AI Governance Readiness Checklist: Are You Compliant?

Implementation: Create an enterprise-wide AI governance charter that defines acceptable use, risk tolerance, and decision-making authority. Include specific guidelines for different AI risk categories as defined by applicable regulations.

Implementation: Form a committee including IT, legal, compliance, data science, and business stakeholders. Meet monthly to review AI initiatives, assess risks, and make governance decisions.

Implementation: Document all AI systems, their purposes, data sources, and risk levels. Update quarterly and ensure alignment with regulatory classification requirements.

Implementation: Develop communication plans for different stakeholder groups, including transparency reports, employee training programs, and customer notification procedures.

Implementation: Set aside 10-15% of your AI development budget for governance activities, including staff, tools, and external expertise.

Implementation: Use standardized risk assessment templates covering technical, operational, legal, and reputational risks. Assess risks before deployment and quarterly thereafter.

Implementation: Implement statistical testing for protected characteristics, use diverse test datasets, and establish bias correction procedures.

Implementation: Set up automated monitoring for model drift, accuracy degradation, and performance anomalies. Define alert thresholds and response procedures.

Implementation: Implement data validation pipelines, encryption for sensitive data, and access controls aligned with data classification policies.

Implementation: Conduct due diligence on AI vendors, review their governance practices, and include AI-specific clauses in vendor contracts.

Implementation: Create playbooks for AI malfunctions, bias discoveries, and regulatory inquiries. Include escalation procedures and communication templates.

Implementation: Establish principles covering fairness, transparency, accountability, and human oversight. Align with industry best practices and organizational values.

Implementation: Specify human-in-the-loop requirements for high-risk decisions, define override capabilities, and establish review procedures for automated decisions.

Implementation: Implement explainable AI techniques, document model logic, and create user-friendly explanations for automated decisions affecting individuals.

Implementation: Implement privacy-by-design principles, obtain explicit consent for AI processing of personal data, and provide opt-out mechanisms where legally required.

Implementation: Use MLOps tools to track model versions, maintain audit trails of changes, and implement approval workflows for model updates.

Implementation: Document model architecture, training data sources, performance metrics, and known limitations. Update documentation with each model revision.

Implementation: Create test suites covering functionality, performance, security, and bias. Require passing all tests before production deployment.

Implementation: Use role-based access control, multi-factor authentication, and principle of least privilege for AI system access.

Implementation: Log all AI decisions, inputs, outputs, and user interactions. Retain logs according to regulatory requirements and implement real-time monitoring.

Implementation: Track compliance with EU AI Act, local regulations, and industry standards. Maintain compliance dashboards and regular assessment reports.

Implementation: Schedule quarterly audits covering governance processes, technical controls, and compliance status. Use independent auditors when possible.

Implementation: Create monthly governance dashboards, quarterly compliance reports, and annual transparency reports for external stakeholders.

Implementation: Review governance effectiveness annually, incorporate regulatory updates, and implement lessons learned from incidents or audits.