When you embed a large language model or generative AI into your product or workflow, you're handing over your data to a third-party vendor—and that decision deserves scrutiny. The difference between a vendor with strong data governance and one without can mean the gap between compliance and breach, proprietary information locked in or leaked. Here's how to vet AI vendors before you sign.
Understand Data Residency and Retention Policies
Before signing any contract, ask where your data physically lives and how long it's kept. Some vendors process data in the US; others route through Europe, Asia, or multiple regions. For LLM integrations specifically, clarify whether:
- Data is stored after API calls complete or deleted immediately
- Training data and user data are separated
- Backups are encrypted and where they're held
- You can request deletion of historical data
If you're handling healthcare, financial, or PII-heavy content, data residency becomes legally binding. A vendor storing EU customer data in the US without adequacy agreements breaks GDPR. Request their Data Processing Agreement (DPA) upfront—if they don't have one templated, that's a red flag.
Check for SOC 2 Type II Certification
SOC 2 Type II is the industry standard for cloud service providers handling sensitive data. It covers security, availability, processing integrity, confidentiality, and privacy across a 6–12-month audit period. When evaluating AI vendors:
- Verify they hold current Type II certification (expired certs don't count)
- Review the actual audit report, not just a badge on their website
- Check the scope—does it cover the specific service you're buying, or just infrastructure?
- Look for notable exceptions flagged in the auditor's findings
Expect mature vendors (OpenAI's API, Anthropic, Cohere) to have this. Smaller boutique integrators often don't; that doesn't disqualify them, but it means requesting additional security documentation becomes mandatory.
Review API Encryption and Data Handling
Encryption in transit (TLS 1.2+) is table stakes. What matters more for LLM integrations:
- Encryption at rest: Are embeddings, fine-tuning data, and model parameters encrypted on disk?
- Key management: Who holds encryption keys? Customer-managed keys (CMEK) are stronger than vendor-managed.
- API logging and monitoring: Can the vendor see your prompts and outputs? Some vendors offer zero-logging options for enterprise contracts (typically 5–10% cost premium).
- Batch processing vs. real-time: Batch APIs often have stricter data handling than streaming endpoints.
If you're integrating a model that processes customer credit card numbers or health data, insist on zero-logging or request a contractual clause preventing vendor-side model training on your data.
Audit Their Vulnerability Disclosure and Incident Response
Ask for their:
- Vulnerability disclosure policy (where security researchers report bugs)
- Mean time to patch (MTTR) for critical vulnerabilities
- Historical incident reports (at least summaries of what happened and how it was resolved)
- Cyber insurance coverage amount and carrier
Reputable vendors publish this openly or share it under NDA. If they deflect or claim they've never had incidents, that's often more suspicious than transparency about past breaches they've handled well. Look for vendors with established security labs and regular third-party penetration testing.
Compare Pricing Models and Lock-In Risk
AI vendor pricing often hinges on data sensitivity and audit burden. Budget realistically:
- Standard API usage: $0.002–0.20 per 1K tokens (varies by model size)
- Enterprise contracts with zero-logging: Add 20–50% premium
- SOC 2 compliance support: $10K–50K annually as part of contract terms
- Custom DPA and data residency: Often requires $100K+ annual spend to negotiate
Avoid vendors with vague data handling terms buried in 50-page ToS. Transparent vendors break down exactly what they store, how long, and under what conditions. Use tools like Mercoly to compare multiple AI vendors' security posture and pricing side-by-side—it saves weeks of back-and-forth emails with sales teams.
Verify Subprocessor Transparency
Your vendor likely uses third-party services (cloud providers, security tools, analytics platforms). Ask for:
- A current list of all subprocessors
- Whether they allow you to object to new subprocessors
- Proof that subprocessors sign their own data processing agreements
Changes in subprocessors without notice is a common compliance violation. Vendors who update subprocessor lists monthly show maturity.
Frequently Asked Questions
Q: Can an AI vendor use my data to improve their model without explicit consent? Most cloud LLM APIs (OpenAI, Anthropic, Cohere) don't use API data for training by default, but they may retain it for safety monitoring unless you opt into a zero-logging plan. Always request and review their data retention addendum.
Q: How do I verify a vendor's SOC 2 report is legitimate? Request the audit report directly from the vendor under NDA—not just a certificate. Cross-check the auditing firm's credentials and call their office if in doubt; it takes 30 seconds and catches fraudulent claims.
Q: What's the typical timeline to negotiate custom data handling terms with an AI vendor? Expect 4–8 weeks for a mid-market contract negotiation. Enterprise deals with custom residency and logging restrictions can stretch 12+ weeks. Plan ahead if your launch date is fixed.
Compare AI vendors on security terms, audit certifications, and pricing in one place to accelerate your decision—then integrate with confidence.