Topic starter
25/06/2026 1:26 am
TL;DR: Agentic AI systems can call APIs, query databases, and modify production systems without human approval, creating eight risks that traditional tools often miss because the dangerous action happens inside authorized workflows, according to WitnessAI. The control problem is not just visibility but assumption collapse: review-based governance cannot reliably contain actors that decide, act, and chain tools in one session.
Editorial analysis by NHI Mgmt Group, based on content published by WitnessAI: “8 cybersecurity risks of agentic AI”.
By the numbers:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
Key questions
Q: What breaks when agentic AI is governed like a normal application account?
A: Security controls break down because agentic systems do not behave like fixed-function applications.
Q: Why do authorised agent actions still create security risk?
A: Because authorization alone does not prove the action was appropriate.
Q: How do security teams know if agentic AI controls are failing?
A: The main signs are session drift, repeated retry loops, unauthorized tool calls, and behaviour that diverges from the documented task sequence.
Practitioner guidance
- Define the agent execution boundary Map the exact point where a prompt, retrieved document, tool result, or memory item can become an executable action, and require policy inspection before that transition occurs.
- Separate task access from irreversible access Grant agents only the minimum tool scope needed for the task, and isolate destructive or high-impact functions behind a different approval path.
- Trace every agent action to a human origin Make sure logs preserve which person initiated the session, which agent acted, which tools were used, and what output or side effect followed.
Bottom line: Agentic AI changes the identity problem because the system itself can choose tools and complete actions without a human checkpoint.
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25/06/2026 10:51 am
Agentic AI creates an assumption collapse, not just a new attack surface. Traditional identity governance assumes that access can be observed, reviewed, and certified after the fact. That assumption fails when the actor can decide, call tools, and complete the work inside a single approval-free session. The implication is that runtime authority, not retrospective review, becomes the control boundary.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: When should organisations require human approval for an AI agent action?
A: Require human approval when the action could change infrastructure, expose sensitive data, move laterally across systems, or trigger a business-critical workflow that is hard to reverse. Approval is also warranted when the agent’s decision depends on ambiguous input or external data that cannot be trusted at face value. High-consequence actions need a human stop point.
👉 Read our full editorial: Agentic AI security risks expose the limits of traditional controls