If you are a compliance officer, risk manager, or AI governance lead at a financial institution, this playbook was built for you.
Financial institutions today face increasing regulatory scrutiny over the ethical and responsible use of artificial intelligence. Regulators demand transparency, accountability, and documented governance processes for AI systems, especially those impacting credit decisions, fraud detection, customer profiling, and market operations. Without a structured, standards-aligned approach, teams struggle to demonstrate compliance, manage socio-technical risks, and maintain consistency across AI initiatives. The absence of clear internal frameworks leads to fragmented controls, audit findings, and potential enforcement actions.
Engaging a Big-4 consultancy to design and implement an AI governance framework aligned with international standards typically costs between EUR 80,000 and EUR 250,000. Building an equivalent capability in-house requires dedicating 3 to 5 full-time staff members for 4 to 6 months, including legal, risk, compliance, and technical resources. This playbook delivers the same foundational structure, documentation, and assessment tools for $395 , a fraction of the cost and time.
What you get
| Phase | File Type | Description | Count |
| Foundation | AI Governance Maturity Assessment | 30-question diagnostic tool aligned to ISO/IEC 22989 domains, scored to identify gaps and readiness levels | 1 |
| Assessment | Domain Assessment: Organizational Governance | 30-question assessment covering leadership accountability, oversight structures, and policy enforcement | 1 |
| Domain Assessment: Risk Management | 30-question assessment evaluating risk identification, classification, mitigation strategies, and escalation protocols | 1 | |
| Domain Assessment: Data Lifecycle Management | 30-question assessment focused on data provenance, quality, bias detection, and retention policies | 1 | |
| Domain Assessment: Model Development & Validation | 30-question assessment covering model design, testing, documentation, and validation rigor | 1 | |
| Domain Assessment: Transparency & Explainability | 30-question assessment on disclosure practices, explainability methods, and stakeholder communication | 1 | |
| Domain Assessment: Human Oversight & Redress | 30-question assessment evaluating human-in-the-loop mechanisms, appeal processes, and intervention rights | 1 | |
| Domain Assessment: Monitoring & Incident Response | 30-question assessment on performance tracking, drift detection, anomaly reporting, and incident handling | 1 | |
| Implementation | Evidence Collection Runbook | Step-by-step guide for gathering and organizing evidence required for internal audits and regulatory reviews | 1 |
| Implementation | Audit Preparation Playbook | Checklist-driven process for preparing AI governance documentation ahead of internal or external audits | 1 |
| Implementation | RACI Matrix Templates | Pre-built responsibility assignment matrices for AI governance roles across business, legal, risk, and technical teams | 7 |
| Implementation | Work Breakdown Structure (WBS) Templates | Hierarchical task breakdowns for implementing each domain of ISO/IEC 22989 across project timelines | 7 |
| Integration | Cross-Framework Mappings | Detailed alignment tables mapping ISO/IEC 22989 controls to GDPR, DPDP, and NIST AI RMF requirements | 40 |
Domain assessments
Organizational Governance: Evaluates the existence and effectiveness of leadership accountability, governance bodies, and policy frameworks for AI oversight.
Risk Management: Assesses the institution's ability to identify, classify, and mitigate risks associated with AI deployment and operation.
Data Lifecycle Management: Reviews data sourcing, quality assurance, bias mitigation, and retention practices across the AI data pipeline.
Model Development & Validation: Measures the rigor of model design, testing, documentation, and independent validation processes.
Transparency & Explainability: Determines the level of disclosure, interpretability, and communication provided to stakeholders about AI systems.
Human Oversight & Redress: Examines mechanisms for human intervention, decision appeal, and corrective actions in AI-driven outcomes.
Monitoring & Incident Response: Tests the robustness of ongoing performance monitoring, anomaly detection, and response protocols for AI incidents.
What this saves you
| Activity | Without This Playbook | With This Playbook |
| Develop AI governance maturity assessment | 40+ hours of internal legal and risk team time to research, draft, and validate | Ready-to-use 30-question assessment aligned to ISO/IEC 22989 |
| Create domain-specific AI risk assessments | 6 to 8 weeks of cross-functional effort to define scope and questions | 7 pre-built 30-question assessments covering all core domains |
| Map ISO/IEC 22989 to GDPR, DPDP, and NIST AI RMF | 30+ hours of compliance analyst time to cross-reference controls | 40 mapping tables included, showing control equivalencies |
| Define roles and responsibilities (RACI) | Multiple workshops and iterations with stakeholders | 7 customizable RACI templates by domain |
| Prepare for AI governance audit | Reactive evidence gathering, often incomplete or inconsistent | Evidence runbook and audit prep playbook included |
Who this is for
- Compliance officers responsible for aligning AI initiatives with data protection and financial regulations
- Enterprise risk managers overseeing model risk and algorithmic accountability
- Chief AI officers or heads of AI governance establishing organizational frameworks
- Legal counsel advising on regulatory exposure from AI deployment
- Internal auditors evaluating AI governance maturity and control effectiveness
- Technology risk leads integrating AI oversight into IT governance structures
- Senior executives needing to demonstrate board-level accountability for AI systems
Cross-framework mappings
ISO/IEC 22989 to GDPR
ISO/IEC 22989 to India's Digital Personal Data Protection Act (DPDP)
ISO/IEC 22989 to NIST AI Risk Management Framework (AI RMF)
GDPR to NIST AI RMF (via ISO/IEC 22989 bridge)
DPDP to NIST AI RMF (via ISO/IEC 22989 bridge)
GDPR to DPDP (through shared AI governance principles)
What is NOT in this product
- Custom consulting services or one-on-one advisory support
- Software tools, platforms, or code for AI model monitoring or deployment
- Legal opinions or jurisdiction-specific regulatory interpretations
- Training sessions, webinars, or certification programs
- Updates or revisions to the playbook after purchase
- Support for non-financial sector use cases such as healthcare or education
- Translations of the playbook into languages other than English
Lifetime access and satisfaction guarantee
This playbook requires no subscription and does not rely on a login portal. After download, all files are yours to use, modify, and distribute internally. If this playbook does not save your team at least 100 hours of manual compliance work, email us for a full refund. No questions, no friction.
About the seller
The creator has spent 25 years developing structured compliance frameworks for regulated industries. They have analyzed 692 regulatory and standards frameworks and built 819,000+ cross-framework mappings to support consistent implementation. Their tools are used by over 40,000 practitioners across 160 countries, including compliance leads, risk officers, and legal advisors in financial services, insurance, and technology sectors.
>
