Topic starter
02/08/2026 12:01 pm
TL;DR: Agentic AI is already in production at 69% of enterprises, but only 21% maintain a fully up-to-date inventory of agents, tools, and connections, according to Akto's State of Agentic AI Security 2025 report. The control gap is no longer theoretical: governance, visibility, and runtime enforcement are lagging the pace of autonomous action.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “The State of Agentic AI Security 2025”.
By the numbers:
- 69% of enterprises are already piloting or running early production agent deployments.
- Only 21% of organizations maintain a fully up-to-date inventory of agents, MCP servers, tools, and connections.
- 79% of enterprises operate with blindspots where agents invoke tools, touch data, or trigger actions that security teams cannot fully observe.
Key questions
Q: What breaks when AI governance starts with policy instead of inventory?
A: Policy-first programmes usually stall because teams cannot define scope, boundaries, or ownership with confidence.
Q: Why do AI assistants expand enterprise risk even when the model output looks harmless?
A: Because the security impact comes from execution, not language quality.
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 an agent inventory standard Create a live register for agents, MCP servers, tools, and active connections, then assign an accountable owner to each record.
- Tighten tool onboarding controls Require explicit approval, scoped permissions, and documented purpose before any agent can invoke a new API, workflow, or external service.
- Instrument runtime monitoring Capture prompts, tool calls, and outputs end to end so that agent behaviour can be traced across the full action path.
Bottom line: Agentic AI readiness is lagging because organisations are deploying agents faster than they can inventory, govern, and monitor them.
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02/08/2026 1:07 pm
Agentic AI has turned identity governance into action governance: The old question was who may authenticate; the new question is what the agent can do once authenticated. That shift matters because agents invoke tools, chain actions, and create state change across systems. The implication is that IAM programmes must now govern execution boundaries, not just access grants.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 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.
A question worth separating out:
Q: What is the difference between managing AI agents like apps and managing them like identities?
A: Applications are usually governed by deployment and configuration controls, while identities are governed by who or what can act, under what permissions, and with what audit trail. Agentic AI behaves more like an identity because it initiates action across tools and systems. That is why access, ownership, and revocation matter so much.
👉 Read our full editorial: Agentic AI security readiness is lagging enterprise adoption