Know Your AI is a governance approach for verifying autonomous AI before it is allowed to act. It focuses on the agent’s identity, sponsor, permissions, operating rules, and revocation state. The goal is to make AI actions attributable, policy-bound, and traceable across transactions, data access, and machine interactions.
Expanded Definition
Know Your AI is a governance discipline for verifying an autonomous AI before it is allowed to operate, much like identity proofing and access vetting for human users. It asks who sponsored the agent, what permissions it has, what policy boundaries govern its actions, and whether it has been suspended or revoked. In NHI security, the point is not to trust the model output alone, but to make the agent itself attributable and controllable across systems, data, and workflows.
Definitions vary across vendors, because some teams use the term to mean onboarding checks while others extend it to continuous authorization. NHI Management Group treats it as an operational control layer that should work alongside lifecycle governance, secret hygiene, and policy enforcement. That framing aligns with the NIST Cybersecurity Framework 2.0 emphasis on identity, access, and governance outcomes, even though NIST does not use this exact phrase. The most common misapplication is treating a prompt filter or model approval as sufficient, which occurs when the organisation verifies the application but not the agent identity, revocation state, or delegated authority.
Examples and Use Cases
Implementing Know Your AI rigorously often introduces onboarding friction, requiring organisations to balance speed of automation against the cost of stronger verification and ongoing oversight.
- A finance team registers each agent with a named sponsor, a scoped policy profile, and a revocation path before it can initiate payment-related actions.
- An engineering organisation ties agent credentials to a machine identity inventory so the agent can be traced during incident response and permission review.
- A customer support workflow allows an AI agent to draft responses, but requires explicit approval gates before it can access account data or modify records.
- An organisation uses continuous checks to detect when an agent’s permissions exceed its declared purpose, then disables the agent until review is complete, consistent with identity governance themes discussed in the The State of Secrets in AppSec research.
- Security teams validate that exposed credentials have not silently expanded agent reach, reflecting attacker behavior observed in the LLMjacking research and the operational exposure described in the DeepSeek breach analysis.
Where the industry is still evolving, some organisations combine this with NIST Cybersecurity Framework 2.0 control mapping and others treat it as an internal governance pattern rather than a formal standard.
Why It Matters in NHI Security
Know Your AI matters because autonomous agents can move faster than human review, and any weakness in identity, sponsorship, or revocation creates an immediate path to unauthorized action. Once an AI agent is granted broad access without clear provenance, incident responders lose the ability to distinguish intended automation from abused automation. That is especially dangerous when secrets, tokens, or API keys are involved, because an agent can inherit privilege far beyond what its operator intended.
NHIMG research shows how quickly exposed credentials become actionable: when AWS credentials are public, attackers attempt access within an average of 17 minutes. That speed illustrates why governance must cover not just model behavior, but the identity and access lifecycle around the agent itself. The issue also connects to broader secret-management weakness, including the 27-day average remediation time for leaked secrets reported in The State of Secrets in AppSec. Organisations typically encounter the need for Know Your AI only after an agent has already overreached, misused a secret, or triggered an unexplained transaction, at which point identity verification becomes operationally unavoidable to address.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-63 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Agent identity and lifecycle governance are core to non-human identity controls. |
| NIST CSF 2.0 | GV.OC, PR.AA | Know Your AI supports governance outcomes and identity assurance for automated actors. |
| NIST SP 800-63 | AAL2 | The term maps to assurance expectations for proving an agent is what it claims to be. |
| NIST Zero Trust (SP 800-207) | SA, PE, DP | Zero trust requires continuous verification of identities and access decisions. |
| OWASP Agentic AI Top 10 | A10 | Agentic AI guidance highlights governance gaps around delegated actions and tool use. |
Register each AI agent, bind it to a sponsor, and verify its lifecycle state before granting action authority.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
