AI Governance Across the AI Lifecycle
Seven stages. Three pillars. The governance that builds trust, enables adoption, and scales AI.
Industry-aligned lifecycle (NIST AI RMF, ISO 42001, EU AI Act) mapped to AI Readi's discovery model and operational governance requirements.
Concept & Planning
Define use case viability, align stakeholders, and classify risk level before committing resources
- Identify highest-impact use cases aligned to business objectives
- Align stakeholders early on purpose, scope, and success criteria
- Assess feasibility before committing resources
- Screen for regulatory risk (EU AI Act risk classification)
- Assess data availability and readiness before proceeding
- Document initial governance requirements and constraints
Stakeholder Alignment
EU AI Act Risk Classification
Discovery Model — Continuous Across All Stages
What type of accountability matters for the use case?
Can we break down the problem to determine accountability for use cases?
Operational Elements
- Business sponsor identified and accountable
- Domain experts engaged from relevant functions
- Compliance officer consulted for risk classification
- Data steward assesses data landscape
- Viability assessment framework applied
- Business case documented with expected outcomes
- Risk classification completed (EU AI Act)
- EU AI Act risk classification (Unacceptable / High / Limited / Minimal)
- NIST AI RMF: MAP function — context and stakeholders identified
- ISO 42001: Clause 4 — context of the organization
Data Acquisition
Assess data quality, diversity, and semantic alignment — the foundation that every later stage depends on
- Discover unique data assets and combination opportunities
- Build semantic alignment across teams before it becomes a blocker
- Establish data provenance and lineage from the start
- Detect bias in source data before it cascades through training
- Verify data rights, privacy compliance, and consent
- Document lineage for audit trails and reproducibility
Data Quality Assessment
Semantic Alignment Check
Discovery Model — Continuous Across All Stages
Do different teams use the same data fields to represent different business concepts?
Is there a shared, explicit definition of what each key data element means in each domain context?
Operational Elements
- Data engineers responsible for acquisition and preparation
- Domain experts validate data meaning in business context
- Data stewards enforce quality standards and access policies
- Bias assessment in source data populations
- Demographic representation analysis
- Data diversity gaps identified and documented
- Privacy exposure from combined datasets
- Incomplete or unrepresentative data risks
- Single-source dependency risks
Governance enables trust
Without governance, organizations cannot build the trust needed for AI adoption. The information gap between teams creates invisible blockers.
63%
lack formal governance
74%
blocked by governance gaps
Development
Design model architecture, select algorithms, train, and document the trade-offs that stakeholders need to understand
- Select optimal algorithms informed by business requirements
- Explore ensemble approaches for better generalization
- Build explainability into the model from the start
- Document architecture decisions and rationale
- Map compliance requirements to model capabilities
- Establish version control and rollback capability
Model Documentation Card
Trade-off Analysis
Decision: Prioritize accuracy over explainability for this use case (documented rationale in ADR-047)
Discovery Model — Continuous Across All Stages
Do we need to make trade-offs (e.g., accuracy vs. explainability)?
Are models documented?
Operational Elements
- Model documentation card maintained throughout development
- Code review and version control workflows
- Architectural decision records (ADRs) for significant choices
- ML experiment tracking platforms
- Compute resources for training
- Model registry and version control
- Baseline performance metrics established
- Success criteria defined with business stakeholders
- Training convergence and stability monitored
Verification & Validation
Validate performance, detect bias, test edge cases, and confirm the model behaves as all stakeholders expect
- Validate against real operational conditions, not just test sets
- Define acceptable output variance for non-deterministic models
- Build confidence across business, operations, and technology
- Bias testing across demographic groups and edge cases
- Adversarial robustness testing before production exposure
- Document limitations and define human-in-the-loop thresholds
Validation Matrix
Performance
Bias & Fairness
Reproducibility
Discovery Model — Continuous Across All Stages
Is there a defined range of expected output variance, and are deviations outside that range monitored?
How are anomalies handled?
Operational Elements
- Adversarial attack simulation
- Model robustness testing
- Input validation stress tests
- Bias testing across protected groups
- Fairness metrics evaluated (demographic parity, equal opportunity)
- Societal impact assessment completed
- Edge case failures documented with severity
- Acceptable variance bounds defined and monitored
- Human-in-the-loop thresholds set for uncertain outputs
Trust enables adoption
When teams trust the data, understand the algorithms, and know who owns what — adoption accelerates. Governance becomes the mechanism, not the obstacle.
34%
higher operating profit
3x
faster to production
Deployment
Integrate into production, establish monitoring, assign ownership, and define rollback procedures
- Accelerate deployment with pre-validated governance checklist
- Establish monitoring baselines from day one
- Clear ownership enables fast decisions when issues arise
- Rollback procedures documented and tested before go-live
- Monitoring alerts configured for drift, anomalies, and failures
- Escalation paths defined for every handover point
Deployment Readiness
4/6 completeOwnership Assignments
Discovery Model — Continuous Across All Stages
At each handover point in the AI lifecycle, is there a named owner rather than a committee or implicit assumption?
Are the specific elements each owner is responsible for documented — what they own, when they act, and what triggers their involvement?
Operational Elements
- Named deployment owner (individual, not committee)
- Operations handover with documented RACI
- Support team briefed on escalation procedures
- Staged rollout plan (canary → percentage → full)
- Monitoring dashboard setup and validation
- Rollback procedures tested in staging environment
- EU AI Act: placing on market / putting into service obligations
- Human oversight protocol activated
- ISO 42001: Annex A.6 — Deployment controls
Operations & Monitoring
Monitor performance, detect drift, handle incidents, and maintain alignment as business context evolves
- Continuous improvement through structured feedback loops
- Capture operational insights that improve model quality
- Early drift detection prevents costly downstream failures
- Drift detection across model, data, and business context
- Incident response with defined ownership and escalation
- Scheduled re-evaluation when business context changes
Drift Monitoring
Incident Log
Discovery Model — Continuous Across All Stages
When business context changes (new products, new markets, regulatory updates), does a defined trigger initiate a review?
Do we have a feedback loop to improve the algorithm and the model?
Operational Elements
- Feedback loop from users to model improvement pipeline
- Incident management with severity classification
- Scheduled re-evaluation cadence (monthly, quarterly)
- Model drift (accuracy degradation over time)
- Data drift (distribution shift in inputs)
- Context drift (business reality changes but model doesn't)
- Drift thresholds and alerting configured
- Retraining cadence based on drift severity
- SLA compliance monitoring and reporting
Adoption enables scale
Scaling AI requires governance mature enough for autonomous agents. Only a fraction of organizations are ready — the gap between deployment ambition and governance maturity is widening.
74%
deploying AI agents
21%
governance-mature
Retirement
Plan transition, manage data retention, decommission gracefully, and preserve knowledge for future systems
- Capture learnings and patterns for future AI systems
- Preserve reusable model components and training pipelines
- Inform successor system design with operational insights
- Data retention compliance with regulatory requirements
- Clean decommissioning with no orphaned dependencies
- Successor system readiness verified before sunset
Retirement Checklist
3/7 completeData Retention Schedule
Discovery Model — Continuous Across All Stages
If data is retained or aggregated, do we have access to the initial raw data?
If we open our data, how do we prove its trustworthiness?
Operational Elements
- Decommissioning owner named and accountable
- Successor system team briefed on transition
- Stakeholders notified of timeline and impact
- Transition plan with phased decommissioning
- Knowledge base documented for future reference
- Stakeholder communication plan executed
- Data retention policies applied per regulation
- Data disposal procedures for PII and sensitive data
- Audit trail archived for required retention period