AI asset inventory and Shadow AI: what IAM teams need now

TL;DR: AI asset inventory is becoming the foundation for governing shadow AI, agentic systems, and embedded AI because organisations cannot secure or assess what they cannot see, according to Pillar Security. The control gap is structural: discovery, ownership, data lineage, and runtime exposure now need to be managed together, not as separate security tasks.

NHIMG editorial — based on content published by Pillar Security: AI Asset Inventory: The Foundation of AI Governance and Security

By the numbers:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.

Questions worth separating out

Q: How should security teams build an AI asset inventory for governance?

A: Start with a minimum schema that records business owner, technical owner, asset type, deployment environment, data sensitivity, dependencies, and lifecycle stage.

Q: Why do AI inventories need to include non-human identities and owners?

A: Because an AI asset without an accountable owner or identity trail cannot be governed, recertified, or retired reliably.

Q: What do security teams get wrong about AI discovery?

A: They often stop at production endpoints and miss the places where AI exposure starts, such as notebooks, source repositories, dependency files, and embedded SaaS features.

Practitioner guidance

• Build an AI-BOM schema that security can actually enforce Include business owner, technical owner, asset type, deployment environment, data classification, dependencies, and lifecycle state for every model, agent, notebook, endpoint, and embedded AI feature.
• Extend discovery into code, cloud, and SaaS layers Scan repositories, CI/CD pipelines, cloud ML platforms, container registries, and embedded AI tools so hidden assets are found before they become uncontrolled access paths.
• Tie every AI asset to an accountable identity Record the human owner and non-human identities that can configure, invoke, or maintain each AI asset, including service accounts, API keys, and third-party connections.

What's in the full article

Pillar Security's full blog post covers the operational detail this post intentionally leaves for the source:

• Step-by-step discovery coverage across code repositories, cloud ML platforms, container registries, and SaaS AI tools.
• Detailed asset fields for building an AI-BOM, including dependency history, compliance profile, and retirement status.
• Runtime guardrail and red teaming examples for AI applications with tool use and external API dependencies.
• Implementation context for integrating AI discovery with posture management and compliance reporting.

👉 Read Pillar Security's analysis of AI asset inventory for AI governance →

AI asset inventory and Shadow AI: what IAM teams need now?

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