Topic starter
10/07/2026 9:23 pm
TL;DR: Almost 80% of organisations are already deploying AI agents, and Gartner predicts task-specific agents will be integrated into 40% of enterprise applications by the end of 2026, according to Zero Networks. The governance gap is not AI capability but identity control: agent access expands, chains, and drifts faster than human-paced review cycles can contain.
Editorial analysis by NHI Mgmt Group, based on content published by Zero Networks: “Agentic AI Cybersecurity Risks: How to Secure AI Agents”.
By the numbers:
- Almost 80% of organisations are already deploying AI agents.
- Gartner predicts 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026.
- Less than 5% of enterprise applications used task-specific AI agents in 2025, according to Gartner research cited by Zero Networks.
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 AI agents increase identity risk even when the login succeeds?
A: A successful login only proves that the agent reached the system.
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
- Build a live agent inventory Track which agents exist, what systems they can reach, and which credentials or tokens they actually use in practice.
- Compare observed reach to intended task scope Measure the gap between provisioned access and real agent behaviour, then remove systems, APIs, or data stores the agent does not demonstrably need.
- Contain agents with deterministic network boundaries Apply identity-based controls at the network layer so each agent can reach only the services it has proven business need for.
Bottom line: AI agents are behaving like privileged machine identities, which means the real control issue is runtime reach rather than model capability alone.
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10/07/2026 9:46 pm
Agentic AI creates assumption collapse, not just a larger attack surface: identity governance was designed around access that remains stable long enough to be reviewed, certified, and revoked. That assumption fails when an AI agent can acquire, chain, and shed access as part of a live task. The implication is that identity programmes must treat access as a runtime condition, not a static entitlement state.
A few things that frame the scale:
- 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.
A question worth separating out:
Q: Should organisations use the same lifecycle controls for AI agents and other non-human identities?
A: Yes, but the review points differ because AI agents can change behaviour during execution. Ownership, inventory, entitlement review, and offboarding still apply, yet they must be driven by observed runtime activity rather than static assignment alone. The lifecycle has to keep pace with the agent’s operating scope.
👉 Read our full editorial: Agentic AI attack surface is outpacing enterprise identity controls