A central record of the risks tied to AI systems, their data, access paths, and mitigations. It turns scattered concerns into structured governance evidence so security, compliance, and engineering teams can track exposure, ownership, and remediation in one place.
Expanded Definition
An AI risk repository is more than a list of hazards. It is a governed register that records AI-specific risks, affected systems, data dependencies, access paths, control owners, treatment decisions, and residual exposure. In practice, it acts as the evidence layer behind AI governance, making it possible to answer what is at risk, why it matters, who is accountable, and what has changed since the last review.
For security and governance teams, the term is closest to a living risk register, but the AI context adds distinct concerns: model behaviour, training and inference data quality, prompt injection, tool access, third-party model services, and human approval points. That is why it maps naturally to the NIST AI Risk Management Framework and the NIST Cybersecurity Framework 2.0, which both expect risk to be identified, governed, and monitored rather than handled ad hoc.
Definitions vary across vendors on whether the repository should include only AI model risks or the wider system risks around identities, secrets, integrations, and data pipelines. NHI Management Group treats the broader view as more useful because agent access, service credentials, and automation paths often create the practical attack surface. The most common misapplication is treating the repository as a static spreadsheet, which occurs when teams capture initial findings but fail to update ownership, control status, and residual risk after model, data, or access changes.
Examples and Use Cases
Implementing an AI risk repository rigorously often introduces governance overhead, requiring organisations to balance faster AI delivery against more disciplined review, documentation, and sign-off.
Common use cases include the following:
- Recording model inventory risks, such as unsafe outputs, data leakage, or over-reliance on a model used in customer support workflows.
- Capturing access-path risks where an AI agent can call internal tools, query sensitive systems, or invoke APIs using privileged secrets.
- Tracking data risks across training, retrieval, and inference stages, including personal data exposure and weak data lineage.
- Documenting mitigation ownership, such as prompt hardening, human approval gates, logging, or control mapping to NIST SP 800-53 Rev 5 Security and Privacy Controls.
- Supporting assurance work by aligning entries to the NIST Cyber AI Profile (IR 8596) when cyber-enabled AI threats are in scope.
For organisations using a management-system approach, an AI risk repository also supports auditability under ISO/IEC 42001:2023 AI Management System Standard, because documented risks and treatments help demonstrate consistent governance rather than one-off review activity.
Why It Matters for Security Teams
An AI risk repository matters because AI risk is usually distributed across teams that do not share a single operational view. Security may see secrets and access paths, engineering may see model performance, and compliance may see policy obligations. Without one repository, gaps appear between those views, and mitigation work is often duplicated, delayed, or lost during deployment changes.
This becomes especially important where AI systems are connected to identities, service accounts, or non-human access. A risk entry that ignores agent credentials, token scope, or tool permissions misses the practical route attackers use to turn a model issue into an operational incident. The repository should therefore help teams decide where human approval is required, where least privilege applies, and which exposures are acceptable versus unacceptable under governance policy.
For assurance and reporting, the repository also supports monitoring expectations in frameworks such as the NIST AI Risk Management Framework and the cyber-focused guidance in NIST Cybersecurity Framework 2.0. Organisations typically encounter the real value of an AI risk repository only after a model incident, access misuse, or data exposure forces them to prove what was known, when it was known, and how it was handled.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF, NIST CSF 2.0, NIST SP 800-53 Rev 5 and NIST IR 8596 set the technical controls, while ISO/IEC 27001:2022 define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | NIST AI RMF structures AI risk identification, measurement, governance, and treatment. | |
| NIST CSF 2.0 | GV.RM | CSF 2.0 formalizes risk management governance for cybersecurity and connected AI systems. |
| NIST SP 800-53 Rev 5 | RA-3 | Risk assessment controls require identifying and documenting system risks and mitigations. |
| NIST IR 8596 | The Cyber AI Profile aligns cyber risk management expectations for AI-enabled environments. | |
| ISO/IEC 27001:2022 | A.5.7 | ISO 27001 supports documented information and risk treatment processes for AI governance. |
Record owners, treatment status, and residual risk so the register supports governance decisions.
