What should organisations do first when they start governing AI agent behaviour?

Why This Matters for Security Teams

Governing AI agent behaviour starts with the workflows where failure causes the most real-world damage: customer data access, payment or spend authority, and actions that can alter systems or reveal secrets. The mistake many teams make is treating agents like static applications with fixed roles. Autonomous agents are goal-driven, can chain tools, and may take actions that were never explicitly scripted, which makes intent drift a practical security issue, not a theoretical one. Current guidance from the NIST AI Risk Management Framework supports risk prioritisation by impact, while NHIMG research on AI Agents: The New Attack Surface report shows how often agents already act outside intended scope. In practice, many security teams encounter agent overreach only after a sensitive dataset has been exposed or an unauthorised action has already been completed, rather than through intentional testing.

How It Works in Practice

The first governance step is to define a small set of high-value agent workflows and treat each one as a distinct security object. For each workflow, document the business purpose, the data it may read, the tools it may call, the escalation path it may trigger, and the expiry conditions for any credential or grant it receives. This is where static RBAC usually fails: an agent does not follow a predictable human schedule, so a role that is “broad enough” for all likely tasks quickly becomes too broad for safe use.

Practitioners are moving toward intent-based or context-aware authorisation, where approval happens at runtime based on what the agent is trying to do, the current dataset, the risk of the action, and the surrounding context. That means combining policy-as-code with short-lived access and workload identity. Standards such as OWASP Top 10 for Agentic Applications 2026 and CSA MAESTRO agentic AI threat modeling framework both reinforce the need to constrain tool use, data access and escalation paths at the point of execution.

• Assign each agent workflow a named purpose and owner.
• Allow only the minimum data classes needed for the task.
• Issue just-in-time credentials with short TTLs and automatic revocation.
• Evaluate every privileged action against live policy, not a one-time approval.
• Log tool calls, data reads and escalation events for review and replay.

NHIMG’s OWASP NHI Top 10 and Top 10 NHI Issues both point to the same operational pattern: keep the agent’s identity, permissions and expiry tightly bound to the specific workflow rather than the broader platform. These controls tend to break down when a single agent is allowed to operate across multiple business domains because the policy context becomes too coarse to distinguish safe from unsafe actions.

Common Variations and Edge Cases

Tighter controls often increase delivery overhead, requiring organisations to balance speed against containment. That tradeoff is especially visible when an agent needs access to multiple systems, such as CRM, code repositories and finance tools, because each additional integration expands the chance of unintended behaviour. There is no universal standard for this yet, but current guidance suggests using separate identities and policies for separate purposes instead of one “super-agent” account.

Edge cases also appear when agents collaborate in multi-agent pipelines. A planner agent, retrieval agent and execution agent may each be individually low risk, yet together they can produce privilege escalation or data movement that no single policy anticipated. In those environments, best practice is evolving toward stepwise approval, per-hop policy checks and constrained handoff tokens. NHIMG’s Ultimate Guide to NHIs - Lifecycle Processes for Managing NHIs and Ultimate Guide to NHIs - Regulatory and Audit Perspectives are useful for aligning expiry, review and evidence collection across these lifecycle stages. For the risk-management side, the NIST AI Risk Management Framework is the clearest baseline. The main exception is highly regulated or legacy environments where runtime policy enforcement cannot yet be embedded, in which case compensating controls must focus on narrower scopes, stronger logging and manual approval gates.

Related resources from NHI Mgmt Group

• Why is single-provider AI agent governance not enough for enterprise security?
• How can organisations reduce the blast radius of compromised agent identities?
• Should organisations prioritise external exposure or internal credential governance first?
• Should organisations prioritise AI agent settings or service account cleanup first?