AI-specific risk identification, assessment, treatment planning, and continuous monitoring
"Our credit scoring model was flagged by regulators for disparate impact. We never identified bias as a risk in our traditional risk assessment. Cost us $50M in fines and reputational damage."
Risk Type: Algorithmic bias (not in traditional risk frameworks)
"Our AI system performed poorly on certain demographics. We had cybersecurity risks documented but no fairness risk assessment. Patients were harmed before we discovered the issue."
Risk Type: Training data bias leading to safety issues
Complete AI risk management system
Facilitate risk identification sessions, document AI-specific risks
Score risks, prioritize treatment, create heat maps and reports
Document treatment decisions, assign ownership, launch monitoring
AI-native risk management vs generic IT risk frameworks
| Capability | Generic IT Risk Frameworks | TrustRail (RMF Playbook) |
|---|---|---|
| Risk Taxonomy |
❌ Generic IT risks (cybersecurity, availability) Miss AI-specific risks entirely |
✓ AI-native risk taxonomy Bias, drift, explainability, fairness, human oversight |
| Regulatory Alignment |
❌ Not mapped to EU AI Act Risk tiers don't match Article 6 classifications |
✓ EU AI Act Article 9 structured process Risk tiers map to minimal/limited/high-risk |
| Assessment Frequency |
❌ Annual risk assessments Can't detect model drift or bias emergence |
✓ Continuous monitoring integrated Real-time risk status updates |
| Control Library |
❌ NIST 800-53, CIS controls (IT-focused) No fairness or explainability controls |
✓ AI-specific control library Fairness testing, bias monitoring, human oversight |
| Documentation |
❌ Generic risk register Not structured for AI audits |
✓ EU AI Act Article 9 compliant documentation Auditor-ready from day 1 |
| Time to Deploy |
❌ 6-12 months to customize Need to build AI risk taxonomy from scratch |
✓ 4-6 weeks with professional services Pre-built AI risk framework ready to deploy |
Built on NIST AI RMF and EU AI Act requirements, not generic IT frameworks
Bias, drift, explainability, fairness - risks generic frameworks miss
Professional risk workshops, assessment facilitation, documentation
RMF Playbook guides comprehensive AI risk management process
Our RMF Playbook implements structured risk management:
Facilitate workshops to identify AI-specific risks using comprehensive taxonomy
Score likelihood and impact, calculate inherent and residual risk levels
Prioritize risks, create heat maps, determine regulatory exposure
Select controls, document treatment decisions, assign ownership
Track control effectiveness, re-assess risks, update treatment plans
Our RMF Playbook produces audit-ready documentation:
Complete catalog of identified risks with scores, ownership, status
Visual prioritization of risks by likelihood and impact
Documented risk treatment decisions with timelines and owners
Controls mapped to risks and regulatory requirements
💡 All documentation structured per EU AI Act Article 9 requirements
AI Risk Management Framework methodology
Sample Outputs: AI risk register, risk heat maps, treatment plans, control mapping, residual risk acceptance documentation
Choose the option that fits your needs