An approved AI path is a sanctioned route for using AI that includes known identity, defined data handling, and accountable ownership. It gives security teams a control point for discovery, policy enforcement, and exception management instead of leaving usage to ad hoc experimentation.
Expanded Definition
An approved AI path is a governed pathway for AI use that security and risk teams have explicitly sanctioned because the identity behind the workflow is known, the data handling rules are defined, and accountability is assigned. In NHI security, the key distinction is not whether AI is being used, but whether the use is attached to a controllable identity, approved scope, and auditable policy set.
Definitions vary across vendors, but the operational idea is consistent with the NIST Cybersecurity Framework 2.0 emphasis on governed access, asset visibility, and risk treatment. A mature approved AI path also supports exception handling when a team needs to use a model, agent, or AI-enabled tool outside the standard pattern. It is most useful when connected to identity lifecycle controls, secret governance, logging, and clear business ownership. NHIMG research on DeepSeek breach shows why ungoverned AI exposure can turn data and secrets into a systemic risk rather than a one-off mistake.
The most common misapplication is treating any enterprise AI subscription as an approved AI path, which occurs when the tool is purchased but the identity, data, and ownership controls are never defined.
Examples and Use Cases
Implementing an approved AI path rigorously often introduces review overhead and usage constraints, requiring organisations to weigh faster adoption against stronger control and traceability.
- A development team uses a sanctioned coding assistant under a named service account, with restricted repository access, approved prompt handling, and logging for audit review.
- An internal support agent relies on a controlled RAG workflow that can read only curated knowledge sources, while sensitive records remain excluded from the model context.
- A finance function gets an exception-approved AI path for invoice triage, with human review, data minimisation, and time-bound access to specific documents.
- A security team approves a vendor chatbot only after validating identity federation, secret storage, and retention settings against governance requirements described in the State of Secrets in AppSec.
- An engineering org stages an AI agent behind a controlled gateway so that tool use, token access, and command execution are limited to a predefined business case aligned with the NIST Cybersecurity Framework 2.0.
These examples differ from shadow ai because the approved path creates a visible control point for discovery, enforcement, and exception management instead of relying on informal team-level judgment.
Why It Matters in NHI Security
Approved AI paths matter because AI use often depends on credentials, tokens, certificates, and delegated access that behave like other NHIs but expand the blast radius when mismanaged. If a model, agent, or assistant is allowed to access internal systems without a sanctioned identity and clear ownership, security teams lose visibility into what was touched, by whom, and under which policy. That makes incident response, access review, and revocation much harder.
This is also where secrets governance becomes critical. NHIMG research in The State of Secrets in AppSec reports that only 44% of developers follow security best practices for secrets management, which underscores how quickly unapproved AI usage can intersect with credential exposure. Once an AI workflow is allowed to retrieve sensitive data or call tools, any weakness in secret handling can become an enterprise incident. An approved path lets defenders define what is allowed, what is logged, and what is blocked before the first prompt is submitted.
Organisations typically encounter this control gap only after a leaked token, unexpected model access, or data exposure forces them to trace AI usage, at which point an approved AI path 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 Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | AI-02 | Covers unsafe agent permissions and uncontrolled tool access in AI workflows. |
| OWASP Non-Human Identity Top 10 | NHI-01 | Approved AI paths depend on known NHI ownership and discoverable access paths. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access and controlled approvals align to governed AI pathways. |
Require approved identities, scoped tools, and logged actions for any agentic workflow.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 10, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
