TL;DR

AI risk management frameworks provide structured approaches to identifying, assessing, and mitigating AI-related risks. The NIST AI Risk Management Framework is the most comprehensive starting point. Effective risk management requires ongoing attention, not one-time assessment.

Why it matters

AI systems can cause significant harm: biased decisions, privacy violations, security breaches, and unintended behaviors. Systematic risk management helps organizations realize AI's benefits while controlling for potential harms. Regulators increasingly require demonstrated risk management practices.

The NIST AI Risk Management Framework

The NIST AI RMF is the most widely adopted framework, organized around four functions:

1. Govern

Establish organizational structures and policies:

Key activities:

  • Define roles and responsibilities
  • Establish risk tolerance thresholds
  • Create policies and procedures
  • Allocate resources
  • Build accountability mechanisms

Outputs:

  • AI governance charter
  • Risk appetite statements
  • Policy documentation
  • Organizational structure

2. Map

Understand the AI system and its context:

Key activities:

  • Document system purpose and capabilities
  • Identify stakeholders affected
  • Analyze deployment context
  • Catalog potential benefits and harms
  • Map interdependencies

Outputs:

  • System specification
  • Stakeholder analysis
  • Context assessment
  • Initial risk identification

3. Measure

Assess risks and track metrics:

Key activities:

  • Quantify identified risks
  • Test for failure modes
  • Measure fairness and bias
  • Evaluate security vulnerabilities
  • Track performance metrics

Outputs:

  • Risk assessments
  • Test results
  • Bias audits
  • Security assessments
  • Performance baselines

4. Manage

Take action on identified risks:

Key activities:

  • Prioritize risks
  • Implement mitigations
  • Monitor effectiveness
  • Plan incident response
  • Iterate and improve

Outputs:

  • Mitigation plans
  • Monitoring dashboards
  • Incident procedures
  • Improvement roadmap

AI risk categories

Reliability risks

System doesn't perform as intended:

  • Model drift over time
  • Edge case failures
  • Inconsistent outputs
  • Integration failures

Assessment: Performance testing, monitoring, stress testing

Fairness risks

System treats groups unfairly:

  • Biased training data
  • Discriminatory outcomes
  • Accessibility issues
  • Unequal error rates

Assessment: Bias audits, disparate impact analysis, accessibility testing

Privacy risks

System violates privacy:

  • Excessive data collection
  • Training data leakage
  • Re-identification risks
  • Unauthorized data use

Assessment: Privacy impact assessment, data flow mapping

Security risks

System is vulnerable to attack:

  • Adversarial manipulation
  • Model extraction
  • Data poisoning
  • API exploitation

Assessment: Penetration testing, threat modeling, red teaming

Transparency risks

System operations are opaque:

  • Unexplainable decisions
  • Hidden biases
  • Undisclosed AI use
  • Missing documentation

Assessment: Explainability testing, documentation review

Building your risk management process

Step 1: Establish governance

Before assessing specific systems:

Create:

  • Risk management policy
  • Roles (AI ethics officer, risk committee)
  • Decision-making framework
  • Escalation procedures

Define:

  • Risk appetite (what level of risk is acceptable)
  • Risk tolerance (thresholds for action)
  • Reporting requirements
  • Review cadence

Step 2: Inventory AI systems

Know what you have:

Information Why it matters
System name/purpose Basic identification
Data used Privacy and bias risks
Decisions made Impact assessment
Users/affected parties Stakeholder identification
Deployment context Contextual risks

Step 3: Prioritize assessments

You can't assess everything deeply. Prioritize by:

  • Impact severity (high-stakes decisions first)
  • Scale (systems affecting more people)
  • Regulatory requirements
  • Organizational risk appetite

Step 4: Conduct assessments

For each prioritized system:

Document:

  • Intended use and capabilities
  • Training data and methodology
  • Known limitations
  • Testing performed

Assess:

  • Each risk category
  • Likelihood and severity
  • Existing controls
  • Residual risk

Step 5: Implement mitigations

Address unacceptable risks:

Options:

  • Technical controls (guardrails, monitoring)
  • Process controls (human review, approval)
  • Organizational controls (training, policies)
  • Risk transfer (insurance, contracts)
  • Risk avoidance (not deploying)

Step 6: Monitor and iterate

Risk management is ongoing:

  • Track risk metrics
  • Review incidents
  • Update assessments
  • Improve processes

Risk assessment template

For each AI system:

System: [Name]
Purpose: [Description]
Risk category: [Low/Medium/High]

Risk: [Description]
Likelihood: [1-5]
Impact: [1-5]
Score: [Likelihood Ɨ Impact]
Existing controls: [List]
Residual risk: [Low/Medium/High]
Mitigation plan: [Actions]
Owner: [Name]
Review date: [Date]

Common mistakes

Mistake Problem Solution
One-time assessment Risks change over time Ongoing monitoring
Technical-only focus Missing organizational risks Holistic approach
No prioritization Resources spread thin Risk-based prioritization
Paper exercise Doesn't change behavior Integrate into operations
No ownership Accountability gaps Clear roles and responsibilities

Integrating with existing processes

AI risk management should connect to:

  • Enterprise risk management
  • Information security programs
  • Privacy programs
  • Change management
  • Incident management
  • Vendor management

What's next

Build comprehensive AI governance: