Topic starter
02/08/2026 9:45 am
TL;DR: The NIST AI Risk Management Framework gives organisations a governance structure for trustworthy AI, but for agentic and LLM applications it still leaves key technical gaps around tool permissions, prompt injection, and runtime control, according to Akto’s guide. The practical issue is that AI security now depends on continuous discovery, measurement, and enforcement, not policy alone.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “NIST AI Security Framework: Practical Guide for LLM Apps”.
Key questions
Q: What fails first when the NIST AI RMF is used without technical controls for agentic AI?
A: The first failure is usually the gap between governance intent and runtime behaviour.
Q: Why do agentic AI systems create greater operational risk than standalone LLMs?
A: Agentic systems create greater operational risk because a successful attack can translate into an executed action, not just a harmful response.
Q: What are the signs that AI agent governance is too weak for production use?
A: Weak governance usually shows up as poor visibility into what agents can access, incomplete audit trails, and inconsistent oversight across security, legal, compliance, and operations teams.
Practitioner guidance
- Define AI agent identity boundaries Assign every agent a unique, bounded identity and document which tools, connectors, and data sets that identity may reach.
- Operationalise continuous agent discovery Track agents, LLM applications, and MCP server connections automatically so the inventory reflects live reality rather than self-reported spreadsheets.
- Test for prompt injection and excessive agency Run repeatable red team exercises that probe instruction hijacking, tool misuse, and over-permissioned behaviour in production-like conditions.
Bottom line: The central risk is not the AI RMF itself, but organisations using it as a governance document without the runtime controls needed to constrain agentic behaviour.
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02/08/2026 12:38 pm
Agentic AI governance is becoming an identity problem before it is a model problem. The NIST AI RMF is useful because it creates an organisational language for risk, but agentic systems fail in the gaps between policy and runtime authority. Once tools, connectors, and delegated credentials enter the picture, identity lifecycle discipline becomes part of AI governance, not a separate workstream. Practitioners should treat every agent as a governed non-human actor with a bounded identity and measurable authority.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: How should security teams respond when an agent gains more tool access than its task requires?
A: They should treat that as an authority problem, not just a configuration issue. Contain the agent’s reach, revoke or reduce the delegated permissions, and revalidate the workflow before it is allowed to continue. The important question is whether the task still needs the access it was given.
👉 Read our full editorial: NIST AI RMF for agentic systems: what practitioners need to operationalize