AI for Business Leaders > Module 4 > Lesson 15

AI Risk Assessment Framework

Implement a systematic framework for identifying, scoring, prioritizing, and mitigating AI risks across your organization.

🎯 Advanced 📋 Framework

From Ad Hoc to Systematic

Most companies approach AI risk reactively: Deploy technology → Something breaks → Scramble to fix. This is expensive, stressful, and dangerous.

What you need instead: A systematic framework that identifies risks before deployment, scores them objectively, prioritizes mitigation efforts, and monitors continuously. This lesson provides that framework—used by Fortune 500 companies and adaptable to any organization size.

💡 Framework Overview: 5 Phases
  1. Identification: What could go wrong? (Comprehensive risk catalog)
  2. Assessment: How bad could it be? (Likelihood × Impact scoring)
  3. Prioritization: What do we address first? (Risk matrix)
  4. Mitigation: How do we reduce risk? (Control implementation)
  5. Monitoring: Are we staying safe? (Continuous oversight)

Phase 1: Risk Identification

You can't manage risks you haven't identified. This phase requires structured brainstorming across multiple stakeholder groups—not just your tech team.

The 6 Risk Categories

Use this taxonomy to ensure comprehensive coverage. For each AI system, ask: "What could go wrong in each category?"

1. Technical Risks

2. Ethical & Bias Risks

3. Legal & Regulatory Risks

4. Security & Privacy Risks

5. Operational Risks

6. Strategic & Reputational Risks

⚠️ Don't Identify Risks in Isolation Form cross-functional risk identification workshops: Budget 4-8 hours per major AI system for comprehensive identification.

Phase 2: Risk Assessment (Scoring)

Not all risks are equal. Scoring helps you allocate resources rationally. Use a 2-dimensional framework: Likelihood × Impact = Risk Score.

Likelihood Scoring (1-5 Scale)

Score Level Definition Example
1 Rare May occur in exceptional circumstances (<5% probability) AI system causes physical injury to humans
2 Unlikely Could occur but not expected (5-25% probability) Chatbot provides dangerously incorrect medical advice
3 Possible Might occur at some point (25-50% probability) Demand forecasting model experiences concept drift
4 Likely Will probably occur in most circumstances (50-75% probability) Facial recognition shows higher error rates for some demographics
5 Almost Certain Expected to occur (>75% probability) EU AI Act compliance requirements will apply to your business

Impact Scoring (1-5 Scale)

Score Level Financial Impact Reputational Impact Operational Impact
1 Negligible <$100K No media attention, handled internally No disruption to operations
2 Minor $100K-$1M Local media coverage, customer complaints Brief service disruption (<4 hours)
3 Moderate $1M-$10M National media coverage, social media backlash Significant disruption (<24 hours)
4 Major $10M-$50M Sustained national media, brand damage, customer churn Major disruption (<1 week), key services offline
5 Catastrophic >$50M Congressional hearings, CEO resignation, existential threat Complete system failure, >1 week recovery
💡 Calculating Risk Score

Risk Score = Likelihood × Impact

This produces scores from 1 (lowest) to 25 (highest). Interpret as:

Example: Scoring a Hiring Algorithm

AI System: Automated resume screening for technical roles

Risk: Gender bias in candidate recommendations

Likelihood Assessment:

Impact Assessment:

Risk Score: 4 × 4 = 16 (HIGH)

Action Required: Mitigate within 90 days before full deployment. Conduct bias audit, acquire diverse training dataset, implement human review, establish appeal mechanism.

Phase 3: Risk Prioritization

You've identified 30+ risks across multiple AI systems. You can't address everything simultaneously—you need priorities. Use the Risk Matrix.

Risk Prioritization Matrix

Risk System Likelihood Impact Score Priority Owner
EU AI Act non-compliance All high-risk systems 5 4 20 P0 (Critical) Chief Legal Officer
Hiring algorithm bias Resume screening 4 4 16 P1 (High) Chief HR Officer
Customer data breach Recommendation engine 3 4 12 P1 (High) CISO
Forecast model drift Demand planning 4 3 12 P2 (Medium) VP Operations
Chatbot misinformation Customer service 3 2 6 P2 (Medium) VP Customer Success
Vendor lock-in Cloud AI platform 2 3 6 P3 (Low) CTO

Prioritization Rules:

Phase 4: Risk Mitigation

For each high-priority risk, develop a mitigation plan using the 4T framework: Terminate, Treat, Transfer, or Tolerate.

The 4T Risk Response Framework

1. TERMINATE (Eliminate the risk by not pursuing the activity)

When to use: Risk is unacceptably high and can't be mitigated cost-effectively.

Example: "We've assessed facial recognition for retail stores. Bias risk scores 20/25 (critical). Recommendation: TERMINATE this project. Deploy alternative loss prevention (security guards + traditional surveillance) instead."

Cost: Opportunity cost of not pursuing AI (but avoids downside risk)

2. TREAT (Reduce likelihood or impact through controls)

When to use: Risk is manageable with appropriate safeguards (most common approach).

Example: "Hiring algorithm bias scores 16/25 (high). Recommendation: TREAT with:

Cost: $500K mitigation investment vs. $10M+ unmitigated risk

3. TRANSFER (Shift risk to third party via contracts, insurance, or outsourcing)

When to use: Risk can be contractually transferred and third party has expertise to manage it.

Example: "Data breach risk scores 12/25 (medium). Recommendation: TRANSFER by:

Cost: Premium fees for secure hosting + $200K annual insurance vs. building security in-house

4. TOLERATE (Accept residual risk after mitigation or consciously accept low-priority risks)

When to use: Risk is low priority, mitigation cost exceeds potential loss, or residual risk remains after treatment.

Example: "Chatbot providing suboptimal product recommendations scores 4/25 (low). Recommendation: TOLERATE. Monitor customer satisfaction scores but don't invest in advanced NLP. Potential revenue loss ($200K) < mitigation cost ($500K+)."

Cost: Ongoing monitoring only (~$20K/year)

Mitigation Plan Template

Risk Mitigation Plan: [Risk Name]

Risk Description: [Clear description of what could go wrong]
Current Risk Score: [Likelihood × Impact = Score]
Mitigation Strategy: [Terminate / Treat / Transfer / Tolerate]
Specific Controls:
  • [Control 1 with target implementation date]
  • [Control 2 with target implementation date]
  • [Control 3 with target implementation date]
Residual Risk Score: [New likelihood × New impact = New score after mitigation]
Investment Required: [Upfront cost + annual ongoing]
Owner: [Executive responsible for execution]
Timeline: [Start date → Key milestones → Completion date]
Success Metrics: [How will we know mitigation is working?]
Monitoring Plan: [Frequency of review, KPIs tracked, escalation triggers]

Phase 5: Continuous Monitoring

Risk management isn't "set and forget." Threats evolve, new regulations emerge, and models degrade. You need continuous monitoring with automated alerts.

The 3-Tier Monitoring System

Tier 1: Real-Time Operational Monitoring (Automated)

Frequency: Continuous (24/7 automated monitoring)

What to Monitor:

Alert Triggers: Performance drops >5%, error rate >0.1%, anomaly detected

Response Time: Immediate (automated alerts to on-call engineers)

Tools: MLOps platforms (Datadog, Weights & Biases, AWS SageMaker Model Monitor)

Tier 2: Tactical Risk Review (Human)

Frequency: Monthly

What to Review:

Attendees: AI Risk Manager, product owners, compliance, security leads

Outcome: Updated risk register, adjustments to mitigation plans

Tier 3: Strategic Risk Review (Executive)

Frequency: Quarterly

What to Review:

Attendees: CEO/COO, Chief AI Officer, CFO, General Counsel, Chief Risk Officer

Outcome: Strategic adjustments to AI risk appetite, budget approvals, major go/no-go decisions

Key Risk Indicators (KRIs)

Track these metrics in your dashboard. When KRIs exceed thresholds, it's an early warning signal.

KRI Calculation Green Zone Yellow Zone Red Zone
Model Performance Drift % change in accuracy vs. baseline <3% decline 3-7% decline >7% decline
Bias Disparity Ratio Max group error rate / Min group error rate <1.2x 1.2-1.5x >1.5x
Incident Frequency AI-related incidents per month 0-1 2-3 >3
Compliance Gap Count Number of unaddressed regulatory requirements 0-2 3-5 >5
Risk Mitigation Budget Variance Actual spend vs. planned spend ±10% ±10-25% >25%
Mean Time to Incident Response Hours from detection to resolution <4 hours 4-12 hours >12 hours

Implementation Roadmap

Rolling out this framework takes time. Here's a realistic timeline for a mid-size company.

90-Day Risk Framework Implementation

Week 1-2: Foundation

Week 3-4: Identification

Week 5-6: Assessment

Week 7-8: Prioritization & Planning

Week 9-10: Executive Approval

Week 11-12: Monitoring Setup

Week 13+: Continuous Improvement

✅ Success Criteria: You've Implemented This Framework When...
  1. Proactive Risk Identification: New AI projects automatically trigger risk assessments before deployment
  2. Clear Accountability: Every high-priority risk has a named executive owner
  3. Objective Decision-Making: Risk scores (not politics) determine mitigation priorities
  4. Continuous Monitoring: Automated alerts catch problems before they become crises
  5. Executive Engagement: C-suite receives quarterly risk briefings and makes informed go/no-go decisions
  6. Budget Alignment: 15-25% of AI budget allocated to risk mitigation (not afterthought)
  7. Learning Organization: Incidents are analyzed, lessons documented, processes improved

Key Takeaways

✅ Remember These 7 Principles
  1. Systematic beats ad hoc: Structured frameworks catch risks that gut feel misses
  2. Cross-functional is essential: Tech teams alone miss business, legal, and ethical risks
  3. Quantify everything: Likelihood × Impact scoring enables rational prioritization
  4. You can't fix everything: Use 4T framework to focus resources on highest-impact mitigations
  5. Monitoring is not optional: Risks evolve—yesterday's safe system is tomorrow's crisis
  6. Executive ownership matters: Risk management fails without C-suite accountability
  7. Investment is insurance: 15-25% of AI budget for risk mitigation prevents 10x losses later
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📝 Knowledge Check

Test your understanding of AI risk assessment frameworks!

1. What is the purpose of a risk assessment framework for AI?

A) To prevent any AI usage
B) To systematically identify and mitigate AI-related risks
C) Frameworks are unnecessary
D) To slow down innovation

2. What should be included in an AI risk assessment?

A) Only technical risks
B) Only financial risks
C) Technical, ethical, legal, and operational risks
D) Risk assessment is optional

3. How often should AI risk assessments be conducted?

A) Continuously and at key project milestones
B) Only once at the beginning
C) Never, after initial deployment
D) Risk assessment is one-time only

4. Who should be involved in AI risk assessment?

A) Only data scientists
B) Only executives
C) Only legal teams
D) Cross-functional teams including technical, legal, and business stakeholders

5. What is risk mitigation in AI context?

A) Ignoring identified risks
B) Implementing controls and safeguards to reduce risk impact
C) Accepting all risks without action
D) Mitigation is unnecessary
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