A tailored course, built for your situation
Mastering ISO 27001 for Product Leaders Driving Gen AI at Scale
Build trusted, auditable AI systems with confidence and clarity
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI moves fast, but skipping governance creates rework, delays, and loss of trust when scrutiny hits
The situation this course is for
Product teams ship AI features under tight timelines, but without structured compliance grounding, they face repeated audits, last-minute documentation scrambles, and second-guessing from security and legal. The cost isn't just time, it's credibility.
Who this is for
Senior product leader at a high-growth tech firm driving generative AI initiatives, balancing innovation speed with regulatory and internal compliance expectations
Who this is not for
Individuals seeking introductory cybersecurity training or non-product roles without decision authority on feature scope and delivery timelines
What you walk away with
- Position yourself as the go-to person for secure AI delivery within your organization
- Produce ISO 27001-aligned documentation that passes internal review without rework
- Lead cross-functional alignment between product, security, and compliance teams confidently
- Anticipate audit questions and build evidence proactively into development cycles
- Turn compliance from a checkpoint into a competitive advantage in feature velocity
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27001 in AI Product Development
Establish a working understanding of ISO 27001 principles as they apply specifically to generative AI systems. Learn how information security objectives align with product milestones and risk tolerance thresholds.
12 chapters in this module
- Defining information assets in a generative AI workflow
- Mapping ISO 27001 clauses to AI product lifecycle stages
- How security context differs between AI prototypes and production systems
- Integrating confidentiality, integrity, and availability into AI design
- Understanding the role of risk assessment in model deployment
- Linking AI use cases to organizational information security policies
- Common misconceptions about ISO 27001 and AI
- Why speed doesn't have to compromise compliance
- Setting baselines for secure AI development environments
- Identifying stakeholders in AI security governance
- Documenting asset ownership in cross-team AI projects
- Aligning AI initiatives with existing ISMS frameworks
Module 2. Scoping AI Systems Under ISO 27001
Learn how to define clear boundaries for AI components subject to ISO 27001 controls, avoiding overreach while ensuring audit readiness.
12 chapters in this module
- Determining which AI components qualify as information assets
- Drawing scope boundaries around data pipelines and model serving
- Excluding non-critical systems without weakening posture
- Justifying scope decisions to compliance reviewers
- Handling multi-region AI deployments under one scope
- When to split AI systems into separate scoping statements
- Documenting rationale for in-scope and out-of-scope elements
- Working with legal to define data classification levels
- Incorporating third-party AI services into scope definitions
- Managing scope creep during agile AI development
- Versioning scope documentation for iterative releases
- Presenting scope clarity to internal audit teams
Module 3. Risk Assessment Specific to Generative AI
Adapt ISO 27001 risk methodology to the unique threats of generative AI, including model leakage, prompt injection, and unintended data exposure.
12 chapters in this module
- Identifying AI-specific threats to information security
- Building a risk register for generative AI workloads
- Assessing likelihood and impact of model inversion attacks
- Evaluating risks from fine-tuning on sensitive datasets
- Mapping adversarial inputs to control objectives
- Handling hallucination-related data integrity concerns
- Quantifying reputational risk from AI-generated content
- Incorporating supply chain risks from open-source models
- Assessing dependencies on external AI APIs
- Prioritizing risks based on organizational exposure
- Documenting risk treatment plans for AI use cases
- Updating risk assessments after model retraining
Module 4. Control Mapping for AI Development Workflows
Translate ISO 27001 controls into actionable steps within AI product development, from ideation to deployment.
12 chapters in this module
- Applying access control principles to AI model repositories
- Securing prompts and prompt histories as sensitive data
- Implementing change management for AI model updates
- Enforcing encryption standards for AI training data
- Logging interactions with AI systems for auditability
- Ensuring personnel screening covers AI-specific roles
- Applying secure development practices to AI codebases
- Managing vendor access to AI infrastructure securely
- Defining backup strategies for AI model weights and data
- Establishing clear user responsibilities in AI interfaces
- Enforcing classification labels on AI-generated outputs
- Integrating incident response planning with AI monitoring
Module 5. Building the Statement of Applicability for AI Projects
Create a defensible SoA that justifies inclusion or exclusion of ISO 27001 controls in the context of AI innovation.
12 chapters in this module
- Structuring the SoA to reflect AI-specific risk posture
- Justifying control exclusions without weakening compliance
- Documenting alternative safeguards for agile AI teams
- Aligning SoA language with product development timelines
- Incorporating red team findings into control justification
- Handling dynamic environments where controls evolve
- Referencing AI-specific policies in the SoA
- Maintaining traceability from risk assessment to controls
- Versioning the SoA alongside AI model releases
- Preparing SoA updates for external auditor review
- Using automation to keep the SoA current
- Presenting the SoA to non-security stakeholders clearly
Module 6. Evidence Generation in Fast-Moving AI Environments
Produce audit-ready evidence without slowing down AI iteration, using lightweight but credible documentation practices.
12 chapters in this module
- Designing evidence collection into sprint planning
- Capturing model versioning and deployment logs
- Automating evidence generation from CI/CD pipelines
- Documenting security decisions without slowing releases
- Using code comments as compliance artifacts
- Storing evidence in tamper-evident repositories
- Generating screenshots that prove control effectiveness
- Maintaining personnel training records for AI teams
- Archiving prompt logs with privacy safeguards
- Producing time-stamped access reviews for AI systems
- Linking Jira tickets to control implementation
- Creating living documentation that survives team changes
Module 7. Internal Audit Readiness for AI Systems
Prepare for internal audits by aligning AI documentation, controls, and team practices with ISO 27001 expectations.
12 chapters in this module
- Anticipating common audit questions for AI projects
- Organizing documentation for quick auditor access
- Conducting pre-audit walkthroughs with compliance teams
- Responding to findings without defensiveness
- Demonstrating continuous improvement in AI security
- Showing evidence of management review for AI risks
- Linking AI controls to broader organizational policies
- Training product engineers on audit expectations
- Using audit feedback to strengthen future releases
- Avoiding over-documentation while proving compliance
- Maintaining consistency across multiple AI initiatives
- Preparing executive summaries of AI compliance posture
Module 8. Cross-Functional Alignment on AI Governance
Lead alignment between product, security, legal, and compliance teams using ISO 27001 as a shared framework.
12 chapters in this module
- Translating product goals into security requirements
- Facilitating joint risk assessment sessions
- Creating shared definitions of 'secure AI'
- Running workshops to align on control implementation
- Building trust between fast-moving product teams and compliance
- Escalating unresolved conflicts constructively
- Documenting decisions from cross-team meetings
- Establishing regular sync points on AI compliance
- Using ISO 27001 language to bridge terminology gaps
- Creating joint ownership of AI security outcomes
- Measuring alignment through shared KPIs
- Recognizing contributions from all functional areas
Module 9. Secure AI Deployment and Change Management
Implement structured change control for AI systems without sacrificing agility.
12 chapters in this module
- Defining what constitutes a 'change' in AI systems
- Establishing approval workflows for model updates
- Assessing security impact of prompt library changes
- Handling emergency model deployments securely
- Documenting rollback procedures for AI features
- Involving security in pre-deployment checklists
- Tracking model versions and dependencies
- Managing configuration drift in AI serving environments
- Applying patch management principles to AI frameworks
- Using canary releases to limit exposure
- Auditing changes post-deployment
- Learning from incidents to improve change control
Module 10. Third-Party Risk in AI Ecosystems
Manage risks from external AI models, APIs, and open-source components within ISO 27001 compliance.
12 chapters in this module
- Assessing security posture of external AI providers
- Reviewing terms of service for AI API usage
- Evaluating data handling practices of third-party models
- Managing model licensing and attribution compliance
- Conducting due diligence on open-source AI frameworks
- Defining acceptable use policies for external AI tools
- Monitoring third-party AI service uptime and breaches
- Establishing fallback plans for discontinued APIs
- Documenting vendor risk treatment decisions
- Ensuring sub-processors meet security standards
- Requiring audit reports from key AI vendors
- Building exit strategies for third-party AI dependencies
Module 11. Continuous Improvement in AI Security
Embed feedback loops that strengthen AI security posture over time.
12 chapters in this module
- Using incident reports to refine AI controls
- Conducting post-mortems on AI-related security events
- Updating risk assessments after model retraining
- Incorporating red team findings into product roadmap
- Measuring control effectiveness over time
- Soliciting feedback from internal audit teams
- Benchmarking AI security against industry peers
- Adjusting control scope based on threat intelligence
- Training new team members on AI security norms
- Tracking maturity of AI compliance practices
- Celebrating improvements in audit outcomes
- Linking security gains to product velocity
Module 12. Positioning Yourself as the Trusted AI Authority
Use your mastery of ISO 27001 to become the recognized internal expert on secure AI delivery.
12 chapters in this module
- Communicating AI security value to senior leaders
- Presenting compliance progress without jargon
- Sharing best practices across product teams
- Mentoring junior product managers on governance
- Publishing internal guides on secure AI patterns
- Representing product in cross-functional risk forums
- Being the first call for new AI compliance questions
- Shaping AI governance policy with influence
- Building credibility through consistent delivery
- Balancing innovation and compliance publicly
- Creating templates others adopt voluntarily
- Leaving a lasting playbook for future teams
How this maps to your situation
- Initial AI product scoping under compliance constraints
- Mid-cycle risk assessment and control implementation
- Pre-audit preparation and documentation finalization
- Post-deployment review and continuous improvement
Before vs. after
Before
Working reactively to compliance requests, scrambling for documentation, and being seen as a bottleneck
After
Proactively shaping secure AI delivery, producing clean evidence, and being sought out as the trusted voice on governance
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: 90 minutes of focused learning, designed to fit within a Sunday morning
If nothing changes
Without structured governance, AI initiatives face repeated rework, loss of credibility with security teams, and missed opportunities to lead in trusted innovation.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to product leaders driving generative AI, with real-world examples and actionable templates that align innovation with ISO 27001 requirements.
Frequently asked
Is this course technical or strategic?
It's designed for product leaders , strategic but grounded in technical realities. No coding required, but deep enough to earn trust from security teams.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in my next promotion?
Yes , by positioning you as the go-to person for secure AI delivery, this course builds the kind of cross-functional influence that leads to advancement.
$199 one-time. 90 minutes of focused learning, designed to fit within a Sunday morning.
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours