1. Where is your data stored?

• Clarify if user inputs or context data are stored for training, analytics, or model improvements.

• Ask: What are your data residency policies? Which regions host our inputs? Is data deleted after inference or reused for model training?

• The Cloud Security Alliance recommends full visibility into vendor data handling and retention practices before trusting them with sensitive inputs Mitratech.

2. What’s the usage-based pricing model?

• Understand granular pricing: is token usage billed per request, by volume, per endpoint, or hidden tiers?

• Ask: How do overage thresholds work? Are there volume discounts? Is billing transparent by call or customer?

• Without clarity, usage balloons and cost unpredictability erode business value.

3. Can you apply Role-Based Access Control (RBAC)?

• Does the vendor support RBAC or attribute-based access control for multi-tenant environments?

• Ask: Can we restrict only certain users or roles to LLM invocation or fine-tuning capability?

• Strong RBAC ensures internal data segmentation and least-privilege usage across teams.

4. What’s your model update cadence?

• Ask: How often do you upgrade model versions or release new weights or patch vulnerabilities?

• Knowing the cadence helps anticipate behavior shifts, deprecation notices, or model drift.

• Vendors should provide model cards and versioning guidance along with release notes Mitratech.

5. Do you offer private fine‑tuning?

• Check if fine-tuning is done on isolated infrastructure rather than shared clouds.

• Ask: Is the training data isolated? Are private models secured from cross-customer contamination? What’s the fine-tuning SLA?

• Shared training setups can leak sensitive prompts or PII—private fine-tuning ensures isolation.

6. Is your system compliant? (SOC 2, GDPR, etc.)

• Ask: Do you maintain SOC 2 Type II compliance? Are you certified for EU GDPR data handling? Do you support HIPAA/BCL impact scopes?

• SOC 2 Type II proves operational maturity in data protection; GDPR ensures lawful processing. Vendors lacking certifications introduce legal risk observe.ai.

7. How do you handle prompt injection risks?

• Ask: What measures are in place to detect direct or indirect prompt injections from user inputs or RAG sources?

• Wired and cybersecurity experts highlight that prompt injection remains a top LLM threat—especially when ingesting external documents or internet content WIRED.

• Vendors should describe sanitization pipelines, isolation zones, input validation, and architectural guardrails.

8. Do you log prompts?

• Ask: Do you retain prompt logs, response outputs, model version, and inference metadata? Are these accessible and queryable for audits?

• Prompt logging enables debugging, hallucination detection, cost attribution, and post-hoc investigation.

• Logging also supports compliance audits and incident tracing for sensitive use cases onetrust.com.

Summary Table

Why These Questions Protect Your Product

Deploying LLMs isn't just plug-and-play. Without clear policies, blind spots emerge:

• Untracked costs

• Data leakage

• Uncontrolled access

• Unintentional prompt manipulation

• Sudden model behavior changes

• Compliance failures

As CSA and privacy frameworks emphasize, third-party LLM vendor selection demands rigorous evaluation across security, governance, and transparency domains onetrust.com.

Final Thoughts

As AI gets embedded into core products, LLM vendor risks extend beyond accuracy—they span security, auditability, compliance, and operational clarity.

Before integrating a third-party model, evaluate it as you would any critical dependency:

• Is data properly handled?

• Are costs predictable?

• Can we segment access?

• Are behavior changes manageable?

• Do we control fine-tuning?

• Is there audit visibility?

• Is injection risk managed?

• Are logs captured for post-analysis?