Why you need this
Deploying AI systems without risk assessment is like launching software without security testing. Organizations face reputational damage, regulatory fines, data breaches, and AI-powered mistakes that harm customers—all preventable with systematic risk evaluation.
The problem: Teams rush to deploy AI without understanding the risks. They overlook data privacy issues, bias in outputs, security vulnerabilities, compliance requirements, and business continuity concerns until something goes wrong.
This template solves that. It provides a structured framework for identifying, evaluating, and mitigating AI-specific risks before deployment and throughout the AI lifecycle.
Perfect for:
- Risk and compliance officers evaluating AI systems
- Product managers launching AI-powered features
- Security teams assessing AI implementation risks
- Executives approving AI projects
What's inside
Comprehensive Risk Framework
Risk Categories:
- Technical risks: Model accuracy, hallucinations, data quality, system failures
- Security risks: Data breaches, prompt injection, adversarial attacks, unauthorized access
- Privacy risks: Data leakage, PII exposure, consent management, GDPR compliance
- Bias & fairness risks: Discriminatory outputs, demographic disparities, fairness violations
- Regulatory risks: Industry compliance (HIPAA, GDPR, SOC 2), AI-specific regulations
- Operational risks: Vendor dependency, cost overruns, integration failures, skill gaps
- Reputational risks: Public backlash, customer trust erosion, brand damage
For Each Risk Category:
- Identification: Specific risk scenarios relevant to your use case
- Likelihood assessment: Probability scoring (1-5)
- Impact assessment: Severity if realized (1-5)
- Current controls: What mitigations are in place
- Risk score calculation: Likelihood × Impact
- Mitigation strategies: Additional controls to reduce risk
- Owner assignment: Who's responsible for managing this risk
Risk Assessment Process:
Phase 1: Pre-Deployment Assessment
- Use case definition and scope
- Stakeholder identification
- Data flow mapping
- Threat modeling
- Initial risk inventory
Phase 2: Risk Evaluation
- Scoring methodology
- Risk prioritization matrix
- Risk appetite alignment
- Compliance gap analysis
- Cost-benefit analysis of mitigations
Phase 3: Mitigation Planning
- Control implementation roadmap
- Testing and validation plans
- Monitoring and alerting setup
- Incident response procedures
- Rollback and contingency plans
Phase 4: Ongoing Monitoring
- Quarterly risk reviews
- Incident tracking
- Control effectiveness testing
- Regulatory update monitoring
- Model drift and performance degradation detection
Assessment Templates:
Risk Register:
- Centralized risk tracking spreadsheet
- Status dashboards
- Trend analysis over time
- Audit trail for compliance
Mitigation Worksheets:
- Control selection frameworks
- Implementation checklists
- Verification procedures
- Documentation requirements
Stakeholder Communication:
- Executive summary templates
- Risk review meeting agendas
- Incident reporting formats
- Board-level risk presentations
Each Template Includes:
- ✓ Pre-filled common AI risks
- ✓ Scoring guidance and examples
- ✓ Industry-specific considerations
- ✓ Mitigation best practices
- ✓ Regulatory mapping
How to use it
- Before AI deployment — Complete initial risk assessment during planning phase
- Vendor evaluation — Assess third-party AI tool risks using the framework
- Quarterly reviews — Re-evaluate risks as AI systems evolve
- Incident response — Use risk register to inform response priorities
Example risk assessment
Risk: Data Privacy Violation
Scenario: Customer support AI accidentally exposes PII from one customer to another due to context window management error.
Likelihood: 3/5 (Medium) — Possible with inadequate input filtering
Impact: 5/5 (Critical) — GDPR violation, customer trust damage, fines
Risk Score: 15 (High priority)
Current Controls:
- Basic input sanitization
- Vendor SOC 2 certification
Gaps:
- No PII detection/redaction before AI processing
- No segregation of customer contexts
- Limited audit logging
Mitigation Plan:
- Implement PII detection and automatic redaction (Week 1)
- Add session isolation for all customer interactions (Week 2)
- Enable comprehensive audit logging (Week 1)
- Quarterly penetration testing (Ongoing)
Owner: Head of Security
Target Risk Score: 6 (Medium) after mitigations
Want to go deeper?
This template provides the framework. For understanding AI-specific risks:
- Guide: AI Safety Basics — Common AI failure modes and risks
- Glossary: Bias — Understanding and detecting AI bias
- Glossary: Hallucination — Why AI fabricates information
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Adapt for your organization's risk management processes
- Share with security, compliance, and governance teams
- Customize for industry-specific regulations
Just include this attribution:
"AI Risk Assessment Template" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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