AI System Risk Register

Systematic risk tracking and management for Gibraltar-compliant AI systems

Sample Risk Register

Risk ID	Description	Severity	Likelihood	Mitigation	Status
RISK-001	Model Bias in Credit Scoring	High	Medium	Regular bias audits, diverse training data, fairness metrics monitoring	Active
RISK-002	Data Privacy Breach	Critical	Low	Encryption at rest/transit, access controls, GDPR compliance framework	Mitigated
RISK-003	Model Drift Degrading Performance	Medium	High	Automated monitoring, retraining pipelines, performance alerts	Active
RISK-004	Adversarial Input Attacks	High	Medium	Input validation, anomaly detection, adversarial training	Active
RISK-005	Inadequate Human Oversight	Medium	Medium	Human-in-the-loop design, override mechanisms, operator training	Mitigated

Effective Risk Management Process

Identify

Systematically identify AI-related risks through stakeholder workshops and technical assessments

Assess

Evaluate severity and likelihood using standardized criteria and risk matrices

Mitigate

Implement controls and strategies to reduce risk exposure to acceptable levels

Monitor

Continuously track risk status and update register as conditions change

Need Help Building Your Risk Register?

Spring Software provides risk assessment services and customized risk register templates tailored to your AI system and Gibraltar regulatory requirements.

Get Risk Management Support

Risk Register FAQs

Common questions about AI risk registers and management

An AI risk register is a structured document that identifies, assesses, and tracks risks associated with AI systems. It includes risk descriptions, severity levels, likelihood assessments, mitigation strategies, and current status for each identified risk.

While not explicitly mandated by name, GFSC expects AI providers to demonstrate systematic risk management. A risk register is the industry-standard tool for documenting risk assessments and mitigation strategies required by regulators.

Update the risk register whenever new risks are identified, significant changes occur in existing risks, or mitigation strategies are implemented. Conduct formal quarterly reviews and comprehensive annual assessments.

Common severity classifications are: Critical (system failure/major harm), High (significant impact), Medium (moderate impact), and Low (minor impact). Define severity criteria specific to your AI system and organizational context.

Likelihood is typically assessed as High, Medium, or Low based on historical data, expert judgment, and environmental factors. Consider both the probability of occurrence and the frequency of exposure to the risk.

The risk register should be maintained by a designated risk manager or compliance officer, with input from AI developers, data scientists, legal teams, and business stakeholders. Senior management should review it regularly.

Showing 6 of 6 questions

Stay Ahead of the AI Curve

Join 2,000+ executives receiving our weekly insights on AI agents, automation trends, and implementation strategies.

No spam. Unsubscribe anytime.