A tailored course, built for your situation
Mastering ISO 27001 for AI Engineering Leaders
Build a self-reinforcing information security practice that scales with every AI product cycle
$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.
Audit evidence that survives AI iteration
The situation this course is for
Engineering teams ship fast, but security documentation lags, creating rework during compliance cycles. The gap isn't intent, it's repeatability.
Who this is for
Senior AI engineering leader at a global technology firm, responsible for secure, compliant AI product delivery at scale
Who this is not for
Individual contributors without delivery ownership, non-technical compliance analysts, or teams focused solely on post-incident response
What you walk away with
- Produce ISO 27001-aligned control evidence in under 72 hours per AI product line
- Standardize security documentation templates across AI delivery pods
- Automate control mapping updates across iterative model releases
- Replicate secure-by-design patterns across multiple AI projects
- Create an internal reference library of approved control implementations
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27001 in AI Systems
Establish the core principles of information security management as they apply to AI development environments, data pipelines, and model deployment lifecycles. Understand how ISO 27001 clauses map to real-world AI risks without over-engineering.
12 chapters in this module
- Understanding ISO 27001 scope in AI product contexts
- Mapping AI data flows to information security domains
- Defining asset ownership in distributed AI teams
- Classifying data sensitivity in training and inference
- Integrating security controls into MLOps pipelines
- Aligning AI governance with ISMS objectives
- Controlled access to model parameters and weights
- Managing third-party AI component risk
- Documenting AI-specific control boundaries
- Establishing accountability across AI sprints
- Versioning security policies alongside models
- Linking AI incidents to ISMS improvement cycles
Module 2. Building the Security Case for AI Projects
Learn how to construct compelling, evidence-based security justifications tailored to AI initiatives, balancing innovation speed with compliance rigor.
12 chapters in this module
- Structuring security narratives for AI prototypes
- Documenting control rationale for fast iteration
- Using real AI deployment examples in evidence packages
- Aligning AI security scope with business impact
- Preempting auditor questions in model design phase
- Embedding compliance checkpoints in AI roadmaps
- Creating living security cases for evolving models
- Linking AI risk assessments to control selection
- Demonstrating continuous improvement in AI security
- Writing clear control narratives for non-security reviewers
- Maintaining evidence integrity during AI refactoring
- Standardizing language across AI security artifacts
Module 3. Control Mapping for Iterative AI Development
Develop methods to maintain accurate, up-to-date control mappings across constantly changing AI systems, ensuring traceability from policy to production.
12 chapters in this module
- Mapping controls to dynamic AI infrastructure
- Tracking control implementation across model versions
- Automating control evidence updates in CI/CD
- Versioning control mappings alongside code
- Identifying control gaps in AI feature additions
- Integrating control checks into pull requests
- Maintaining control consistency across AI services
- Updating mappings after architecture changes
- Documenting control exceptions in AI experiments
- Using templates to standardize control application
- Aligning control scope with AI deployment regions
- Ensuring mappings survive team reorganizations
Module 4. Secure Design Patterns for AI Systems
Adopt reusable architectural blueprints that bake in ISO 27001 compliance from the earliest stages of AI development.
12 chapters in this module
- Designing compliant data ingestion pipelines
- Implementing secure model training environments
- Architecting encrypted inference endpoints
- Protecting model versioning systems
- Securing AI-powered APIs by default
- Building access controls for AI dashboards
- Designing audit trails for model decisions
- Hardening containerized AI deployments
- Implementing secure model rollback procedures
- Protecting AI configuration management
- Designing for data subject rights in AI
- Securing federated learning setups
Module 5. Automating Evidence Collection
Implement automated systems to gather and maintain compliance evidence across AI product lines, reducing manual effort and human error.
12 chapters in this module
- Automating control validation in test environments
- Generating evidence from security scanning tools
- Integrating evidence collection into CI/CD
- Using infrastructure as code for compliance
- Automating access review documentation
- Capturing network configuration changes
- Logging model deployment events for audit
- Validating encryption settings automatically
- Monitoring for configuration drift in AI systems
- Automating vulnerability scan integrations
- Creating evidence dashboards for AI teams
- Scheduling recurring control checks
Module 6. Cross-Team Security Integration
Coordinate security practices across AI engineering, infrastructure, data science, and product teams to ensure consistent control implementation.
12 chapters in this module
- Establishing cross-functional AI security roles
- Creating shared definitions of done for security
- Integrating security into AI project kickoffs
- Running joint control design workshops
- Documenting team responsibilities in AI security
- Aligning sprint goals with compliance needs
- Creating cross-team security playbooks
- Facilitating control handoffs between teams
- Standardizing security communication channels
- Running cross-functional control reviews
- Integrating security feedback into retros
- Coordinating incident response across AI teams
Module 7. Audit Readiness for AI Deployments
Prepare for compliance audits by building self-sustaining documentation systems that evolve with AI products.
12 chapters in this module
- Structuring audit packages for AI systems
- Preparing evidence for AI-specific controls
- Documenting AI risk treatment decisions
- Creating clear control implementation proofs
- Preparing for auditor walkthroughs of AI pipelines
- Anticipating follow-up questions on AI security
- Maintaining evidence between audit cycles
- Demonstrating continuous compliance in AI
- Preparing security leads for audit interviews
- Updating documentation after AI changes
- Verifying completeness before audit submission
- Streamlining auditor access to AI evidence
Module 8. Scaling Security Across AI Product Lines
Extend successful security practices from one AI product to multiple teams, creating organization-wide standards.
12 chapters in this module
- Identifying transferable security patterns
- Adapting controls for different AI use cases
- Creating templates for new AI projects
- Onboarding teams to established practices
- Customizing frameworks for AI domains
- Maintaining consistency across AI verticals
- Sharing lessons from past AI audits
- Scaling tooling across engineering groups
- Standardizing security documentation
- Creating centralized AI security resources
- Measuring adoption across teams
- Optimizing practices based on team feedback
Module 9. Managing Third-Party AI Risk
Assess and control security risks introduced by external AI platforms, models, and data sources.
12 chapters in this module
- Evaluating vendor security practices for AI tools
- Assessing risks in pre-trained models
- Managing data leakage in third-party AI APIs
- Documenting third-party control reliance
- Verifying compliance of AI cloud services
- Conducting security due diligence on AI vendors
- Managing supply chain risks in AI components
- Creating vendor risk assessment templates
- Monitoring third-party AI service changes
- Establishing incident response with vendors
- Negotiating security terms for AI contracts
- Auditing vendor compliance claims
Module 10. Security Versioning and Change Management
Track and control changes to AI systems and their security implementations over time.
12 chapters in this module
- Versioning security policies alongside AI models
- Managing control changes in agile environments
- Documenting security decisions in changelogs
- Aligning security updates with release cycles
- Handling security debt in AI systems
- Tracking control implementation status
- Managing exceptions and waivers
- Automating version consistency checks
- Preserving historical evidence
- Communicating changes across teams
- Reviewing control effectiveness after changes
- Planning security updates during refactoring
Module 11. Building Internal Security Expertise
Develop the skills and knowledge within AI teams to maintain compliant systems independently.
12 chapters in this module
- Training engineers on ISO 27001 basics
- Creating internal security certification
- Mentoring team security champions
- Developing onboarding for new hires
- Sharing audit lessons across projects
- Creating self-service security resources
- Running security brown bags for AI teams
- Documenting internal security standards
- Establishing peer review practices
- Encouraging security ownership in sprints
- Recognizing security contributions
- Building sustainable learning pathways
Module 12. Creating a Self-Reinforcing Security Practice
Establish systems where each AI product delivery strengthens the organization's overall security maturity.
12 chapters in this module
- Turning audit findings into preventive measures
- Reusing successful control implementations
- Automating lessons learned from incidents
- Creating feedback loops from operations
- Measuring security improvement over time
- Celebrating security wins in AI teams
- Linking security outcomes to business goals
- Demonstrating ROI of security investments
- Documenting maturity progression
- Sharing successes across leadership
- Planning next-generation security initiatives
- Building institutional memory of AI security
How this maps to your situation
- Initial AI product development
- Multi-team AI deployment
- Post-audit improvement cycle
- Third-party AI integration
Before vs. after
Before
Spending weeks compiling evidence for each AI product audit, with inconsistent practices across teams and recurring rework.
After
Producing audit-ready security documentation in days, with standardized patterns that compound across every new AI delivery.
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: Approximately 4 hours per module, designed to be completed in 90-minute Sunday sessions over 12 weeks.
If nothing changes
Continuing with ad-hoc security documentation leads to increased rework, audit findings, and potential delays in AI product launches. Without systematic practices, scaling becomes unsustainable and security gaps multiply across teams.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI engineering leaders, focusing on practical, repeatable systems that integrate with existing development workflows. It emphasizes automation, pattern reuse, and cross-team alignment specific to fast-moving AI environments.
Frequently asked
Is this course focused on technical or managerial aspects of security?
It bridges both , teaching engineering leaders how to implement technical controls while managing team practices and cross-functional coordination.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-AI machine learning systems?
Yes , while optimized for AI, the control patterns apply to any model-driven system requiring compliance assurance.
$199 one-time. Approximately 4 hours per module, designed to be completed in 90-minute Sunday sessions over 12 weeks..
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