Topic starter
09/06/2026 11:51 pm
Agentic AI governance is an assumption problem before it is a policy problem: existing frameworks were designed for AI that suggests, not AI that acts. That assumption fails when the system can select tools, take actions and continue execution without a human approval gate. The implication is that governance must be rebuilt around runtime authority rather than model output review.
A few things that frame the scale:
- 54% of organisations are actively deploying AI agents across workflows, yet only 21% report a mature governance model for agentic AI.
A question worth separating out:
Q: What should IAM and PAM teams do differently for AI agents than for human users?
A: They should move from human-centric authentication assumptions to task-based authorisation, workload identity, and revocation-first controls. AI agents do not need a user experience, but they do need tightly bounded access, monitoring, and ownership. IAM and PAM teams should design for faster change, shorter access windows, and more frequent reassessment of what the agent can do.
👉 Read our full editorial: Agentic AI governance is already behind enterprise reality
10/06/2026 2:22 am
Agentic AI creates an identity governance problem, not just an AI policy problem. Once an agent can act across systems, the question is no longer whether its output is acceptable. The question is whether its runtime authority is bounded well enough for IAM, PAM, and governance controls to make sense. That moves the issue into identity security, where inventory, entitlement scope, and escalation boundaries are the real control plane. Practitioners should treat agent access as governed identity, not as a content moderation problem.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an autonomous agent causes business harm?
A: Accountability sits with the organisation that granted the agent its access, defined its guardrails, and failed to monitor its runtime behaviour. In practice, responsibility spans the AI owner, the identity team, and the control owners for every connected system the agent can touch. Governance must make that chain explicit before incidents occur.
👉 Read our full editorial: AI governance for agentic systems breaks at runtime, not policy
11/06/2026 1:23 am
Agentic AI creates an identity governance problem, not just an AI policy problem. Once an agent can act across systems, the question is no longer whether its output is acceptable. The question is whether its runtime authority is bounded well enough for IAM, PAM, and governance controls to make sense. That moves the issue into identity security, where inventory, entitlement scope, and escalation boundaries are the real control plane. Practitioners should treat agent access as governed identity, not as a content moderation problem.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an autonomous agent causes business harm?
A: Accountability sits with the organisation that granted the agent its access, defined its guardrails, and failed to monitor its runtime behaviour. In practice, responsibility spans the AI owner, the identity team, and the control owners for every connected system the agent can touch. Governance must make that chain explicit before incidents occur.
👉 Read our full editorial: AI governance for agentic systems breaks at runtime, not policy
12/06/2026 2:57 am
Runtime governance is the real control boundary for agentic AI: policies written for conversational systems do not control systems that can execute actions across multiple tools in one session. The problem is not policy intent but timing, because agent decisions now happen inside the same runtime window as the action itself. Practitioners should treat execution-time control as the primary governance boundary.
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.
- 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What happens when an AI agent is allowed to delegate across other agents and tools?
A: The blast radius grows quickly because one compromised instruction chain can propagate through downstream agents and systems before monitoring notices. Traditional human-centric oversight has no reliable frame of reference for this kind of non-human delegation, so the chain needs explicit runtime supervision.
👉 Read our full editorial: AI governance for agentic systems breaks at runtime, not policy