Know Your Agent

Expanded Definition

Know Your Agent is the discipline of verifying that an AI agent is the intended actor throughout execution, not merely at sign-in. In NHI governance, that means correlating identity, delegated authority, runtime context, tool usage, and behavioral signals so the agent cannot silently inherit privileges from a compromised session or a spoofed orchestration layer. The concept sits between identity proofing and operational control, which is why it maps closely to guidance in the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10.

Definitions vary across vendors because some teams treat this as simple authentication for an agent, while others include continuous verification of tool calls, policy adherence, and action provenance. NHI Management Group treats it as a runtime trust pattern: the system should be able to answer who the agent is, what it is allowed to do, why it is doing it, and whether its current behaviour still matches that declared identity. The most common misapplication is assuming a valid token proves the agent is legitimate, which occurs when organisations ignore session hijack, impersonation, or delegated credential abuse after initial authentication.

Examples and Use Cases

Implementing Know Your Agent rigorously often introduces latency and policy complexity, requiring organisations to weigh stronger runtime assurance against simpler automation flows.

• An engineering assistant requests repository access, and the platform verifies the agent’s workload identity, approved tool scope, and policy-bound context before allowing code changes.
• A customer support agent attempts to retrieve account data, and the control plane checks whether the action matches its assigned role and current task state, not just its API key.
• A financial operations bot signs a payment workflow, and verification ensures the agent has not been redirected through a compromised orchestrator or replaced by a lookalike agent.
• A security analyst uses an autonomous remediation agent, and each privileged step is re-evaluated against delegated authority and recorded provenance, similar to the issues discussed in NHIMG’s AI LLM hijack breach analysis.
• A platform team validates agent trust boundaries using ideas reflected in the MITRE ATLAS adversarial AI threat matrix and the OWASP NHI Top 10, then maps those checks to its own agent runtime policy.

These patterns are especially important where an agent acts across multiple systems with different trust assumptions, because a weakness in one connector can change the identity story for the whole workflow.

Why It Matters in NHI Security

Know Your Agent matters because an AI agent is a non-human identity with execution authority, and that authority can be abused in ways traditional IAM does not detect. If organisations cannot distinguish the intended agent from a hijacked or impersonating one, they lose control over delegated secrets, tool access, and downstream actions. That failure often becomes visible only after damage has already propagated across connected systems. NHIMG research shows that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which is why agent trust must be treated as an operational security problem, not just an access-management detail, as reinforced by the Ultimate Guide to NHIs — 2025 Outlook and Predictions.

Continuous verification also supports zero trust thinking for autonomous systems, where trust is never implied by network location or one-time login. In practice, teams use this concept to contain blast radius when an agent is tricked into calling the wrong tool, using stale credentials, or following malicious prompts embedded in a workflow. Organisations typically encounter the need for Know Your Agent only after an agent has already executed an unauthorised action, at which point attribution, containment, and rollback become operationally unavoidable to address.

Related resources from NHI Mgmt Group

• AI Agent Authentication
• How do you know if agent identity controls are actually working?
• How do organisations know if AI agent governance is actually working?
• How do security teams know whether an AI agent is operating safely?