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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.
NHIMG editorial — based on content published by Lakera: From Access Control to Outcome Control: Securing AI Agents with Check Point and Google Cloud AI Security
By the numbers:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
Questions worth separating out
Q: How should security teams govern AI agents that can take actions across systems?
A: They should govern the agent at the point of action, not only at authentication.
Q: Why do AI agents complicate least-privilege design?
A: Because the agent’s next move is not always knowable at provisioning time.
Q: What breaks when organisations rely only on access control for AI agents?
A: They can approve the right identity and still allow the wrong behaviour.
Practitioner guidance
- Define outcome-based policy rules Specify unacceptable agent actions in context, such as sensitive-data disclosure, unsafe transaction execution, or tool misuse, rather than relying only on resource-level allowlists.
- Broker agent actions through a control point Place identity, policy enforcement, and logging between agents and the tools or APIs they use so decisions are evaluated before execution completes.
- Instrument prompt and tool-response checks Add detection for prompt injection, manipulative inputs, and risky multi-step interactions so security teams can see when an agent is being steered off course.
What's in the full article
Lakera's full article covers the operational detail this post intentionally leaves for the source:
- How the Google Cloud Gemini Enterprise Agent Platform and Check Point AI Defense Plane are connected in the runtime control flow
- The specific agent-gateway and agent-registry functions used to discover agents and apply policy before deployment
- Runtime examples showing how prompt injection, sensitive data exposure, and unsafe tool use are detected and blocked
- The practical deployment context for financial services and other high-risk agent workflows
👉 Read Lakera's analysis of outcome control for AI agents on Google Cloud →
AI agent outcome control: what it means for IAM teams?
Explore further
05/07/2026 7:01 pm
Outcome control is becoming the missing layer in agentic identity governance. Access control still matters, but it cannot answer the question of whether an agent should take a specific action in a specific context. When the same entitlement can produce safe or unsafe behaviour depending on prompt and tool context, the programme needs a control point that evaluates intent, sequence, and result. Practitioners should treat this as a new governance layer, not a tuned version of old IAM.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
- Another 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the same survey.
A question worth separating out:
Q: How do IAM teams decide whether an AI agent needs runtime policy enforcement?
A: Use runtime policy whenever the agent can retrieve data, invoke tools, or trigger workflows that have operational or data-loss impact. If the action can be harmful even when authorised, static entitlements are not enough. That is the point where outcome control becomes necessary.
👉 Read our full editorial: AI agent governance shifts from access control to outcome control
