AI Governance Checklist - Gen AI Podcast

Purpose: Ensure your AI initiatives have appropriate oversight, controls, and accountability from conception through deployment and monitoring.

Use This For: Every AI project, before initial approval and at key milestones

Outcome: Clear governance framework that balances innovation speed with responsible AI practices

Review all checkpoints before AI project approval
Assign responsibility for each checkpoint (use RACI matrix)
Document evidence/status for each item
Review governance compliance at project milestones (design, development, pre-launch, post-launch)
Update this checklist as regulations and best practices evolve

Phase 1: Strategy & Planning Governance

✓	Checkpoint	Responsible Party	Status / Evidence
	Executive Sponsor Identified Senior leader accountable for project outcomes and governance
	Business Case Approved Clear ROI, strategic alignment, and success metrics documented
	Stakeholder Analysis Complete All impacted parties identified and engagement plan created
	Risk Assessment Conducted Technical, operational, reputational risks identified and mitigated
	Budget & Resources Allocated Full project costs approved including ongoing operations

✓	Checkpoint	Responsible Party	Status / Evidence
	Ethical Review Completed AI Ethics Committee (or equivalent) has reviewed and approved project
	Bias & Fairness Assessment Potential for discriminatory outcomes evaluated and mitigation planned
	Privacy Impact Assessment Data privacy risks evaluated per GDPR, CCPA, or applicable regulations
	Transparency Requirements Defined What will be disclosed to users about AI decision-making?
	Explainability Standards Set Can AI decisions be explained to users, regulators, auditors?
	Human Oversight Defined When and how will humans review/override AI decisions?
	Regulatory Compliance Verified Legal review confirms compliance with industry-specific regulations

✓	Checkpoint	Responsible Party	Status / Evidence
	Data Inventory Complete All data sources documented including origin, owner, refresh rate
	Data Quality Assessed Accuracy, completeness, consistency evaluated and acceptable
	Data Lineage Documented End-to-end data flow mapped and understood
	Data Access Controls Implemented Role-based access, encryption, audit logging in place
	Data Retention Policy Defined How long will training/operational data be retained?
	Third-Party Data Agreements Contracts allow AI use? IP ownership clear? Vendor security vetted?

✓	Checkpoint	Responsible Party	Status / Evidence
	Model Development Standards Code review, version control, documentation standards followed
	Model Performance Benchmarks Minimum accuracy, precision, recall thresholds defined and met
	Bias Testing Completed Model tested across demographic groups, edge cases, adversarial inputs
	Security Testing Passed Vulnerability assessment, penetration testing, model robustness verified
	User Acceptance Testing Business users validate model outputs and usability
	Failure Mode Analysis What happens when model fails? Graceful degradation planned?

All checkpoints above must be complete before production deployment.

✓	Checkpoint	Responsible Party	Status / Evidence
	Deployment Plan Approved Phased rollout, rollback plan, success criteria defined
	User Communication Complete Users informed about AI system, capabilities, limitations
	Training Materials Ready User guides, FAQs, training programs available
	Support Processes Established Helpdesk trained, escalation paths defined, feedback mechanism live
	Incident Response Plan Who responds to model failures, bias incidents, security breaches?

✓	Checkpoint	Responsible Party	Status / Evidence
	Model Performance Monitoring Accuracy, drift detection, anomaly detection automated and tracked
	Bias Monitoring Active Ongoing evaluation of fairness metrics across user segments
	User Feedback Collection Process for capturing and acting on user-reported issues
	Business Impact Measurement KPIs tracked, ROI calculated, value realization monitored
	Model Retraining Schedule When and how will model be updated? Version control in place?
	Audit Trail Maintained All decisions, changes, incidents logged for compliance/audit
	Governance Review Cadence Quarterly reviews with AI governance committee scheduled

Issue Type	Escalate To	Timeline
Model performance below threshold	AI Product Owner → CTO	Within 24 hours
Bias or fairness concern identified	AI Ethics Committee	Immediate
Security vulnerability discovered	CISO → Executive Leadership	Immediate
Regulatory compliance question	Legal / Compliance Officer	Within 48 hours
Data privacy incident	DPO → Legal → CEO	Immediate
Reputational risk (media, social)	Communications → CEO	Within 4 hours
Project budget overrun >20%	Executive Sponsor → CFO	Within 1 week

R = Responsible | A = Accountable | C = Consulted | I = Informed

Governance Activity	Exec Sponsor	Product Owner	Data Science	Legal/Compliance	IT/Security
Project approval	A	R	C	C	I
Ethical review	I	C	R	A	C
Model development	I	A	R	C	C
Security testing	I	C	C	C	A, R
Production deployment	A	R	C	I	R
Ongoing monitoring	I	A	R	C	R

Customize this matrix for your organization:

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