Topic starter
02/08/2026 12:18 pm
TL;DR: As organisations move from generative AI to agentic AI, the security problem shifts from prompt safety to governing agents that call tools, assume identities, and act across APIs, according to Akto. The operating model now depends on inventory, risk assessment, guardrails, and response because static IAM assumptions break when software can act independently.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “CISO Strategy for Agentic AI Security in 2026”.
Key questions
Q: What breaks when an AI agent is not part of identity inventory?
A: When an AI agent is not part of identity inventory, governance breaks at the point of discovery.
Q: Why do agentic AI systems create more security risk than standard chatbots?
A: Agentic systems can turn model output into action, which means a bad instruction can affect code flow, tool use, and downstream state.
Q: What are the signs that AI agents are failing their intended guardrails?
A: Warning signs include repeated probing, unusual coordination between otherwise isolated agents, creation of side channels or message boards, and attempts to fake task outputs.
Practitioner guidance
- Build an agentic asset graph Map each agent, the identities it assumes, the tools it can call, and the data domains it can touch so ownership and scope are visible before deployment.
- Red-team agent workflows continuously Test prompt injection, tool chaining, context poisoning, and over-permissioned actions against the agents that touch production APIs, customer data, or financial systems.
- Enforce approved tool catalogs Limit every agent to a controlled tool list and deny access to APIs or commands that have not been explicitly authorised for that workflow.
Bottom line: Agentic AI security changes the control problem from prompt hygiene to governing software that can act through identities, tools, and APIs.
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02/08/2026 1:20 pm
Agentic AI turns identity governance into runtime control, not static entitlement management. The article is right to start with visibility because the real problem is not just how many agents exist, but which identities they assume and which systems those identities can reach. That shifts IAM from provisioning records to live execution oversight. Practitioners should treat agent identity as an operational control plane, not a catalog entry.
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.
- 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 CISOs respond when an agentic AI workflow touches production or financial systems?
A: They should narrow the tool set, add human validation for sensitive actions, and enforce detailed logging before the workflow expands further. The decision point is not whether the agent is useful, but whether the control surface is strong enough for the systems it can reach.
👉 Read our full editorial: CISO strategy for agentic AI security: visibility, guardrails, response