AI Risk Management & Monitoring
AI systems introduce risks that traditional risk management frameworks were not designed to handle — model drift, adversarial inputs, hallucinations, data poisoning, and emergent behaviors. We help organizations build AI risk management programs that identify, assess, and mitigate these risks with clear policies, technical controls, monitoring, and incident response procedures that keep AI systems safe and reliable in production.
Overview
- AI risk assessment frameworks aligned to NIST AI RMF and organizational risk appetite
- Risk identification and categorization for AI systems across the deployment lifecycle
- Technical controls for model drift detection, input validation, and output monitoring
- Incident response planning for AI failures, adversarial attacks, and unexpected behavior
- Risk reporting dashboards and escalation workflows for leadership visibility