5 engineering practices for handling untrusted AI outputs safely
goyalshaliniuk · x · 2026-09-27
A practical thread on safely handling AI outputs in production, built on one principle: treat AI output as untrusted input. Workflow: Generate → Validate → Verify → Approve → Execute.
- Validate before using: check required fields, data types, allowed values, formats, business rules.
- Verify with external sources: cross-check facts against search APIs, databases, official docs, internal knowledge bases.
- Sandbox AI actions: run code/commands/tool calls with least privilege (restricted APIs, read-only access, resource limits).
- Add human approval: require review for emails, financial actions, production changes, sensitive info, high-impact decisions.
- Monitor & log everything: track outputs, tool calls, validation failures, user feedback and actions taken.
The more powerful the AI system, the more important its guardrails.
Related event: Treat AI Output as Untrusted Input: Five Engineering Safeguards(2 posts)→
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