle control and secret governance, and AI agents need runtime scope enforcement because their tool use can change during execution. One programme can cover all three, but the policy model cannot treat them as interchangeable identities.
👉 Read our full editorial: OWASP for AI security shows why agentic identity controls must evolve