Topic starter
02/08/2026 11:54 am
TL;DR: AI systems now face prompt injection, data poisoning, leakage, and autonomous tool abuse, and Akto’s guide argues that traditional IT risk methods are too static for agentic environments. The real shift is that AI governance now depends on continuous monitoring, adversarial testing, and identity-aware access control, not just pre-launch review.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Risk Management Framework: Components, Risks and Implementation Guide”.
Key questions
Q: What breaks when an AI agent is given real user sessions and tool access?
A: The main failure is that a single injected instruction can inherit the user's active permissions and convert a reading task into exfiltration, command execution, or message sending.
Q: Why do AI agents require different risk controls than static applications?
A: AI agents can make decisions in real time, follow instructions embedded in prompts, and call tools without a fixed path through the system.
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 inventory and ownership Catalogue every AI system, including LLMs, RAG pipelines, AI agents, and AI-powered features.
- Scope delegated access for every agent Treat each agent as a non-human identity with a narrow purpose and limited tool permissions.
- Build continuous adversarial testing into operations Test prompt injection, data poisoning, leakage, and harmful tool execution on an ongoing basis.
Bottom line: Traditional AI risk programmes fall short when agents can decide, call tools, and access data at runtime.
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02/08/2026 1:03 pm
Identity-aware AI risk management is now a governance requirement, not a specialist enhancement. Once agents can act, access, and delegate, the risk domain shifts from model behaviour alone to the control of non-human authority. That makes identity, privilege, and runtime access part of the same governance conversation as red teaming and model monitoring. Practitioners should treat agent identity as a first-class design and audit concern.
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 organisations govern AI agents that need database access?
A: Organisations should give AI agents narrow read access by default, separate write privileges from query privileges, and require human approval for any session that can modify data. That keeps agent activity inside a clearly bounded role while preserving auditability and reducing the chance of unsupervised state change.
👉 Read our full editorial: AI risk management now needs identity-aware controls for agentic systems