AI Risk Assessment Framework

Purpose: This framework provides a systematic approach to identifying, assessing, and mitigating risks associated with AI systems throughout their lifecycle. It is designed to help organizations meet regulatory requirements (EU AI Act, NIST AI RMF) while protecting stakeholders from potential harms.

1. Risk Assessment Process

Context Establishment

Define the AI system's purpose, stakeholders, operating environment, and organizational risk appetite. Document intended use cases and foreseeable misuse scenarios.

Risk Identification

Systematically identify risks across all categories: technical, operational, ethical, legal, reputational, and safety. Use structured techniques including FMEA, HAZOP, and stakeholder interviews.

Risk Analysis

Evaluate each risk's likelihood and potential impact. Consider both direct and indirect consequences, affected populations, and reversibility of harm.

Risk Evaluation

Prioritize risks using the risk matrix. Compare against organizational risk tolerance and regulatory thresholds to determine which risks require treatment.

Risk Treatment

Select and implement appropriate controls: avoid, mitigate, transfer, or accept. Document treatment plans with owners, timelines, and success criteria.

Monitor & Review

Continuously monitor risks and control effectiveness. Update assessments when the system, environment, or threat landscape changes.

2. Risk Matrix

Use this matrix to determine risk level based on likelihood and impact assessment:

Likelihood ↓ / Impact →	Negligible (1)	Minor (2)	Moderate (3)	Major (4)	Catastrophic (5)
Almost Certain (5)	Medium	High	High	Critical	Critical
Likely (4)	Low	Medium	High	High	Critical
Possible (3)	Low	Medium	Medium	High	High
Unlikely (2)	Low	Low	Medium	Medium	High
Rare (1)	Low	Low	Low	Medium	Medium

Risk Level Response Requirements:

Critical (20-25): Immediate action required. Halt deployment until mitigated. Board notification.
High (12-19): Priority treatment within 30 days. Senior management approval for deployment.
Medium (6-11): Treatment plan required within 90 days. Monitor closely.
Low (1-5): Accept with standard controls. Periodic review.

3. AI Risk Categories

🔒 Safety & Security Risks

Physical harm from autonomous systems
Adversarial attacks and model manipulation
Data poisoning and model theft
System failures causing downstream harm
Unauthorized access and misuse

⚖️ Fairness & Bias Risks

Discrimination against protected groups
Historical bias amplification
Unequal performance across demographics
Proxy discrimination through correlated features
Selection and sampling bias in training data

🔍 Transparency & Explainability Risks

Inability to explain decisions to affected individuals
Black-box systems in high-stakes contexts
Lack of auditability for regulatory compliance
Insufficient documentation of system behavior

🛡️ Privacy & Data Protection Risks

Unauthorized personal data processing
Re-identification of anonymized data
Inference of sensitive attributes
Non-compliance with data retention requirements
Cross-border data transfer violations

⚙️ Operational & Reliability Risks

Model drift and performance degradation
Integration failures with existing systems
Scalability limitations under load
Dependency on third-party services
Inadequate monitoring and alerting

📋 Compliance & Legal Risks

EU AI Act non-compliance
GDPR and data protection violations
Sector-specific regulation breaches
Intellectual property infringement
Contractual liability exposure

4. Impact Assessment Criteria

Level	Safety Impact	Financial Impact	Reputational Impact	Regulatory Impact
Catastrophic (5)	Loss of life or permanent injury	>$50M or bankruptcy risk	Sustained global negative coverage	License revocation, criminal liability
Major (4)	Serious injury requiring hospitalization	$10M - $50M	National media coverage, customer exodus	Major fines, enforcement action
Moderate (3)	Minor injury or significant distress	$1M - $10M	Industry coverage, stakeholder concerns	Warning letters, compliance orders
Minor (2)	Temporary discomfort	$100K - $1M	Social media criticism, some complaints	Informal regulatory inquiry
Negligible (1)	No health impact	<$100K	Minimal or no public awareness	No regulatory interest

5. Mitigation Strategies

Risk Category	Recommended Mitigations
Bias & Fairness	Diverse training data sourcing Regular fairness audits across demographics Bias testing in pre-deployment checklist Human review for high-stakes decisions
Safety & Security	Adversarial testing and red-teaming Input validation and anomaly detection Secure model serving infrastructure Incident response playbook
Privacy	Privacy-by-design implementation Differential privacy techniques Data minimization practices Consent management systems
Operational	Continuous monitoring dashboards Automated drift detection Rollback procedures SLA-based alerting
Compliance	Regulatory mapping and gap analysis Documentation automation Audit trail maintenance Legal review checkpoints

6. Risk Register Template

ID	Risk Description	Category	L	I	Score	Owner	Mitigation	Status
R-001	[Example: Model exhibits bias against protected group]	Fairness	3	4	12	[Name]	[Treatment plan]	Open
R-002	[Add risks...]