Secure AI Supply Chain Risks Best Practices
This workshop helps leaders understand how supply chain risk shows up in GenAI—from third-party tools and pretrained components to vendors, distribution channels, and ongoing dependencies. You’ll learn practical best practices for evaluating provenance, strengthening oversight, and building repeatable guardrails that help teams move faster with fewer surprises.
Leave with a clear understanding of AI supply chain risk best practices and prioritized next steps.
GenAI supply chains are broader and less visible than traditional software supply chains—making oversight harder as adoption expands.
- Hidden third-party exposure: Teams adopt external capabilities quickly, without a consistent view of what they rely on and what risks follow.
- Unclear provenance and trust: Leaders often lack practical standards for assessing where critical components come from and whether they’re dependable.
- Ongoing dependency drift: Vendor changes, component updates, and shifting terms can quietly change the risk profile after initial approval.
When supply chain risk isn’t actively managed, GenAI scale increases exposure.
We equip leaders with best practices and actionable steps to reduce GenAI supply chain risk while keeping delivery moving.
- Supply chain risk map: Establish a clear view of where third-party and dependency risk can enter GenAI initiatives across the organization.
- Provenance and trust criteria: Align on practical standards for evaluating the origin, reliability, and suitability of critical components.
- Vendor and open-source due diligence: Define what “good” looks like for assessment, contracting expectations, and ongoing assurance.
- Distribution and access safeguards: Clarify how to reduce exposure across channels where capabilities are accessed, shared, or embedded.
- Ongoing monitoring approach: Set expectations for how supply chain risk is reviewed over time as dependencies and vendors evolve.
- Understanding AI Supply Chain Risk Vectors
- Validating Provenance of Pretrained Models
- Managing Dependencies in AI Pipelines
- Securing APIs and Model Distribution Channels
- Auditing Vendors and Open Source Components
Develop a shared understanding of AI supply chain risks that matter most for GenAI initiatives
Define a prioritized set of next steps to strengthen due diligence, approvals, and ongoing oversight
Establish clear criteria for evaluating provenance, trustworthiness, and third-party dependency exposure
Adopt a practical approach for vendor and open-source review that supports consistency and defensibility
Create a monitoring outline for tracking supply chain risk as components, vendors, and terms change
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes