How should organisations centralise AI use case and model inventories?

Why This Matters for Security Teams

A central inventory is not a reporting nicety. It is the control plane that tells security, risk, and platform teams what exists, who owns it, and whether it is still authorised to operate. Without one authoritative record, AI use cases, models, and agents drift across spreadsheets, ticketing systems, and cloud consoles, creating blind spots in approval, monitoring, and retirement. The result is duplicated effort, inconsistent governance, and model sprawl that security teams only notice after an incident or audit finding.

The issue is more urgent when AI systems handle sensitive data or connect to production tools, because the inventory becomes the starting point for access review, change control, and incident response. NIST’s NIST Cybersecurity Framework 2.0 treats governance and asset awareness as prerequisites for effective risk management, and NHIMG’s NHI Lifecycle Management Guide frames lifecycle ownership as the basis for control. In practice, many security teams encounter unauthorised AI use only after a model has already been deployed, integrated, and inherited by multiple teams.

How It Works in Practice

Centralisation works best when the registry is treated as the system of record, not a passive catalogue. Each entry should capture at minimum the use case, model or agent name, business owner, technical owner, environment, data classification, approval status, and lifecycle stage. If the organisation uses multiple model providers or agent frameworks, the registry should also record dependency relationships so teams can see which downstream apps, APIs, or workflows inherit the same risk.

That record then feeds three operational decisions. First, it supports intake and approval, so new AI work cannot move into production without review. Second, it enables continuous governance, where controls such as logging, access review, and retraining checks are tied to the asset rather than the team that built it. Third, it supports retirement, because stale models and abandoned agents are often where shadow risk accumulates. The Top 10 NHI Issues research highlights how fragmented identity ownership and poor lifecycle discipline create persistent exposure, while NIST CSF 2.0 reinforces the need for inventory-linked governance and monitoring. For large environments, many organisations also map inventory entries to control families in a GRC platform so policy decisions and technical telemetry stay aligned.

• Use one registry with mandatory ownership fields, not separate lists by business unit.
• Sync the registry with CI/CD, cloud, and model deployment workflows so registration happens before release.
• Attach lifecycle state changes to approvals, decommissioning, and periodic review.
• Link each model or agent to its data sources, tool access, and downstream consumers.

These controls tend to break down when teams can self-deploy models into local sandboxes or unmanaged SaaS AI tools because the inventory never sees the asset in the first place.

Common Variations and Edge Cases

Tighter centralisation often increases coordination overhead, requiring organisations to balance governance speed against local delivery autonomy. That tradeoff is real, especially in product teams that need rapid experimentation. The practical answer is usually not a single monolithic workflow for every use case, but a tiered registry with lighter intake for low-risk experiments and stricter review for production systems, sensitive data, or agentic workflows that can invoke tools.

There is no universal standard for how much metadata every inventory must hold, but current guidance suggests minimum fields should be consistent across the enterprise even if some teams add domain-specific attributes. The important part is comparability. If one team records model version and another records only project name, the inventory cannot support retirement, audit evidence, or incident triage. The Ultimate Guide to NHIs and Key Challenges and Risks is useful here because it shows how unmanaged identity growth becomes an operational risk, not just a documentation problem.

Edge cases also appear with embedded models, third-party APIs, and shadow AI. Best practice is evolving, but most mature programmes still require these assets to be registered if they influence regulated decisions, process sensitive data, or have network and tool access. Where organisations struggle most is with rapidly changing agentic systems, because their dependencies can change faster than manual cataloguing can keep up.

Related resources from NHI Mgmt Group

• How should organisations use data observability for AI reliability and audit readiness?
• How can organisations know whether AI model registration is actually working?
• AI use case inventory
• Should organisations use just-in-time access for AI model operations?