If you are an AI governance lead or security architect at a cloud-native enterprise, this playbook was built for you.
You are responsible for deploying AI agents in production environments where security, compliance, and operational resilience are non-negotiable. Your organization is adopting large language models and autonomous agents faster than your control frameworks can keep up. You face mounting pressure to align with emerging AI-specific regulations, demonstrate due diligence to auditors, and prevent adversarial exploits in agent workflows, all while maintaining velocity in a competitive market.
Regulatory scrutiny on AI systems is intensifying. You must now account for algorithmic transparency, data provenance, model drift, and adversarial prompt injection under evolving standards. Auditors are beginning to treat AI workloads like critical infrastructure, demanding evidence of risk assessment, red teaming, and continuous monitoring. At the same time, cloud-native architectures introduce distributed trust boundaries, making it harder to enforce Zero Trust principles across agent-to-agent communications and external API integrations.
Engaging a Big-4 consultancy to build a custom AI risk implementation roadmap typically costs between EUR 80,000 and EUR 250,000. Alternatively, dedicating internal resources means assigning 2 to 3 full-time engineers, legal analysts, and security architects for 4 to 6 months to research frameworks, map controls, and develop operational templates. This playbook delivers the same outcome at a fraction of the cost: $395 one-time, with no recurring fees.
What you get
| Phase | File Type | Description | Count |
| Assessment | Domain Assessment | Structured questionnaire covering governance, risk, security, and compliance per NIST AI RMF domain | 7 |
| Evidence | Evidence Collection Runbook | Step-by-step guide to gather technical logs, model cards, access controls, and testing reports for audit validation | 1 |
| Audit | Audit Preparation Playbook | Checklist and timeline for internal and external audits, including artifact packaging and stakeholder coordination | 1 |
| Execution | RACI Template | Pre-defined responsibility matrix for AI risk activities across engineering, security, legal, and compliance teams | 1 |
| Execution | Work Breakdown Structure (WBS) | Hierarchical task list for implementing controls, conducting red team exercises, and integrating with CI/CD pipelines | 1 |
| Mapping | Cross-Framework Control Matrix | Comprehensive spreadsheet linking NIST AI RMF to MITRE ATLAS, ISO/IEC 42001, and Microsoft SDL for AI | 1 |
| Threat Modeling | AI Threat Modeling Workbook | 30-question diagnostic tool to identify vulnerabilities in LLM agent chains, memory stores, and tool integrations | 1 |
| Red Teaming | Red Team Playbook | Tactics for simulating adversarial attacks on AI agents, including prompt injection, data poisoning, and privilege escalation | 1 |
| Governance | Policy Alignment Guide | Template language for updating acceptable use, incident response, and model lifecycle policies to include AI agents | 1 |
| Architecture | Secure AI Agent Reference Architecture | Diagram and documentation for implementing Zero Trust controls in agent communication, data handling, and API gateways | 1 |
| Monitoring | Runtime Observability Template | Log schema, alert thresholds, and dashboard configurations for detecting anomalous agent behavior | 1 |
| Training | AI Risk Awareness Deck | Presentation for educating developers, product managers, and executives on AI-specific threats and controls | 1 |
| Integration | CI/CD Pipeline Control Scripts | Automated checks for model signing, dependency scanning, and policy enforcement in deployment workflows | 50 |
Domain assessments
Each of the seven domain assessments contains 30 targeted questions to evaluate maturity across the NIST AI RMF lifecycle:
- Govern: Evaluate policies, accountability structures, and oversight mechanisms for AI system development and deployment.
- Map: Identify data sources, model dependencies, system boundaries, and third-party integrations in AI agent environments.
- Measure: Assess performance metrics, bias detection methods, and uncertainty quantification for AI outputs.
- Manage: Review risk treatment plans, incident response procedures, and model retirement processes.
- Secure: Validate encryption, access controls, and network segmentation for AI models and their supporting infrastructure.
- Monitor: Examine logging, anomaly detection, and drift tracking capabilities during AI system operation.
- Test: Confirm the existence and execution of red teaming, adversarial testing, and scenario-based validation.
What this saves you
| Activity | Without This Playbook | With This Playbook |
| Framework Mapping | 60+ hours manually aligning NIST AI RMF, MITRE ATLAS, ISO/IEC 42001, and Microsoft SDL | Completed in under 2 hours using pre-built cross-mapping spreadsheet |
| Threat Modeling | Weeks spent researching attack patterns and designing agent-specific diagnostics | Use ready-made 30-question workbook tailored to LLM agent chains and memory stores |
| Audit Preparation | 3 to 4 months compiling evidence, writing responses, and coordinating stakeholders | Follow audit playbook to reduce prep time by 70% with standardized artifact templates |
| Red Teaming | Hire external consultants or train internal teams from scratch on AI-specific tactics | Deploy pre-written red team scenarios for prompt injection, data leakage, and role impersonation |
| Policy Updates | Legal and compliance teams draft AI policies without technical input, leading to gaps | Use policy alignment guide with technical and governance language ready for review |
| Architecture Design | Iterate on secure designs over multiple sprints with inconsistent control application | Implement Zero Trust patterns using reference architecture and CI/CD control scripts |
Who this is for
- AI Governance Leads responsible for policy, risk, and compliance alignment in AI system rollouts
- Security Architects designing secure cloud-native AI agent systems with Zero Trust principles
- Compliance Managers preparing for audits involving AI workloads under ISO or sector-specific standards
- Head of AI Product overseeing safe deployment of LLM-powered agents in customer-facing applications
- DevSecOps Engineers integrating AI risk controls into CI/CD pipelines and runtime environments
- Chief Information Security Officers seeking to standardize AI risk posture across business units
- Internal Audit Teams requiring structured assessment tools for AI system reviews
Cross-framework mappings
This playbook provides direct control mappings between the following frameworks:
- NIST AI Risk Management Framework (AI RMF)
- MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
- ISO/IEC 42001 (Information Security Management for AI Systems)
- Microsoft Security Development Lifecycle (SDL) for AI
What is NOT in this product
- This is not a software tool or SaaS platform. It does not include automated scanning, monitoring, or enforcement capabilities.
- No vendor-specific configurations for cloud providers, AI platforms, or identity systems are included.
- The playbook does not provide legal advice or guarantee regulatory compliance.
- Custom consulting, training sessions, or implementation support are not part of this purchase.
- There are no certifications, badges, or audit attestation services included.
- Model performance tuning, fine-tuning guidance, or data labeling workflows are outside the scope.
- Real-time threat intelligence feeds or dynamic rule updates are not provided.
Lifetime access and satisfaction guarantee
You receive lifetime access to all 64 files with no subscription and no login portal. Once downloaded, the materials are yours to use, modify, 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
We have spent 25 years building structured compliance tooling for complex regulatory environments. Our research covers 692 global frameworks across privacy, security, and AI governance. We maintain a database of 819,000+ cross-framework mappings and have trained 40,000+ practitioners in 160 countries. This playbook reflects field-tested methodologies used by organizations deploying AI at scale under strict oversight.
