If you are an Enterprise Risk Officer or AI Governance Lead at a global organization, this playbook was built for you.
As AI systems become embedded across operations, your team is under increasing pressure to define, measure, and govern AI-related risks within the existing enterprise risk management (ERM) structure. Regulatory scrutiny is intensifying, with new requirements emerging from financial, healthcare, and data protection authorities demanding documented risk assessments, accountability frameworks, and ongoing monitoring of AI deployments. You are expected to produce auditable evidence that AI risks are identified, classified, and managed in alignment with both technical standards and corporate governance expectations. Without a structured approach, teams face fragmented controls, inconsistent risk scoring, and reactive responses to audit findings.
Engaging a Big-4 consultancy to design and implement an AI governance framework typically costs between EUR 80,000 and EUR 250,000. Alternatively, dedicating internal resources requires at least 3 full-time equivalents over 4 to 6 months to research frameworks, build assessment tools, align with existing ERM processes, and prepare for audits. This comprehensive playbook delivers the same foundational structure, documentation, and implementation guidance for $395.
What you get
| Phase | Deliverable | File Count | Description |
| 1. Foundation | AI Risk Inventory and Use Case Classification Assessment | 1 | 30-question assessment to catalog active and planned AI use cases, classify by risk tier, and determine governance requirements. |
| 2. Domain Assessment | Domain 1: Governance | 1 | 30-question assessment covering policies, oversight structures, escalation pathways, and integration with ERM. |
| Domain 2: Risk Assessment | 1 | Evaluates processes for identifying, analyzing, and prioritizing AI risks across model lifecycle stages. | |
| Domain 3: Data Management | 1 | Assesses data quality, provenance, labeling integrity, and bias mitigation practices. | |
| Domain 4: Model Development | 1 | Reviews model design, documentation, testing protocols, and performance validation. | |
| Domain 5: Deployment & Monitoring | 1 | Examines change control, runtime monitoring, drift detection, and incident response. | |
| Domain 6: Human Oversight | 1 | Evaluates human-in-the-loop mechanisms, escalation procedures, and user training. | |
| Domain 7: External Transparency | 1 | Assesses disclosure practices, stakeholder communication, and third-party reporting obligations. | |
| 3. Operationalization | Evidence Collection Runbook | 1 | Step-by-step guide for gathering and organizing evidence required for internal audits and regulatory reviews. |
| 4. Audit Readiness | Audit Preparation Playbook | 1 | Checklist-driven process for preparing documentation, conducting mock audits, and responding to auditor inquiries. |
| 5. Program Management | RACI and Work Breakdown Structure (WBS) Templates | 2 | Editable templates to assign roles, define responsibilities, and structure implementation timelines across teams. |
| 6. Integration | Cross-Framework Mappings | 50 | Detailed alignment matrices between NIST AI RMF, ISO/IEC 42001, and COSO ERM components, enabling seamless integration into existing governance programs. |
| Total | 64 |
Domain assessments
Governance Assessment: Evaluates the existence and effectiveness of policies, oversight committees, escalation protocols, and integration with enterprise risk management functions.
Risk Assessment Assessment: Measures the maturity of processes used to identify, analyze, and prioritize AI-related risks across development and deployment stages.
Data Management Assessment: Reviews data sourcing, quality assurance, labeling practices, and bias detection methods applied to training and validation datasets.
Model Development Assessment: Assesses model documentation, version control, testing rigor, and validation procedures prior to deployment.
Deployment & Monitoring Assessment: Examines runtime monitoring, performance tracking, model drift detection, and incident logging mechanisms.
Human Oversight Assessment: Determines the adequacy of human-in-the-loop controls, decision review processes, and escalation pathways for high-risk outputs.
External Transparency Assessment: Evaluates communication practices with regulators, customers, and third parties regarding AI system capabilities and limitations.
What this saves you
| Activity | Traditional Approach | With This Playbook |
| Build AI risk inventory tool | 40, 60 hours of internal analyst time | Ready-to-use 30-question assessment included |
| Develop domain-specific assessments | 7 domains × 30 hours = 210 hours | All 7 assessments pre-built and validated |
| Map NIST AI RMF to ISO/IEC 42001 | 50+ hours of cross-referencing and documentation | 50 pre-built mapping files included |
| Prepare for internal audit | Ad hoc evidence collection, high risk of gaps | Structured runbook and audit prep guide included |
| Assign roles and responsibilities | Manual drafting of RACI and WBS | Editable templates provided for immediate use |
Who this is for
- Enterprise Risk Management (ERM) leads integrating AI risk into corporate risk registers
- Chief AI Officers or AI Governance Program Managers establishing oversight frameworks
- Compliance officers responsible for aligning AI deployments with regulatory expectations
- Internal auditors preparing to assess AI governance maturity
- Data protection officers evaluating AI systems under privacy regulations
- Legal and policy teams drafting AI use policies and accountability standards
- Technology risk managers overseeing model risk in non-financial sectors
Cross-framework mappings
This playbook includes detailed alignment between NIST AI RMF (2023), ISO/IEC 42001:2023, and COSO ERM (2017). Each mapping file connects specific controls, objectives, and assessment criteria across all three frameworks, enabling organizations to satisfy multiple compliance requirements through a unified implementation approach. Mappings cover governance structures, risk identification methods, control activities, monitoring procedures, and reporting obligations.
What is NOT in this product
- This is not a software tool or SaaS platform. It is a collection of documentation templates and assessment guides.
- No automated data collection, model monitoring, or real-time dashboards are included.
- It does not provide legal advice or substitute for regulatory counsel.
- No custom consulting, training sessions, or implementation support is offered with purchase.
- It does not cover sector-specific AI regulations such as EU AI Act high-risk classification criteria in detail.
- There are no pre-filled examples or organization-specific data in the templates.
Lifetime access and satisfaction guarantee
You receive lifetime access to the playbook with no subscription and no login portal. The files are delivered as downloadable documents that you can store, edit, and distribute within your organization. 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 building structured compliance resources for risk and governance professionals. They have analyzed 692 regulatory and industry frameworks and built 819,000+ cross-framework mappings to support consistent implementation. Their materials are used by over 40,000 practitioners across 160 countries in enterprises, government agencies, and regulated institutions.
