AI Risk Assessment Framework 2026

Comprehensive guide to ai risk assessment framework for enterprise AI governance.

AI Risk Categories

AI systems face distinct risks beyond traditional software: accuracy risks (wrong decisions), bias risks (discrimination), security risks (adversarial attacks), privacy risks (data exposure), legal risks (compliance violations), and reputational risks (trust erosion).

Risk Taxonomy

Accuracy: Model produces wrong results affecting decisions
Bias: Model discriminates against protected groups
Security: Adversarial attacks or model poisoning
Privacy: Personal data exposure or model inversion
Legal: Regulatory violations or compliance gaps
Reputational: Loss of trust from AI failures

Assessment Methodology

Identify AI systems, classify by risk level (high/medium/low), assess each risk category, rate likelihood and impact, document mitigations, prioritize remediation. Review quarterly.

Risk Register Template

Create spreadsheet tracking: System name, risk category, description, likelihood (1-5), impact (1-5), risk score (L×I), mitigation, responsible party, status. Update as risks evolve.

Implementation Checklist

Action Items

Review current AI deployment against this framework
Identify compliance gaps
Develop remediation timeline
Assign ownership for compliance
Schedule quarterly review

Compliance is an ongoing process, not a one-time effort. Regular review and updates ensure your AI systems remain compliant as regulations and technology evolve.

Back to Compliance Pillar