Governments should establish a basic security baseline before broad AI adoption, starting with inventory, access control, and clear governance for high-value assets. AI agents need to be treated as identities with defined privileges, oversight, and monitoring, not as experimental add-ons. Without that foundation, organizations expand attack surface faster than they can understand or control it.
What baseline security standards should come first?
Governments should start with the controls that make AI agents governable at all: a complete inventory of agents and related non-human identities, clear ownership, strong authentication, least-privilege access, and defined approval paths for privileged actions. The baseline should be simple enough to enforce across ministries, agencies, and vendors before more advanced autonomy is allowed.
That baseline matters because an AI agent is not just software logic, it is an actor with some form of authority. If governments cannot answer who owns it, what it can access, and when it must be re-approved, they will scale uncertainty instead of capability.
Baseline standards should also cover high-value assets first. Systems that hold sensitive data, payments, citizen records, operational commands, or privileged infrastructure access need tighter rules than low-impact pilots, because those are the places where an agent mistake becomes a material security event.
How should governments treat AI agents and other non-human identities?
Governments should treat AI agents as identities with lifecycle, privilege, and accountability requirements, not as temporary experiments outside normal control. That means each agent should have a defined owner, a narrow purpose, an explicit trust boundary, and a way to revoke access when the task, model, vendor, or environment changes.
This same logic applies to broader non-human identities such as service accounts, automations, and workload credentials. The control question is the same: can the system prove the actor is authorised, limit what it can do, and detect when it behaves outside its expected scope?
A practical baseline is to separate agent identity from human identity as early as possible. Shared credentials, delegated user sessions, or vague inherited permissions make it hard to attribute action, rotate access, or contain misuse once the agent starts interacting with production systems.
How do governments scale safely without creating unmanaged attack surface?
Governments scale safely when security gates are part of rollout, not an afterthought. Before new agent use is approved, there should be an inventory entry, an assigned business owner, a risk review, a privilege review, logging requirements, and a defined offboarding path.
That approach keeps the security baseline tied to operational reality. It also prevents agencies from deploying dozens of partially governed agents that each look harmless in isolation but collectively create broad access paths, hidden dependencies, and difficult-to-audit automation chains.
The scaling rule should be straightforward: expand autonomy only after the current population is visible, reviewable, and bounded. If an organisation cannot inventory an agent, explain its privileges, and trace its actions, it is not ready to multiply that pattern across more departments or services.
Risk and Threat Considerations
Governments that scale AI agents before establishing baseline controls can create fast-growing exposure in access, oversight, and recovery. The most common failure is not a single dramatic flaw, but many small trust decisions that add up to excessive privilege, unclear ownership, and weak monitoring.
Failure mechanism: Agents inherit broad access, use shared or long-lived credentials, or act through human accounts without enough policy separation. That makes it harder to detect misuse, harder to rotate access safely, and easier for a compromised agent, vendor path, or workflow to move laterally into sensitive systems.
Impact: The organisation loses control over who or what is acting, which systems are reachable, and how much damage a mistake or compromise can cause. In government settings, that can affect citizen data, operational continuity, and confidence in automated decision support.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and OWASP API Security Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI agents and other NHIs must have scoped privileges before scale. |
| NHI-07 — Long-Lived Secrets | Baseline governance must avoid secrets that outlive ownership or review. | |
| Recommendation — Limit each agent to the minimum access needed for its approved task. Rotate and replace long-lived agent secrets with time-bound credentials. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The question is about setting identity and privilege baselines for agents. |
| ASI10 — Rogue Agents | Unmanaged agents become rogue when ownership and oversight are absent. | |
| Recommendation — Enforce per-agent authorization and block inherited broad privilege. Require ownership, monitoring and revocation paths before deployment. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Non-human identities and agents need strong machine-to-machine authentication. |
| AC-6 — Least Privilege | Baseline standards must keep agent access narrow and reviewable. | |
| AU-2 — Event Logging | Governable agents require audit trails for actions and privilege use. | |
| Recommendation — Authenticate agents and workloads with controlled service identities. Grant only the minimum permissions each agent needs. Log agent actions and privilege changes with enough detail for review. | ||
| NIST CSF 2.0 | GV.RM-01 — Risk Management Strategy | Governments need a risk strategy before expanding autonomous agents. |
| ID.AM-01 — Physical devices and systems are inventoried | A baseline starts with inventory of agents, systems and related identities. | |
| Recommendation — Define risk thresholds and approval gates before scaling agent use. Inventory every agent and related identity before granting production access. | ||
| OWASP API Security Top 10 | API5 — Broken Function Level Authorization | Agent actions must be authorised at the function level, not assumed from login. |
| Recommendation — Check function-level authority before allowing sensitive agent actions. | ||
Practitioner Guidance
What to prioritise: Build the baseline around inventory, ownership, authentication, privilege scope, logging, and revocation before approving broader agent deployment. If those six elements are missing, scale should be paused rather than accelerated.
What to verify: Every agent should have a named owner, a documented purpose, a bounded set of systems it can reach, and a tested way to disable it quickly. Verify that privileged actions are separately approved, not silently inherited from a human operator or generic platform role.
What good looks like: Security and operations teams can answer, for any agent, what it is, who owns it, what it can do, and how it is taken out of service. That level of clarity is the real threshold for responsible expansion.
Practitioner takeaway: Governments do not need perfect AI policy before adoption, but they do need a minimum identity and access foundation that makes every agent visible, attributable, and retractable.
