An AI catalog is an inventory of the models, applications, agents, and integrations used across an organisation. It provides ownership, purpose, and data-lineage context so security, governance, and privacy teams can place controls where the AI system actually operates.
Expanded Definition
An AI catalog is more than a spreadsheet of model names. In security and governance terms, it is the authoritative inventory that ties each AI system to its owner, intended business purpose, data sources, deployment context, and downstream integrations. That context matters because an AI model in isolation is not the real risk surface; risk emerges when models, agents, APIs, plugins, and human workflows interact. Definitions vary across vendors on whether the catalog should include only models, or also prompts, RAG pipelines, agent toolchains, and safety policies, so organisations should state their scope explicitly.
For NHI Management Group, the catalog becomes the control plane for visibility, accountability, and policy enforcement across AI use cases. It helps teams distinguish a sanctioned internal assistant from a production-facing agent that can execute actions, access secrets, or trigger workflows. A well-governed catalog also supports impact analysis when models are retrained, replaced, or connected to new data sets. The closest governance anchor is the NIST Cybersecurity Framework 2.0, which emphasizes asset visibility, risk governance, and control mapping.
The most common misapplication is treating the AI catalog as a procurement list, which occurs when teams record product names but omit ownership, data lineage, and operational integrations.
Examples and Use Cases
Implementing an AI catalog rigorously often introduces administrative overhead, requiring organisations to weigh operational visibility against the effort needed to keep entries current as systems change.
- A security team records every customer-facing chatbot, its owning business unit, and the API endpoints it can reach, so a compromised integration can be traced quickly.
- A governance group catalogs an internal RAG application together with its knowledge sources, retention rules, and approval status to support privacy review and audit readiness.
- An engineering team documents an agent that can open tickets, query databases, and send emails, making its tool permissions visible to OWASP guidance for LLM and agent risk reviews.
- A cloud security team inventories third-party AI services used in software development, noting where source code, secrets, or sensitive prompts may be transmitted outside controlled boundaries.
- A model risk owner links each deployed model to its training data, release date, and fallback plan so a problematic update can be isolated during change management.
These use cases are especially valuable when AI is embedded in existing workflows rather than launched as a standalone product. The catalog creates the evidence base for questions such as who approved it, what data it touched, and which downstream system it can influence.
Why It Matters for Security Teams
Security teams need an AI catalog because they cannot protect what they cannot see. Without a trustworthy inventory, access reviews miss agentic tools, privacy teams miss data flows, and incident responders miss the systems that actually executed a harmful action. This is where the catalog intersects with identity and non-human identity governance: many AI services and agents authenticate with tokens, service accounts, or workload identities, so the catalog should capture which NHI or secret grants execution authority and whether that authority is still justified.
The catalog also helps align AI governance with enterprise control frameworks. If an organisation already uses the NIST Cybersecurity Framework 2.0, the AI catalog becomes the source of truth for asset management, risk prioritisation, and control ownership across AI-related assets. In practice, this makes it easier to decide where monitoring, review, and rollback procedures belong when an AI application is tied to business operations.
Organisations typically encounter the real cost of a weak AI catalog only after a model misbehaves, an agent overreaches, or an audit asks for a complete list of AI-connected data flows, at which point the inventory 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, NIST AI RMF and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OV-01 | CSF 2.0 frames governance and oversight of assets, which an AI catalog supports. |
| NIST AI RMF | AI RMF centers on mapping, measuring, and managing AI risks across the system lifecycle. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance relies on visibility into tool access, autonomy, and integrations. | |
| OWASP Non-Human Identity Top 10 | NHI guidance applies where AI services use workload identities, tokens, or API keys. | |
| NIST SP 800-63 | IAL2 | Identity assurance is relevant when AI workflows depend on verified human approvals. |
Use the catalog as the authoritative inventory for AI ownership, oversight, and control assignment.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 2, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
