AI/ML Model and Data Supply Chain Security

You are building a supply-chain assurance program for AI/ML components used by [INSERT_ORGANIZATION] - foundation models, fine-tuned weights, datasets, embeddings, plugins, and open-source packages. 1. Governance & Authorization: Under the AI security policy, require approved intake for every external model, dataset, and plugin; shadow usage discovered on managed endpoints is assessed, not exploited. 2. Asset Inventory: Catalog models, weights, datasets, adapters, MCP servers/plugins, model APIs, with versions, owners, data lineage, and deployment environments. 3. Vendor & Provenance: Verify publisher identity, model cards, training-data disclosures, and licenses; prefer signed model packages and approved registries ([INSERT_MODEL_REGISTRY]). 4. Risk Process: Intake request -> static/reputation review -> sandbox evaluation -> license/compliance review -> approval with conditions -> continuous monitoring. 5. Technical Scanning: Scan model packages for pickle/deserialization risks, tampered files, embedded secrets, malware, known vulnerable ML dependencies, and corrupted or poisoned weights. 6. Dataset Assurance: Check datasets for poisoning indicators, duplicates, biased/abusive content, PII, and license restrictions; hash and version approved datasets. 7. Plugin/Tool Risk: Review MCP/plugin manifests, requested permissions, network egress, and OAuth scopes; allow-list and isolate third-party tools with least privilege. 8. Sandbox Validation: Run new models/plugins in an isolated sandbox with dummy data, monitor file/network behavior, and benchmark safety/output anomalies before promotion. 9. AI Bill of Materials: Maintain an AI-BOM (base model, datasets, adapters, dependencies) mapped to vulnerability feeds and security advisories. 10. Evidence & Compliance: Store approvals, scan reports, hash manifests, and vendor assessments in the GRC vault, aligned with [INSERT_REGULATION] and procurement standards. 11. Runtime Controls: Enforce signed artifacts, registry-only deployment, egress controls, resource quotas against model DoS/abuse, and integrity verification at load. 12. Remediation: On advisories or compromise, quarantine affected versions, rotate credentials, rebuild from signed sources, patch dependencies, and retire unsupported models.