Resource
AI Guardrails Checklist
A practical checklist for safer AI adoption, covering governance, data protection, and rollout enablement.
Checklist
- Define acceptable use and prohibited use cases.
- Set access levels and approval workflows.
- Document data sources and retention expectations.
- Assess vendor risk and contractual safeguards.
- Establish evaluation and monitoring routines.
- Design RAG patterns with traceable sources.
- Provide training, prompt standards, and feedback loops.
Governance focus areas
Data protection
- Data classification and sensitivity mapping
- Access controls and least-privilege rules
- Retention and deletion requirements
Evaluation and quality
- Quality and safety evaluation criteria
- Monitoring for drift and regressions
- Incident response and escalation path
RAG risks
- Source ownership and update cadence
- Grounding and citation practices
- Access boundaries for knowledge bases
Rollout enablement
- Training and enablement sessions
- Prompt standards and review routines
- Adoption metrics aligned to outcomes
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