If you are a cybersecurity leader in an AI-integrated enterprise, this playbook was built for you.
As the architect of your organization's AI risk posture, you are under growing pressure to ensure that machine learning systems, autonomous decision engines, and generative AI tools operate safely within your cyber defense perimeter. Regulatory bodies now demand documented risk assessments for AI-driven clinical diagnostics, synthetic media detection, and adversarial threat resilience, especially when AI interfaces with critical digital infrastructure. You must demonstrate control over model drift, data poisoning, and unintended AI behaviors without overburdening engineering teams or delaying innovation. The absence of structured governance creates audit exposure, operational blind spots, and reputational risk when AI systems fail in production.
Engaging external consultants to design an AI risk framework typically costs between EUR 80,000 and EUR 250,000 depending on organizational scale and deployment complexity. Alternatively, assigning internal teams to reverse-engineer compliance from NIST AI RMF and ISO/IEC 42001 guidance consumes 3 to 5 full-time equivalents over 4 to 6 months, time most security leaders cannot spare. This playbook delivers the same outcome for $395: a ready-to-deploy operational framework that aligns with NIST and ISO requirements, reduces implementation time by over 90%, and integrates directly into existing cybersecurity workflows.
What you get
| Phase | File Type | Description | Count |
| Domain Assessment | AI Risk Domain Workbook | 30-question assessment per domain covering governance, data integrity, model transparency, adversarial robustness, and incident response for AI systems | 7 |
| Evidence Collection | Runbook | Step-by-step instructions for gathering technical evidence from data science, DevOps, and security teams to support AI risk claims | 1 |
| Audit Preparation | Playbook | Checklist-driven guide to preparing for internal and external audits of AI systems under NIST AI RMF and ISO/IEC 42001 | 1 |
| Governance Setup | RACI Template | Pre-built responsibility assignment matrix for AI risk roles across security, compliance, data science, legal, and executive leadership | 1 |
| Project Execution | Work Breakdown Structure (WBS) | Hierarchical task list for implementing AI risk controls, including milestones, dependencies, and deliverables | 1 |
| Framework Alignment | Cross-Mapping Index | Detailed reference linking NIST AI RMF subcategories to ISO/IEC 42001 clauses and NIST CSF functions | 1 |
| Implementation Support | Guidance Notes | Contextual explanations for each control, including real-world application examples in healthcare and cyber defense environments | 50 |
| Total Files | 64 |
Domain assessments
Each of the seven domain assessments contains 30 targeted questions designed to surface risks in high-impact AI use cases. These domains are:
- AI Governance and Accountability: Evaluates the existence of oversight structures, ethical review boards, and escalation paths for AI-related incidents.
- Data Provenance and Integrity: Assesses controls over training data sourcing, labeling accuracy, and protection against data tampering or bias injection.
- Model Development and Transparency: Reviews documentation practices, version control, explainability methods, and model validation procedures.
- Adversarial AI Threat Modeling: Identifies preparedness for evasion attacks, model inversion, prompt injection, and data poisoning in deployed systems.
- Autonomous System Safety: Examines fail-safes, human-in-the-loop requirements, and override mechanisms for self-operating AI agents.
- Deepfake Detection and Synthetic Media Defense: Measures capabilities to identify and respond to AI-generated audio, video, and text used in social engineering or disinformation.
- Incident Response and Recovery: Tests readiness for AI-specific outages, model degradation, and cyberattacks targeting machine learning pipelines.
What this saves you
| Task | Without This Playbook | With This Playbook |
| Build AI risk assessment framework | 6, 9 months of internal working group effort | Deployable in under 2 weeks using pre-built templates |
| Map controls to NIST AI RMF and ISO/IEC 42001 | Manual cross-referencing across 200+ pages of guidance | Pre-built mapping index included |
| Assign AI risk responsibilities | Ad hoc role definition leading to accountability gaps | RACI template with defined roles for 12 AI risk functions |
| Prepare for AI system audit | Reactive evidence gathering under time pressure | Evidence runbook enables proactive collection |
| Train security team on AI threats | External training programs at $2,500+ per seat | Guidance notes and workbooks serve as internal training material |
| Total estimated time saved | 400, 600 hours of labor | Implementation in under 100 hours |
Who this is for
- Chief Information Security Officers (CISOs) responsible for securing AI-integrated environments
- AI Risk Managers tasked with implementing governance frameworks across machine learning portfolios
- Compliance Officers in healthcare organizations using AI for medical imaging, diagnostics, or patient monitoring
- Cybersecurity Architects designing defenses against adversarial AI and deepfake attacks
- Chief Technology Officers (CTOs) overseeing AI deployment in regulated digital infrastructure
- Privacy Officers ensuring AI systems comply with data protection obligations
- Internal Audit Leads preparing to assess AI system controls and risk management practices
Cross-framework mappings
This playbook provides direct alignment between the following standards and frameworks:
- NIST Artificial Intelligence Risk Management Framework (AI RMF) 1.0
- ISO/IEC 42001:2023 , Artificial Intelligence Management System
- NIST Cybersecurity Framework (CSF) 1.1
The included cross-mapping index documents how each control in the AI risk assessments corresponds to specific subcategories in NIST AI RMF, clauses in ISO/IEC 42001, and functions in NIST CSF, enabling seamless integration into existing compliance programs.
What is NOT in this product
- This is not a software tool or automated scanning platform. It does not integrate with MLOps platforms or AI monitoring systems.
- It does not include legal advice or regulatory representation. Users are responsible for validating compliance with jurisdiction-specific laws.
- No AI models, datasets, or code repositories are provided. The playbook focuses on process, policy, and assessment design.
- It does not cover non-AI cybersecurity controls such as firewall configuration, endpoint protection, or identity management unrelated to AI systems.
- There are no certifications or attestations included. The product supports preparation for certification but does not confer compliance status.
- Support for frameworks outside NIST AI RMF, ISO/IEC 42001, and NIST CSF is not provided.
Lifetime access and satisfaction guarantee
You receive lifetime access to the playbook with no subscription, no login portal, and no recurring fees. The files are delivered as downloadable documents that you own and control. 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
We have spent 25 years building structured compliance frameworks for regulated industries worldwide. Our research team has analyzed 692 global standards and created 819,000+ cross-framework mappings to help organizations reduce duplication and streamline risk management. Over 40,000 practitioners across 160 countries use our methodology to implement governance for emerging technologies including artificial intelligence, quantum computing, and autonomous systems. This playbook reflects that depth of operational experience applied to the challenges of securing AI in real-world environments.
