Handling Sensitive Outputs: Review, Redaction, Storage
AI outputs can create new risk: they may include sensitive data, infer privileged strategy, or confidently introduce false facts. Treat outputs like draft work product that needs review controls.
Output handling workflow
- Review for accuracy: verify facts, citations, numbers, and assumptions.
- Review for confidentiality: confirm the output did not reveal sensitive or privileged information.
- Redact if needed: remove identifiers or privileged content before sharing.
- Store safely: save outputs only in approved systems with appropriate access controls.
- Label clearly: “Draft—AI Assisted—Requires Attorney Review” for high-risk work.
Common “output risks” to look for
- Fabricated authority: cases/statutes that do not exist or do not say what the draft claims.
- Over‑generalization: “always/never” statements that are not legally accurate.
- Hidden assumptions: missing facts, incorrect jurisdictions, or outdated law.
- Confidentiality drift: output includes personal data that was not in the input (inference) or repeats identifiers you intended to remove.
Storage & retention (practical policies)
- Store outputs in matter systems, not personal drives or tool chat histories.
- Prefer tools with configurable retention and audit logs.
- Maintain an “AI work product” folder structure with versioning and reviewer sign‑off.
- Do not store sensitive prompts/outputs in systems without access controls and encryption.
Redaction guidance (simple rule set)
- Remove client identifiers before sharing drafts outside the core team.
- For training examples, use synthetic facts or heavily anonymized excerpts.
- Never use real privileged strategy in demos, marketing, or public AI tools.
Practice tip: “Draft” does not mean “safe.” A draft that leaves the team can still cause disclosure or reliance harm.