AI & ML Inventory

Expanded Definition

AI & ML Inventory is more than a register of models. It is the operational record of AI and machine learning assets, their dependencies, their deployment locations, and the secrets or service identities that allow them to run. In NHI security, the inventory matters because hidden AI usage often appears first as a new API key, a notebook credential, a model endpoint, or a coding assistant embedded in a workflow. Industry usage is still evolving, so some teams treat the inventory as an AI asset register while others extend it to include supply chain evidence, provenance, and access paths.

A mature inventory ties each asset back to source control, build pipelines, cloud projects, and owners so policy can be enforced with evidence rather than assumptions. That aligns well with the intent of the NIST Cybersecurity Framework 2.0, even though no single standard yet defines every field an ai inventory must contain. The most common misapplication is treating the inventory as a static spreadsheet, which occurs when teams fail to update it as models, packages, and credentials move through development and deployment.

Examples and Use Cases

Implementing AI & ML Inventory rigorously often introduces governance overhead, requiring organisations to weigh faster experimentation against tighter visibility and control.

• Security teams map every approved model endpoint to the cloud account, code repository, and owner so they can trace usage after a credential leak.
• Platform teams record coding assistants, vector stores, and model gateways alongside traditional software assets, then reconcile them with repository and CI/CD evidence.
• Risk teams use inventory data to identify orphaned notebooks and forgotten service accounts before they become unmanaged access paths, a pattern often seen in incidents discussed in the DeepSeek breach.
• Cloud security teams compare inventory records against live usage to find shadow AI deployments and exposed secrets, including cases similar to the Hugging Face Spaces breach.
• Governance teams classify whether a system is a model, a coding assistant, or an AI-enabled workflow so policy can be applied consistently across teams and environments.

Why It Matters in NHI Security

An AI & ML Inventory is a control point for discovering where non-human identities actually exist in the stack. Without it, organisations cannot reliably answer basic questions such as which systems hold secrets, which models have external tool access, or which service identities can call a proprietary endpoint. That gap increases exposure to secret sprawl, shadow AI, and unreviewed access paths that bypass normal IAM reviews. NHIMG research on The State of Secrets in AppSec shows that 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, which makes inventory coverage a governance issue, not just an asset management task. The operational value is especially clear when AI systems inherit credentials from pipelines, notebooks, or developer tools that were never meant to persist.

Once a secret is exposed or an AI agent misbehaves, the inventory becomes the evidence trail needed to scope blast radius, revoke access, and confirm what was actually deployed. Organisations typically encounter the true cost of missing inventory only after a secret leak or unauthorized model use, at which point AI & ML Inventory becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• What is the difference between AI discovery and AI inventory?
• What is the difference between AI inventory and AI governance?
• When does AI agent discovery become more than an inventory problem?
• What do organisations get wrong about AI agent inventory and visibility?