Model Discovery

Model discovery surfaces what models exist, where they run, who owns them, and how they are used. Without it, AI Bills of Materials, license compliance, and model security are all best-effort exercises.

Model discovery commonly captures:

• Inventory: Names, versions, vendors, and deployment locations.
• Provenance: Source repositories, training data, and licensing.
• Usage: Which applications, agents, and users invoke the model.
• Risk posture: Known vulnerabilities, jailbreak susceptibility, and license issues.
• Governance state: Approvals, model cards, and required documentation.

Mature model discovery feeds directly into AI Bill of Materials reporting and into model approval workflows. That tight loop is what turns inventory from a passive list into an active control.

Programs that operate model discovery well also keep the inventory enriched with model card status, evaluation results, and runtime telemetry, giving stakeholders a single source of truth.

The hardest model discovery problems live inside SaaS apps and developer environments where models ship as features, so programs that mature fastest deploy specialized integrations for those channels.

How PointGuard AI Helps

PointGuard's AI Discovery continuously identifies and profiles models across the enterprise, and AI Supply Chain Security adds provenance, license, and vulnerability context to each one. Together they make every model in use traceable, governable, and risk-rated from procurement to retirement.

Learn More

• Mitchell et al., Model Cards for Model Reporting arXiv:1810.03993
• NIST AI Risk Management Framework
• EU AI Act official text