2.1 The "Three Lines of Defense" Model
The Three Lines of Defense (3LoD) model, originally developed for financial services risk management, provides a proven framework for structuring AI governance responsibilities across your organization. It ensures clear accountability while preventing gaps or overlaps in risk coverage.
The 3LoD model was formalized by the Institute of Internal Auditors (IIA) in 2013 and updated in 2020 to the "Three Lines Model" emphasizing value creation alongside protection. It's considered best practice across finance, aviation, healthcare, and is increasingly applied to AI governance.
Overview: The Three Lines
First Line
Development & Data Teams
Own & manage operational risk
Second Line
RAI Council & Risk Officers
Oversee & challenge risk management
Third Line
Internal Audit
Independent assurance
2.1.1 First Line: Development & Data Teams (Operational Risk)
The first line consists of those who build, deploy, and operate AI systems. They are the primary risk owners because they make day-to-day decisions that directly impact AI behavior and outcomes.
First Line Roles
- Data Scientists & ML Engineers: Build models, select algorithms, tune hyperparameters
- Data Engineers: Manage data pipelines, ensure data quality, maintain lineage
- Software Developers: Integrate AI into applications, implement guardrails
- Product Managers: Define use cases, set requirements, prioritize features
- DevOps/MLOps Engineers: Deploy, monitor, and maintain AI systems in production
- Business Analysts: Translate business needs, validate outcomes
First Line Responsibilities
| Activity | Description | Deliverables |
|---|---|---|
| Risk Identification | Identify potential biases, safety issues, and ethical concerns in design | Risk register entries, preliminary risk assessment |
| Control Implementation | Build safeguards into systems (guardrails, validation, monitoring) | Technical controls, test results, deployment checklists |
| Documentation | Create and maintain model cards, system documentation, audit trails | Model cards, technical docs, change logs |
| Testing & Validation | Execute fairness tests, adversarial testing, performance validation | Test reports, fairness metrics, validation results |
| Monitoring | Track model performance, detect drift, identify incidents | Monitoring dashboards, alert configurations, incident reports |
| Issue Escalation | Report concerns and incidents to second line | Escalation tickets, incident notifications |
First Line Controls for AI
Data Quality Checks
Automated validation of training data completeness, accuracy, and representation. Detect anomalies before they enter models.
Bias Testing in Pipeline
Integrated fairness metrics calculated during model training. Automated alerts when disparate impact thresholds are exceeded.
Code Review Standards
AI-specific code review checklist covering security, privacy, and ethical considerations alongside standard quality checks.
Deployment Gates
Automated checks preventing deployment without required documentation, test results, and approvals.
2.1.2 Second Line: RAI Council & Risk Officers (Oversight & Policy)
The second line provides expertise, support, and challenge to the first line. They don't build AI systems but set the standards, review compliance, and monitor overall risk posture.
Second Line Roles
- Responsible AI Council/Committee: Cross-functional body setting AI ethics policy and reviewing high-risk systems
- Chief AI Officer (CAIO): Executive accountable for AI strategy and governance
- AI Risk Officers: Specialists assessing and monitoring AI-specific risks
- Data Privacy Officers: Ensuring AI compliance with privacy regulations
- Legal/Compliance: Interpreting regulations, reviewing contracts, advising on liability
- Ethics Advisors: Providing ethical guidance on complex or novel use cases
Second Line Responsibilities
| Activity | Description | Deliverables |
|---|---|---|
| Policy Development | Create AI governance policies, standards, and procedures | AI Policy Manual, Acceptable Use Policy, Risk Taxonomy |
| Framework Management | Maintain and evolve the Responsible AI Framework | Framework documentation, templates, guidance |
| Risk Assessment Review | Review and challenge first-line risk assessments | Assessment reviews, recommendations, approvals |
| Monitoring & Reporting | Aggregate risk metrics, report to leadership | Risk dashboards, Board reports, trend analysis |
| Training & Awareness | Develop and deliver AI ethics training | Training materials, completion tracking |
| Regulatory Monitoring | Track evolving regulations, assess compliance gaps | Regulatory updates, compliance roadmaps |
RAI Council Structure
- Chair: CAIO or Chief Ethics Officer (decision authority)
- Core Members: Representatives from Legal, Risk, Privacy, Security, HR
- Technical Advisors: Senior data scientists, ML architects (non-voting)
- Business Representatives: Leaders from major AI-using business units
- External Advisors: Academic or civil society perspectives (for high-risk reviews)
- Secretary: Governance team member for documentation and coordination
Meeting Cadence: Monthly standing meetings + ad-hoc reviews for high-risk deployments
Second Line Decision Framework
| Risk Tier | Review Level | Approval Authority |
|---|---|---|
| Minimal | Self-assessment by first line | Model Owner |
| Limited | Second-line review of documentation | AI Risk Officer |
| High | Full RAI Council review | RAI Council vote |
| Prohibited | Automatic rejection | N/A (cannot proceed) |
2.1.3 Third Line: Internal Audit (Independent Assurance)
The third line provides independent, objective assurance on the effectiveness of governance and risk management. They report to the Board/Audit Committee, not to management, ensuring true independence.
Third Line Roles
- Internal Audit: Traditional audit function with AI-specific capabilities
- AI Audit Specialists: Technical auditors who can assess ML systems
- External Auditors: Third-party firms for independent assessments (especially for high-risk systems)
- Regulatory Examiners: External regulators conducting compliance reviews
Third Line Responsibilities
| Activity | Description | Deliverables |
|---|---|---|
| Framework Effectiveness | Assess whether RAI framework is properly designed and implemented | Framework audit report, gap analysis |
| Control Testing | Independently test first and second line controls | Control testing results, deficiency reports |
| Model Validation | Independent validation of high-risk AI models | Model validation reports, technical findings |
| Compliance Verification | Verify compliance with policies and regulations | Compliance audit reports |
| Thematic Reviews | Deep-dive audits on specific AI risk areas | Thematic audit reports (e.g., bias, security) |
| Board Reporting | Report findings directly to Board/Audit Committee | Board presentations, management letters |
AI Audit Competencies Needed
Most internal audit functions lack AI-specific expertise. Organizations should:
- Train existing auditors on AI fundamentals and risk assessment
- Hire or develop technical audit specialists with ML/data science backgrounds
- Partner with external firms for specialized technical audits
- Develop AI-specific audit methodologies and testing procedures
- Invest in AI audit tools for automated testing and monitoring
Audit Focus Areas for AI
- AI inventory completeness and accuracy
- Risk classification appropriateness
- Documentation quality (model cards, impact assessments)
- Control design and operating effectiveness
- Training data governance and provenance
- Bias testing methodology and results
- Model monitoring and drift detection
- Incident response and remediation
- Vendor oversight and due diligence
- Regulatory compliance (EU AI Act, sector-specific)
Making the Model Work
Common Implementation Challenges
| Challenge | Symptom | Solution |
|---|---|---|
| Silo Mentality | Lines don't communicate; duplicate or conflicting activities | Regular cross-line coordination meetings; shared platforms |
| First Line Resistance | Developers view governance as obstacle; bypass controls | Involve first line in policy development; automate where possible |
| Second Line Overload | Too many reviews; bottlenecks; rubber-stamping | Risk-based prioritization; delegate lower-risk reviews |
| Third Line Gaps | Auditors lack AI expertise; superficial reviews | Invest in training; use external specialists; develop AI audit tools |
| Information Asymmetry | Second/third lines only see what first line shows them | Independent data access; automated monitoring; surprise testing |
Keys to Success
Clear Mandates
Document each line's responsibilities explicitly. Avoid ambiguity that leads to gaps or overlaps.
Appropriate Resourcing
Each line needs adequate staff, skills, and tools. Under-resourced lines become checkbox exercises.
Executive Support
C-suite and Board must visibly support the model, empowering second and third lines to challenge.
Cultural Integration
Risk management seen as everyone's responsibility, not just "the risk team's job."
Implementation Steps
Map Current State
Document existing roles and responsibilities for AI governance. Identify gaps and overlaps against the 3LoD model.
Timeline: 2-3 weeks | Owner: AI Governance Lead
Design Target State
Define the desired 3LoD structure for your organization, including specific roles, reporting lines, and decision rights.
Timeline: 2-4 weeks | Owner: CAIO / Chief Risk Officer
Formalize the RAI Council
Establish the second-line council with charter, membership, meeting cadence, and decision authority. Secure executive sponsorship.
Timeline: 3-4 weeks | Owner: CAIO / Legal
Build Third Line Capability
Assess internal audit's AI readiness. Develop training plan, hiring needs, or external partnership strategy.
Timeline: 4-8 weeks | Owner: Chief Audit Executive
Communicate and Train
Roll out the 3LoD model organization-wide. Provide role-specific training on responsibilities and expectations.
Timeline: 4-6 weeks | Owner: HR / Communications