AI Vendor Risk Assessment and Third-Party AI Due Diligence

What is an AI vendor risk assessment?

An AI vendor risk assessment is a structured evaluation of how a third-party AI provider handles data privacy, model governance, security, compliance, auditability, agent and tool permissions, and cost, performed before the tool is approved for enterprise use. Unlike standard vendor reviews, it must address AI-specific risks: prompt and output data retention, model behavior, hallucination, autonomous actions, and downstream model dependencies.

Why traditional vendor reviews fail for AI

A SOC 2 report and a security questionnaire tell you how a vendor protects infrastructure, not how its models behave. AI vendors introduce risks that only appear at inference time:

• Data exposure: prompts containing PII or confidential data retained or used for training
• Model opacity: third-party and fourth-party model dependencies the vendor doesn't disclose
• Agentic risk: tools that take autonomous actions, API calls, data access, workflow triggers
• Compliance drift: vendor model updates that silently change behavior after approval
• Cost risk: unbounded token consumption with no enterprise-side controls

What questions should you ask AI vendors?

Before approving any AI vendor, get documented answers to: Which models (and model versions) power the product? Where is customer data processed, and are prompts retained or used for training? What audit logs are exposed to the customer? Can the enterprise enforce its own policies at runtime? What permissions do agents and tools hold, and can they be scoped? Which compliance frameworks (HIPAA, GDPR, EU AI Act, NIST AI RMF) does the vendor support with evidence?

How Trussed AI strengthens third-party AI due diligence

• Governance Advisory, define vendor review criteria, approval workflows, and risk tiers so every AI tool is assessed consistently before production use.
• AI Control Plane, apply your policies to third-party AI traffic through a drop-in proxy, without code changes to the vendor's product or your systems.
• Audit Assurance, generate continuous evidence: every interaction logged with policy results, model versions, timestamps, and data lineage.
• Agentic Governance, authorize vendor agents' tool calls, data access, and workflow triggers before execution.
• Cost Governance, attribute vendor AI spend by team and workflow, with budget thresholds and hard stops.
• Risk Monitoring, keep visibility into vendor behavior and compliance posture after onboarding, so due diligence doesn't end at signature.

Why Trussed AI

Due diligence answers "should we approve this vendor?" Trussed answers the harder question: "is the vendor still behaving as approved?" Because the control plane sits in the flow of AI interactions, your approval criteria become enforced runtime policy, with sub-20ms added latency and audit trails produced automatically as a byproduct of every governed interaction.

Frequently Asked Questions

How is AI vendor due diligence different from standard vendor risk management? Standard reviews assess the vendor's organization and infrastructure. AI due diligence also assesses model behavior, data handling in prompts and outputs, agent permissions, and the vendor's own model supply chain, risks that only materialize at runtime.

Can Trussed govern AI vendors we don't control? Yes. Trussed deploys as a proxy between your users and third-party AI tools, so your policies, logging, and cost controls apply even when you can't change the vendor's code.

How often should AI vendor assessments be repeated? Continuously, not annually. Model updates change vendor behavior without notice; runtime monitoring turns point-in-time approval into ongoing assurance.

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