Building Your AI Risk Register: A Practical Framework

Traditional IT risk registers don't adequately capture AI-specific risks. After helping dozens of organizations build AI risk programs, I've developed a framework that actually works.

Why Traditional Risk Registers Fall Short

Standard IT risk registers focus on:

• Availability (will the system be up?)
• Confidentiality (will data be protected?)
• Integrity (will data be accurate?)

AI risks include these but add:

• Output accuracy and reliability
• Training data provenance
• Model behavior unpredictability
• Vendor AI usage by third parties
• Regulatory uncertainty

The AI Risk Register Framework

Risk Categories

**Data Risks**

• Sensitive data exposure to AI services
• Training data contamination
• PII in AI outputs
• Data residency violations

**Model Risks**

• Hallucination and inaccuracy
• Bias in outputs
• Prompt injection vulnerabilities
• Model extraction attacks

**Operational Risks**

• Shadow AI proliferation
• Vendor lock-in
• Service availability
• Cost overruns

**Compliance Risks**

• Regulatory violations (GDPR, HIPAA, etc.)
• Contractual breaches
• Intellectual property issues
• Audit findings

Risk Assessment Criteria

For each risk, assess:

• **Likelihood:** How probable is this risk materializing?
• **Impact:** What's the business impact if it does?
• **Velocity:** How quickly would impact occur?
• **Detectability:** How easily can we identify this risk?

Control Mapping

Map each risk to:

• **Preventive controls:** Stop the risk from materializing
• **Detective controls:** Identify when risk materializes
• **Corrective controls:** Respond to materialized risks

Maintaining the Register

Review frequency:

• Full review: Quarterly
• High risks: Monthly
• Emerging risks: Continuous

Trigger events requiring immediate review:

• New AI tool deployment
• Regulatory changes
• Security incidents
• Vendor changes

A risk register is only valuable if it's used. Keep it actionable, keep it current, and integrate it into your decision-making processes.

This article reflects research and analysis by the ZeroShare editorial team. Statistics and regulatory information are sourced from publicly available reports and should be verified for your specific use case. For details about our content and editorial practices, see our Terms of Service.