risk-management

name	risk-management
description	AI-specific risk management taxonomy, assessment method, controls, monitoring KPIs, and incident response aligned to enterprise risk frameworks.

Risk Management Skill

You help teams identify, assess, mitigate, monitor, and respond to AI-specific risks across the AI system lifecycle.

Important: You support risk management work but do not provide legal advice. Use this as a structured reference and adapt it to the organization risk appetite and sector obligations.

1. AI risk taxonomy

Use this taxonomy to structure risk identification and to populate a risk register.

Core AI risk categories:

• Bias and discrimination risk
  • Disparate impact, proxy variables, representation bias
• Hallucination and misinformation risk
  • Fabricated facts, unverifiable outputs, citation errors
• Privacy and data protection risk
  • Personal data exposure, re-identification, unlawful processing
• Security risk
  • Prompt injection, data exfiltration, model theft, supply chain compromise
• Safety risk
  • Physical or psychological harm, unsafe recommendations, misuse
• Accountability and governance risk
  • Unclear ownership, lack of approvals, missing audit trail
• Transparency and explainability risk
  • Inadequate user notices, inability to explain decisions, opacity of limitations
• Reliability and performance risk
  • Drift, degraded accuracy, brittleness, failure under edge cases
• Legal and regulatory risk
  • Misclassification under the AI Act, non-compliance with deployer duties, sector regulation
• Operational and business continuity risk
  • Vendor outages, dependency lock-in, lack of fallback

2. Risk assessment methodology for AI systems

2.1 Define the assessment unit

Assess at a useful granularity:

• System level (product or internal tool)
• Model level (foundation model, fine-tune, classifier)
• Feature level (a specific automated decision or recommendation)

2.2 Likelihood x impact matrix

Score each risk using likelihood and impact and document rationale.

Likelihood (1 to 5):

• 1 Remote
• 2 Unlikely
• 3 Possible
• 4 Likely
• 5 Almost certain

Impact (1 to 5) with AI specific criteria:

• 1 Negligible - minor user inconvenience, no sensitive data, no regulatory concern
• 2 Low - limited internal impact, reversible harm, minimal user exposure
• 3 Moderate - material user impact, potential complaints, measurable operational disruption
• 4 High - significant harm potential, sensitive data exposure, likely regulatory attention
• 5 Critical - severe harm, systemic discrimination, major breach, enforcement likely

Risk score = Likelihood x Impact

Risk bands (example):

• 1 to 4 - GREEN
• 5 to 9 - YELLOW
• 10 to 15 - ORANGE
• 16 to 25 - RED

2.3 Inherent vs residual risk

For each risk:

• Inherent risk: before controls
• Controls: preventive and detective measures
• Residual risk: after controls
• Acceptance: who accepts residual risk and under what conditions

2.4 AI Act linkage

When relevant, include:

• AI Act risk tier and obligation mapping
• Human oversight expectations
• Logging and traceability expectations
• Transparency duties

3. Standard control catalog for AI risks

Use this control catalog as a baseline. Select controls based on risk tier and context.

Governance and lifecycle controls:

• AI inventory entry with owner, purpose, and classification
• Risk assessment and DPIA triggers defined and followed
• Approval gates for procurement, launch, and major changes
• Document control, versioning, and evidence retention

Data and privacy controls:

• Data minimization and purpose limitation
• Dataset documentation (sources, licenses, quality checks)
• PII handling rules for prompts and outputs
• Access controls and secrets management
• Retention schedule and deletion workflows

Model and output quality controls:

• Pre-deployment evaluation plan and acceptance criteria
• Bias testing and subgroup analysis where appropriate
• Robustness testing and adversarial testing
• Guardrails: refusal rules, content filters, policy prompts
• Human in the loop review for sensitive decisions
• Explainability artifacts: model cards, limitations, confidence cues

Security controls:

• Threat modeling for LLM usage and AI pipelines
• Prompt injection defenses and output sanitization
• Isolation of tools and least privilege for agents
• Supply chain risk management for models and dependencies
• Incident detection for anomalous usage and exfiltration

Operational controls:

• Monitoring for drift, performance, and safety regressions
• Canary releases and rollback procedures
• Fallback modes and business continuity plans
• Vendor SLAs and exit plans

4. Monitoring and KPI framework for AI systems in production

Define KPIs by risk category and system purpose. Tie KPIs to alert thresholds and response actions.

Suggested KPI families:

• Quality and performance
  • Accuracy, precision, recall, calibration where applicable
  • Task success rate, user satisfaction, deflection rates
• Safety and misuse
  • Rate of policy violations, unsafe outputs, blocked attempts
  • Red team findings and closure rate
• Fairness
  • Disparity metrics by subgroup, equalized odds proxies, false positive disparity
• Reliability
  • Latency, timeouts, error rates, vendor outage minutes
• Security
  • Prompt injection attempts detected, tool abuse events, secrets exposure attempts
• Privacy
  • PII leakage rate, DSAR related incidents, retention policy exceptions
• Oversight
  • Human review rate, override rate, appeal rate, time to resolution

Monitoring operating model:

• Define owners for each KPI
• Define review cadence (daily, weekly, monthly)
• Define stop conditions and rollback criteria
• Maintain a monitoring report archive as evidence

5. Incident classification and response for AI-related incidents

5.1 Incident classes

Classify incidents to drive consistent response:

• Model behavior incident
  • Unsafe output, hallucination causing harm, policy violation
• Bias and fairness incident
  • Evidence of discriminatory outcomes
• Security incident
  • Prompt injection leading to unauthorized action, exfiltration, model theft
• Privacy incident
  • Personal data leakage, unlawful data use
• Compliance incident
  • Use outside approved scope, missing transparency notice, logging failure
• Vendor incident
  • Provider outage, breach, material change without notice

5.2 Severity tiers

Example severity tiers:

• SEV 1 - Critical harm, major breach, enforcement likely
• SEV 2 - Significant user harm potential or sensitive data exposure
• SEV 3 - Limited impact, contained, reversible
• SEV 4 - Near miss or low impact issue

5.3 Response playbook

For each incident:

• Detect and triage
• Contain and mitigate
• Preserve evidence (logs, prompts, outputs)
• Notify internal stakeholders (security, DPO, legal, business owner)
• Decide on external notifications where required
• Root cause analysis and corrective actions
• Post-incident review and control improvements

Maintain an incident register that links to:

• Timeline
• Impact assessment
• Communications
• Fixes and prevention

6. Connection to enterprise risk management frameworks

Position AI risk management as an extension of existing ERM.

ISO 31000 alignment:

• Establish context - AI scope, stakeholders, risk criteria
• Risk assessment - identify, analyze, evaluate
• Risk treatment - select controls, accept, avoid, transfer
• Communication and consultation - cross-functional governance
• Monitoring and review - KPIs, audits, management review

COSO alignment:

• Governance and culture - roles, accountability, ethics
• Strategy and objective setting - risk appetite for AI use
• Performance - risk identification, assessment, response
• Review and revision - incident learnings and drift monitoring
• Information, communication, reporting - evidence packs and board reporting

Practical integration tips:

• Use the same risk register format and scoring conventions where possible
• Add AI-specific categories, controls, and KPIs
• Ensure Board risk reporting includes AI systems and material incidents