BasicAgent
AI Risk Mitigation
AI risk mitigation strategies for LLM and ML systems, from controls to monitoring and rollback.
AI risk mitigation reduces the likelihood and impact of failures in production AI systems.
Common mitigation strategies
- evaluation gates before deployment
- prompt and model versioning
- human review for high-risk outputs
- rate limits and cost controls
- drift detection with alerts
Evidence to retain
- mitigation controls mapped to risks
- test and evaluation results
- incident logs and postmortems
Related pages
- AI risk management:
/ai-risk-management/ - AI risk management tools:
/ai-risk-management-tools/ - Operational risk mitigation strategies:
/operational-risk-mitigation-strategies/ - LLM observability:
/llm-observability-agent-workflows/
Create account
Build narrative
Follow a coherent path from thesis to lab notes to proof-of-work instead of isolated pages.
Step 1
Intelligence systems office
The strategic map for what is being built and why.
Step 2
Lab notes
Build footprints and progression logs as proof-of-work.
Step 3
Control surface
Governance and monitoring architecture for operational reliability.
Step 4
Private alignment
Convert insight into execution with scoped collaboration.