Treat each AI agent as a governed identity with a defined action scope, approved data access, and runtime enforcement. Governance should not stop at policy approval. Teams need discovery, access boundaries, and controls that can stop unsafe actions when an agent moves outside its intended workflow.
How to govern AI agents as accountable actors, not just tools
Public sector teams should treat an AI agent like an accountable actor with a bounded mandate, not a generic application feature. That means defining who owns it, what it may do, what systems it may touch, and what data it may use. Governance has to cover the full lifecycle, from registration to retirement, so the agent cannot keep acting after its purpose has expired.
The practical shift is from approving an intended use to governing real runtime behaviour. If an agent can call APIs, move between systems, or trigger workflows, those actions need explicit scope, approval, and traceability. Without that, an agent becomes an unowned pathway for automation, privilege creep, and unintended cross-system impact.
For a broader view of how agent identity changes across autonomy levels, see AI Agents vs Agentic AI and the Agentic AI Identity Guide.
What controls matter when one agent spans multiple systems?
Cross-system agents need more than a single access policy. Teams should enforce task-scoped permissions, separate environments, and a clear distinction between the agent’s standing capabilities and the temporary rights it needs for a specific action. The safest pattern is least privilege with per-action decisions, so the agent is continuously authorised rather than broadly trusted once at deployment.
Access boundaries should also reflect the systems involved. A public sector agent may need read access to one service, write access to another, and no direct access to sensitive datasets unless the workflow specifically requires it. The control objective is to keep each step narrow enough that a mistake, compromise, or misconfiguration cannot automatically spread across the whole operating chain.
For authorisation design and zero-standing-privilege thinking, use AI Agent Authorisation Guide and Zero Trust for AI Agents. The NIST AI Risk Management Framework is also useful where the public sector needs a governance structure for accountable AI decisions.
Why discovery, monitoring, and kill switches are part of governance
Governance fails if it only exists on paper. Teams need discovery so they can inventory which agents are active, what connectors they use, and which workflows they influence. They also need observability that shows when an agent is taking unusual actions, escalating its reach, or operating outside the approved sequence. If the platform cannot surface that behaviour quickly, the organisation cannot distinguish legitimate automation from unsafe drift.
Runtime controls matter because policy approval does not stop an agent that has gone wrong. The practical requirement is to be able to pause, revoke, or block actions when the agent strays outside its intended workflow. That is especially important in public sector environments where one mistaken action can affect records, case handling, payments, or citizen services across multiple systems.
For discovery and response, see Shadow AI and AI Agent Discovery Guide and AI Agent Observability, Audit and Incident Response Guide. For threat-oriented controls around tool use, escalation, and containment, the OWASP Agentic AI Top 10 is a useful external reference, and CSA MAESTRO agentic AI threat modeling framework helps structure the operational review.
Risk and Threat Considerations
Cross-system agents concentrate trust. If their credentials, tool access, or delegated authority are too broad, a single prompt, connector failure, or misrouted action can create disproportionate exposure across otherwise separate services. In public sector settings, that can turn an operational mistake into a records, privacy, or service continuity incident.
Failure mechanism: The agent is allowed to act as though it were a trusted operator across multiple systems, so a compromised prompt, malicious input, or unsafe workflow step can propagate through connected tools before human review catches it.
Impact: The result can be unauthorised data access, incorrect changes in downstream systems, broken auditability, and a wider blast radius than the original task justified.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Directly addresses agent identity, delegated authority, and overreach across systems. |
| Recommendation — Enforce per-action authorisation and remove standing privilege from agents. | ||
| CSA MAESTRO | MAESTRO | Structures threat modelling for multi-agent orchestration and cross-system autonomy risks. |
| Recommendation — Model cross-system agent workflows and contain high-impact failure paths. | ||
| NIST AI RMF | Govern | Supports accountable AI governance, oversight, and lifecycle controls for deployed agents. |
| Recommendation — Establish governance, accountability, and monitoring for AI agent use. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Limits agent permissions to the minimum needed for each task. |
| AU-2 — Event Logging | Logs agent actions so cross-system activity can be traced and reviewed. | |
| Recommendation — Restrict agent permissions to the minimum necessary for approved actions. Log agent actions and preserve audit trails for cross-system activity. | ||
Practitioner Guidance
What to verify: Confirm that every production agent has an owner, a defined purpose, a bounded action list, and explicit connector approvals. If any of those are missing, the agent is not ready for cross-system use, even if the workflow appears to work.
Decision rule: If the agent can change state in another system, require per-action authorisation or a comparable runtime control. If it can only read data, keep the boundary narrow and still log the access path so you can prove what it saw and why.
Practitioner takeaway: The hardest part of governing AI agents is not approving them, it is preserving control after they start acting. Public sector teams should optimise for bounded authority, continuous visibility, and fast shutdown when behaviour no longer matches the approved workflow.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on October 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org