If you are an AI risk officer, compliance lead, or technology governance professional at a financial institution, this playbook was built for you.
Operating in a heavily regulated environment, you are under increasing pressure to establish a structured, defensible process for managing risks tied to generative AI systems. Regulators are demanding transparency into model development, deployment controls, and third-party AI vendor oversight. At the same time, product and engineering teams are rapidly adopting GenAI tools, creating a gap between innovation velocity and governance readiness. Without a standardized approach, your team risks inconsistent risk assessments, audit findings, and potential regulatory scrutiny.
Traditional consulting routes using large audit firms can cost between EUR 80,000 and EUR 250,000 for a comparable implementation. Alternatively, building an internal team of 3 to 5 specialists to develop this capability from scratch would require 6 to 9 months of effort. This playbook delivers the same outcome at a fraction of the cost: $395 one-time payment, no recurring fees.
What you get
| Phase | File Type | Description | Quantity |
| Foundation | RACI Template | Defines roles and responsibilities across AI risk domains for legal, compliance, data science, and engineering teams | 1 |
| Foundation | Work Breakdown Structure (WBS) | Hierarchical decomposition of implementation tasks, timelines, and dependencies for AI risk program rollout | 1 |
| Assessment | Domain Assessment Workbook | 30-question evaluation per AAISM domain covering governance, data provenance, model transparency, adversarial robustness, and more | 7 |
| Evidence | Evidence Collection Runbook | Step-by-step guide for gathering, labeling, and storing documentation required for internal and external audits | 1 |
| Audit | Audit Preparation Playbook | Checklist and workflow for responding to regulatory inquiries, internal audits, and third-party assessments | 1 |
| Mapping | Cross-Framework Mapping Matrix | Comprehensive alignment between NIST AI RMF, ISO/IEC 42001, AAISM domains, and MITRE ATLAS tactics | 1 |
| Vendor | GenAI Vendor Security Assessment | 30-question workbook to evaluate third-party AI providers on data handling, model explainability, and security controls | 1 |
| Implementation | Guidance Notes | Contextual instructions for adapting templates to specific organizational policies and risk appetite | 45 |
Domain assessments
Each of the seven domain assessments contains 30 targeted questions designed to evaluate implementation maturity across critical AI risk areas:
- Domain 1: Governance , Assesses policies, oversight structures, and accountability mechanisms for AI system development and deployment.
- Domain 2: Data Provenance and Integrity , Evaluates controls around training data sourcing, labeling, bias detection, and data lifecycle management.
- Domain 3: Model Assurance , Reviews model testing, validation, explainability, and performance monitoring practices.
- Domain 4: Adversarial Robustness , Measures resilience against prompt injection, data poisoning, and model evasion techniques.
- Domain 5: Transparency and Documentation , Checks for completeness of model cards, system documentation, and disclosure practices.
- Domain 6: Human Oversight and Interaction , Examines processes for human-in-the-loop decisioning, escalation paths, and user feedback.
- Domain 7: Third-Party and Supply Chain Risk , Assesses due diligence, contract terms, and ongoing monitoring of external AI vendors and tools.
What this saves you
| Activity | Time Without Playbook | Time With Playbook | Estimated Hours Saved |
| Develop AI risk assessment framework | 320 hours | 40 hours | 280 |
| Create vendor evaluation questionnaire | 80 hours | 10 hours | 70 |
| Prepare for internal audit | 120 hours | 30 hours | 90 |
| Map controls to NIST AI RMF | 100 hours | 15 hours | 85 |
| Establish RACI and WBS | 60 hours | 5 hours | 55 |
| Collect and organize evidence | 140 hours | 40 hours | 100 |
| Total Estimated Savings | 820 hours | 140 hours | 680 |
Who this is for
- AI Risk Officers responsible for establishing governance over machine learning and generative AI systems
- Compliance Managers in financial institutions needing to meet regulatory expectations for algorithmic accountability
- Technology Governance Leads overseeing model risk management programs
- Chief Data Officers building organization-wide AI assurance frameworks
- Internal Audit Teams preparing to assess AI system controls
- Legal and Regulatory Affairs Specialists interpreting AI-related guidance from financial regulators
- Security Architects integrating AI risk into enterprise cybersecurity programs
Cross-framework mappings
This playbook provides direct mappings to the following frameworks and standards:
- NIST AI Risk Management Framework (AI RMF 1.0)
- ISO/IEC 42001:2023 , Artificial Intelligence Management System
- AAISM AI Risk Domains (Governance, Data, Model, Adversarial, Transparency, Human, Third-Party)
- MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems)
What is NOT in this product
- This is not a software tool or SaaS platform. It does not include automated scanning, monitoring, or integration with AI model pipelines.
- It does not provide legal advice or regulatory interpretation specific to any jurisdiction.
- There are no pre-filled templates. All documents require customization to your organization's policies and risk appetite.
- No training sessions, consulting hours, or implementation support are included in the purchase.
- It does not cover non-financial sector use cases such as healthcare, education, or public sector AI deployments.
- The playbook does not include model validation code, statistical testing scripts, or data lineage tools.
Lifetime access and satisfaction guarantee
You receive lifetime access to the playbook with no subscription, no login portal, and no recurring fees. Files are delivered as downloadable documents. 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 methodologies for regulated industries. They have analyzed 692 governance, risk, and compliance frameworks and built 819,000+ cross-framework mappings. Their tools are used by 40,000+ practitioners across 160 countries, supporting consistent, auditable risk management in highly supervised environments.
