Topic starter
05/07/2026 6:44 pm
TL;DR: AI agents now retrieve data, invoke tools, and execute workflows in real time, which means access can be valid while the outcome is still wrong, according to Lakera. The security model is shifting from permission checks to behaviour and policy enforcement across agent actions, tool calls, and runtime context.
Editorial analysis by NHI Mgmt Group, based on content published by Lakera: “From Access Control to Outcome Control: Securing AI Agents with Check Point and Google Cloud”.
Key questions
Q: How should security teams govern AI agents that can produce unsafe outputs after login?
A: Security teams should govern AI agents with two separate controls: identity access and behavioural assurance.
Q: Why is authentication alone not enough for agentic AI access control?
A: Authentication answers whether the agent can prove its identity, but agentic AI can still take actions that exceed the original intent of that identity.
Q: What breaks when organisations audit AI agents like service accounts?
A: Audit trails break when teams record only the API call and ignore the prompts, tools, and model outputs that caused it.
Practitioner guidance
- Define agent outcome policy Specify which classes of actions an AI agent may take in context, not just which systems it may reach.
- Instrument runtime decision logging Log tool calls, API requests, input context, and blocked decisions so reviewers can reconstruct why an agent acted and whether the decision was appropriate.
- Separate provisioning from execution approval Treat the entitlement to connect as distinct from the right to execute a sensitive action, especially where the same agent can move across workflows.
Bottom line: AI agents introduce a control problem that traditional access checks do not resolve, because legitimate permissions can still produce unsafe behaviour.
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05/07/2026 7:01 pm
Outcome control is the right abstraction for agentic systems because access validity no longer predicts safety. In traditional IAM, the entitlement question is whether a subject may reach a resource. In agentic systems, that answer is only the starting point because the agent can still select unsafe tool sequences after access is granted. Practitioners should stop treating authorisation as the end of governance and start treating it as the beginning.
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: How can organisations tell whether AI agent governance is actually working?
A: Look for evidence that agent access is ephemeral, traceable, and constrained at the action level. If the organisation cannot show which runtime acted, what it touched, and which endpoint or command it used, then governance is still too coarse. Effective control produces auditable decisions, not just authentication events.
👉 Read our full editorial: AI agent governance shifts from access control to outcome control