Set our LLM data retention policy now, or wait for an incident to force it?
Court orders can override vendor zero-data-retention policies indefinitely, while system prompts fail to prevent sensitive data exposure, leaving customer data vulnerable daily.
The question
We send customer data to multiple LLM providers daily — chat history, embeddings, RAG context, function-call inputs. Our retention policy today is the providers' defaults. The EU AI Act traceability obligations and our SOC2 audit are both pushing toward a documented policy. Do we author and enforce a unified policy now — before the next incident or audit forces it — and what should it commit to?
Counsel's position
Proactively author and enforce a unified, minimal LLM data retention policy now, committing to the shortest period consistent with business and legal needs.
Verdict
The verdict: Proactively author and enforce a unified, minimal LLM data retention policy now, committing to the shortest period consistent with business and legal needs.
Court orders can override vendor zero-data-retention policies indefinitely
Given your reliance on provider defaults, vendor promises of zero retention cannot guarantee data privacy against legal mandates.
System prompts fail to prevent sensitive data exposure to LLMs
Given your daily transmission of customer data, natural-language instructions cannot secure PII.
Traditional security stacks cannot detect voluntary employee AI data egress
Given your need for a documented policy, recognize that existing security controls will not prevent employees from pasting customer data into AI tools.
Persistent LLM memory enables cross-session poisoning without write-time validation
Given you send embeddings and chat history, long-term memory introduces new attack vectors that require strict governance.
Text-only LLM governance policies produce vacuous compliance in 27% of cases
Given your push toward a documented policy, natural-language rules alone will fail to constrain model behavior under stress.
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