Managing third-party LLM and AI vendor risk

By Sam Rivera, Founder, SentinelPanda · June 19, 2026 · 1 min read · AI Governance

You probably buy your AI, not build it. That makes AI governance largely a vendor-management problem — with some sharp, AI-specific edges.

The reality: you consume AI

Outside of AI-first companies, most organisations use AI through third-party APIs (an LLM provider) and AI features embedded in SaaS tools, rather than training their own models. That makes AI governance largely an extension of vendor management — with some AI-specific risks bolted on.

The AI-specific diligence

• Data: what does the vendor do with your inputs — train on them, retain them, share them? Is there a no-training / no-retention option, and a DPA/BAA where needed?
• Behaviour and accountability: who is responsible when the AI output is wrong or harmful, and what guardrails does the vendor provide?
• Availability and lock-in: model deprecation, changes in behaviour between versions, and your dependency on a single provider.

Data is the sharpest edge

The risk that bites is data leaving your control into a model you do not govern — especially regulated data. Confirm the contractual data terms (no training on your data, retention limits) before sensitive data flows, and treat consumer-grade tools as unsafe for anything confidential.

Manage it as vendor risk

Bring AI vendors into your existing vendor-management program — inventory, tier by risk, diligence the high-risk ones, and review at renewal — with the AI-specific questions added. SentinelPanda tracks AI vendors alongside the rest of your third parties with the AI diligence captured.