Risk Assessment Process Overview
AI risk assessment is a systematic process to identify, analyze, evaluate, and treat risks associated with AI systems. It forms the core of the NIST AI RMF's Map and Measure functions and supports ISO 42001 requirements.
💡 Continuous Process
AI risk assessment is not a one-time activity. It must be conducted initially during development, updated at deployment, and repeated periodically throughout the AI system lifecycle as the system, data, and context evolve.
Step 1: Risk Identification
Systematically identify all potential risks that could arise from the AI system throughout its lifecycle.
Identification Techniques
- Threat Modeling: Analyze potential attack vectors and misuse scenarios
- Failure Mode Analysis: Consider how the AI system could fail technically
- Stakeholder Analysis: Identify impacts on different affected groups
- Regulatory Mapping: Map compliance requirements to potential violations
- Historical Analysis: Review incidents from similar AI systems
- Expert Elicitation: Gather input from domain and technical experts
- Red Teaming: Adversarial testing to discover vulnerabilities
Documentation Requirements
For each identified risk, document:
- Risk description and category (technical, operational, legal, etc.)
- Potential causes and contributing factors
- Affected stakeholders and assets
- Current controls or mitigations in place
- Risk owner (accountable individual)
Step 2: Likelihood Assessment
Assess the likelihood that each identified risk will materialize.
Likelihood Factors to Consider
- Technical Maturity: How proven and stable is the AI technology?
- Data Quality: How reliable and representative is the training data?
- Deployment Context: How controlled is the operational environment?
- User Population: How sophisticated and diverse are the users?
- Threat Landscape: How attractive is the system to malicious actors?
- Control Effectiveness: How effective are existing safeguards?
Likelihood Scale
| Level | Description | Frequency |
|---|
| 1 - Rare | Exceptional circumstances only | Less than once per 5 years |
| 2 - Unlikely | Could occur but not expected | Once every 2-5 years |
| 3 - Possible | Might occur at some time | Once every 1-2 years |
| 4 - Likely | Will probably occur | Several times per year |
| 5 - Almost Certain | Expected to occur regularly | Monthly or more frequent |
Step 3: Impact Assessment
Assess the severity of impact if the risk materializes across multiple dimensions.
Impact Dimensions
- Human Impact: Physical safety, mental health, autonomy, dignity
- Rights Impact: Privacy, equality, freedom of expression, due process
- Financial Impact: Direct costs, fines, lost revenue, remediation
- Operational Impact: Business disruption, process failures
- Reputational Impact: Brand damage, stakeholder trust
- Strategic Impact: Market position, competitive advantage
Impact Scale
| Level | Description | Example |
|---|
| 1 - Insignificant | Minimal impact, easily absorbed | Minor inconvenience to users |
| 2 - Minor | Limited impact, manageable | Temporary service degradation |
| 3 - Moderate | Significant impact requiring response | Customer complaints, minor regulatory attention |
| 4 - Major | Serious impact threatening objectives | Regulatory investigation, significant financial loss |
| 5 - Catastrophic | Severe impact threatening viability | Loss of life, existential business threat |
Step 4: Risk Evaluation
Combine likelihood and impact assessments to determine overall risk level and prioritization.
Risk Matrix
Almost Certain
Medium
High
High
Critical
Critical
Likely
Low
Medium
High
High
Critical
Possible
Low
Medium
Medium
High
High
Unlikely
Low
Low
Medium
Medium
High
Rare
Low
Low
Low
Medium
Medium
Step 5: Risk Treatment
Choose and implement appropriate strategies to address identified risks.
Treatment Options
🚫
Avoid
Eliminate risk by not proceeding with activity
📉
Mitigate
Reduce likelihood or impact through controls
🔁
Transfer
Share risk through insurance or contracts
✅
Accept
Accept residual risk within tolerance
AI-Specific Mitigation Controls
- Technical: Bias testing, robustness testing, uncertainty quantification, adversarial training
- Process: Human-in-the-loop, staged rollout, A/B testing, canary deployments
- Governance: Ethics review, risk committee approval, independent audit
- Monitoring: Performance tracking, drift detection, feedback loops