If you are a GenAI product lead or security architect at a technology-driven education or AI product organization in the Middle East and North Africa, this playbook was built for you.
As generative AI becomes central to product strategy, your team faces mounting pressure to demonstrate compliance with evolving AI governance standards while maintaining rapid development cycles. You are expected to defend against prompt injection, prevent data leakage through model outputs, secure third-party LLM integrations, and document risk controls for external auditors, often without clear internal guidelines. Regulatory scrutiny is increasing, with regional data protection laws and international frameworks like the EU AI Act setting new benchmarks for accountability. At the same time, engineering teams are under pressure to ship features, creating tension between innovation and compliance.
Traditional approaches to AI security, engaging external consultants from global audit firms or assembling internal task forces, come at significant cost. A comparable advisory engagement from a major professional services provider would range from EUR 80,000 to EUR 250,000. Alternatively, dedicating 3 to 5 full-time staff across product, engineering, and compliance for 4 to 6 months would be required to build equivalent documentation and controls from scratch. This playbook delivers the same depth of structure and readiness at a fraction of the cost: $395.
What you get
| Phase | File Type | Description | Quantity |
| Threat Modeling | LLM Threat Modeling Workbook | 30-question assessment aligned with OWASP LLM Top 10 categories, guiding teams through context-specific risk identification during design phase | 1 |
| Domain Assessments | Domain-Specific Risk Assessment | Structured questionnaire per OWASP LLM Top 10 domain, each containing 30 targeted questions to evaluate technical and procedural controls | 7 |
| Evidence Collection | Evidence Runbook | Step-by-step guide for gathering technical logs, model configuration records, access policies, and prompt validation test results required for audit defense | 1 |
| Audit Preparation | Audit Prep Playbook | Checklist-driven process for responding to third-party AI audits, including mock review scenarios and evidence packaging templates | 1 |
| Team Execution | RACI Matrix Template | Pre-built responsibility assignment chart mapping roles across product, security, legal, and engineering for each control implementation task | 1 |
| Project Planning | Work Breakdown Structure (WBS) | Hierarchical task list covering all activities from initial threat modeling to final audit submission, with estimated effort and dependencies | 1 |
| Compliance Alignment | Cross-Framework Mapping Matrix | Detailed spreadsheet linking each OWASP LLM Top 10 control to equivalent requirements in NIST AI RMF, ISO/IEC 42001, and EU AI Act | 1 |
| Supplemental Tools | Implementation Guides | Short-form technical notes on applying controls such as prompt sanitization, output filtering, and model provenance tracking in production environments | 50 |
Domain assessments
Each of the seven domain assessments contains 30 focused questions designed to uncover gaps in security posture across critical areas of LLM application development:
- LLM01: Prompt Injection , Evaluates defenses against malicious input manipulation that leads to unauthorized actions or data exposure.
- LLM02: Insecure Output Handling , Assesses safeguards for downstream components that process untrusted model outputs.
- LLM03: Training Data Poisoning , Reviews controls for ensuring integrity of data used in fine-tuning and retrieval-augmented generation pipelines.
- LLM04: Denial of Service via Resource Exhaustion , Measures resilience against abuse of computational resources through adversarial query patterns.
- LLM05: Supply Chain Vulnerabilities , Examines security practices for third-party models, plugins, and vector databases integrated into the application stack.
- LLM06: Sensitive Information Disclosure , Tests policies and technical measures to prevent leakage of personal, proprietary, or regulated data in model responses.
- LLM07: Inadequate Sandboxing , Determines the strength of isolation mechanisms between the LLM runtime and backend systems.
What this saves you
| Activity | Traditional Approach | With This Playbook |
| Initial threat modeling | 3 weeks of cross-functional workshops with external facilitators | Structured 2-day session using the 30-question workbook |
| Control gap assessment | Manual review across 7 risk domains, 40+ hours per domain | Standardized assessments, 10, 12 hours per domain |
| Evidence compilation | Reactive collection during audit cycle, high risk of missing artifacts | Proactive runbook with defined owners and retention rules |
| Audit response preparation | 6, 8 weeks of internal coordination and document assembly | 2-week readiness cycle using checklist and mock review templates |
| Cross-framework alignment | Dedicated analyst mapping controls across standards over 3 months | Pre-built mapping matrix covering OWASP, NIST, ISO, and EU AI Act |
Who this is for
- GenAI product managers responsible for delivering compliant AI features in EdTech or enterprise SaaS platforms
- Security architects designing secure integration patterns for LLM-powered applications
- Compliance leads preparing for third-party AI audits or regulatory inspections
- Engineering directors overseeing secure development lifecycle adoption for AI projects
- Chief AI officers establishing governance frameworks across multiple product lines
- Data protection officers ensuring alignment with regional privacy laws in the MENA region
- DevOps leads implementing runtime protections and monitoring for production LLM services
Cross-framework mappings
This playbook provides direct mappings between the OWASP LLM Top 10 and the following international standards and regulatory frameworks:
- OWASP LLM Top 10 (2023 Edition)
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0)
- ISO/IEC 42001 , Information Security Management for AI Systems
- European Union Artificial Intelligence Act (Title III, Annex III , High-Risk AI Systems)
What is NOT in this product
- This is not a software tool or API for automated scanning of LLM applications
- No real-time monitoring, firewall, or prompt injection detection engine is included
- It does not provide custom legal advice or replace consultation with regulatory counsel
- There are no pre-filled templates with organizational data or client-specific configurations
- The playbook does not include training videos, live workshops, or personalized support
- No integration with CI/CD pipelines or code repositories is provided
- It is not a certification body or audit service
Lifetime access and satisfaction guarantee
You receive permanent download access to all 64 files with no subscription required and no login portal to maintain. There are no recurring fees or access expirations. 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 emerging technologies, analyzing 692 global standards across privacy, security, and AI governance. Our research team maintains a repository of 819,000+ cross-framework control mappings used by more than 40,000 practitioners in 160 countries. This playbook reflects field-tested methodology applied in regulated sectors including education technology, financial services, and public sector AI deployment.
