AI Risk Assessment Template for UK Organisations

Every AI initiative carries risk. The question isn't whether to accept risk—it's whether you understand it and have chosen it deliberately.

This guide provides a practical framework for assessing AI risks, along with templates you can adapt for your organisation.

Why AI Risk Assessment Matters

AI systems can fail in ways that traditional systems don't:

Unpredictable behaviour: Unlike rule-based systems, AI can produce unexpected outputs when encountering novel situations. Hidden biases: Training data biases can lead to unfair outcomes for certain groups. Opacity: Even experts may not fully understand why an AI system made a particular decision. Dynamic performance: AI systems can drift over time as data patterns change.

These characteristics demand a structured approach to risk.

The Risk Assessment Framework

Step 1: Define the System Scope

Before assessing risks, clearly define what you're assessing:

System purpose:

• What problem does this AI system solve?
• What decisions or actions does it support?
• Who uses it and who is affected by it?

System boundaries:

• What inputs does the system receive?
• What outputs does it produce?
• What other systems does it interact with?

Operational context:

• Where and when will it be used?
• What's the expected volume of decisions?
• What's the deployment timeline?

Step 2: Identify Stakeholders

Map everyone affected by the system:

Primary users: People who directly use the system Subjects: People about whom the system makes predictions or decisions Operators: People who maintain and operate the system Oversight bodies: Regulators, auditors, governance functions Broader society: Community members, public interest considerations

Step 3: Categorise the Risk Level

Use a tiered approach based on potential impact:

High Risk:

• Decisions affecting fundamental rights
• Significant financial impact on individuals
• Health and safety implications
• Regulated activities
• Vulnerable population involvement

Medium Risk:

• Internal business decisions
• Customer service automation
• Operational efficiency applications
• Non-critical analytics

Low Risk:

• Productivity tools
• Content recommendations
• Research and development
• Internal experimentation

Step 4: Conduct Impact Assessment

For each significant risk, assess:

Likelihood: How likely is this risk to materialise?

• Almost certain (>90%)
• Likely (60-90%)
• Possible (30-60%)
• Unlikely (10-30%)
• Rare (<10%)

Impact: If it occurs, how severe would the consequences be?

• Catastrophic: Existential threat to organisation, severe harm to individuals
• Major: Significant financial/reputational damage, substantial harm to individuals
• Moderate: Material impact requiring management attention
• Minor: Manageable impact with limited consequences
• Negligible: Minimal impact, easily absorbed

Velocity: How quickly would impacts materialise?

• Immediate: Impacts occur within hours
• Days: Impacts occur within a week
• Weeks: Impacts occur within a month
• Months: Impacts develop over quarters
• Years: Slow-developing consequences

Step 5: Evaluate Specific Risk Categories

#### Technical Risks

Model performance:

• What if accuracy degrades over time?
• What if the model fails on edge cases?
• What if performance varies across groups?

Data risks:

• What if training data is biased or unrepresentative?
• What if data quality degrades?
• What if data access is lost?

Integration risks:

• What if dependent systems change?
• What if integration fails?
• What if latency becomes unacceptable?

#### Ethical Risks Fairness:

• Could the system treat groups differently?
• Are there historical biases in training data?
• How will fairness be monitored?

Transparency:

• Can decisions be explained to affected parties?
• Is the use of AI appropriately disclosed?
• Can individuals understand how they're affected?

Autonomy:

• Does the system respect human agency?
• Are humans appropriately in the loop?
• Can individuals opt out?

#### Legal and Compliance Risks Data protection:

• Is there a lawful basis for processing?
• Are subject rights respected?
• Is a DPIA required?

Regulatory compliance:

• Are sector-specific regulations met?
• Is the EU AI Act relevant?
• Are there emerging requirements to consider?

Liability:

• Who is responsible if the system causes harm?
• Are contracts appropriately protective?
• Is insurance adequate?

#### Operational Risks Availability:

• What if the system becomes unavailable?
• What are the fallback procedures?
• What's the recovery time objective?

Security:

• Could the system be manipulated?
• Is data adequately protected?
• Could adversarial attacks succeed?

Dependency:

• What if the vendor fails?
• Is there excessive concentration?
• Can the system be replaced?

Step 6: Define Mitigations

For each significant risk, identify controls:

Preventive controls: Stop the risk from occurring

• Data quality processes
• Model validation procedures
• Access controls
• Training and awareness

Detective controls: Identify when risks materialise

• Monitoring and alerting
• Auditing and logging
• Performance dashboards
• Complaint tracking

Corrective controls: Respond when risks occur

• Incident response procedures
• Rollback capabilities
• Communication plans
• Remediation processes

Step 7: Document and Approve

Create a formal risk assessment document including:

• System description and scope
• Stakeholder analysis
• Risk categorisation
• Impact assessments for each significant risk
• Mitigation plans
• Residual risk acceptance
• Review schedule

Obtain appropriate approval based on risk level:

• High risk: Executive/board approval
• Medium risk: Senior management approval
• Low risk: Department head approval

Risk Assessment Templates

Quick Assessment (Low Risk Systems)

``markdown

AI System Quick Assessment

System Name: Owner: Date:

Purpose

What does this system do?

Data

What data does it use? Is personal data involved?

Decisions

What decisions does it inform or make?

Users

Who uses it? Who is affected?

Key Risks

1. [Risk description] - [Likelihood] / [Impact] 2. [Risk description] - [Likelihood] / [Impact]

Mitigations

1. [Control description] 2. [Control description]

Approval

Approved by: _________________ Date: _________ `

Standard Assessment (Medium Risk Systems)

`markdown

AI System Risk Assessment

1. System Overview

Name: Description: Owner: Developer: Go-live date:

2. Scope and Boundaries

Inputs: Outputs: Integrations: Users: Affected parties:

3. Risk Category

[ ] High Risk [x] Medium Risk [ ] Low Risk Rationale:

4. Technical Risks

| Risk | Description | Likelihood | Impact | Score | Mitigations | |------|-------------|------------|--------|-------|-------------| | Model accuracy | | | | | | | Data quality | | | | | | | Integration failure | | | | | |

5. Ethical Risks

| Risk | Description | Likelihood | Impact | Score | Mitigations | |------|-------------|------------|--------|-------|-------------| | Fairness | | | | | | | Transparency | | | | | | | Autonomy | | | | | |

6. Legal Risks

| Risk | Description | Likelihood | Impact | Score | Mitigations | |------|-------------|------------|--------|-------|-------------| | Data protection | | | | | | | Regulatory | | | | | | | Liability | | | | | |

7. Operational Risks

| Risk | Description | Likelihood | Impact | Score | Mitigations | |------|-------------|------------|--------|-------|-------------| | Availability | | | | | | | Security | | | | | | | Dependency | | | | | |

8. Residual Risk Statement

After mitigations, the residual risk is assessed as: [ ] Acceptable [ ] Acceptable with conditions [ ] Unacceptable - requires further work

9. Review Schedule

Next review date: Review trigger events:

10. Approval

Prepared by: _________________ Date: _________ Reviewed by: _________________ Date: _________ Approved by: _________________ Date: _________ ``

Ongoing Risk Management

Risk assessment isn't a one-time activity:

Regular reviews:

• Quarterly for high-risk systems
• Annually for medium-risk systems
• When significant changes occur

Trigger events requiring re-assessment:

• Significant model updates
• Data source changes
• Scope expansion
• Incident occurrence
• Regulatory changes

Monitoring:

• Track key risk indicators
• Monitor for emerging issues
• Report to appropriate governance forums

Common Mistakes to Avoid

Checkbox compliance: Going through the motions without genuine analysis. Take time to think through realistic scenarios. Over-optimistic assessments: Underestimating likelihood or impact. Get independent review. Ignoring residual risk: Assuming mitigations work perfectly. Track whether controls are effective. One-and-done: Completing assessment then never revisiting. Schedule regular reviews. Missing stakeholders: Only considering internal perspectives. Include those affected by the system.

Getting Started

If you're implementing AI risk assessment:

1. Start with high-risk systems: Focus governance effort where it matters most

2. Adapt templates: Modify frameworks to fit your organisation's context

3. Build capability: Train staff on risk assessment approaches

4. Integrate with existing governance: Link to enterprise risk management

5. Learn and improve: Refine processes based on experience

Risk assessment should enable AI adoption by making risks visible and manageable. Done well, it builds confidence that AI can be deployed responsibly.

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Related Reading

• AI Governance Framework for UK Enterprises — Build comprehensive AI governance
• AI Data Protection Guide for UK Organisations — Navigate privacy requirements
• How to Run an AI Pilot That Actually Scales — Assess risks during pilot design