A tailored course, built for your situation
Mastering ISO 27001 for Senior AI Governance Leaders
Turn AI governance from oversight function to profit centre
$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.
Most AI governance initiatives stall under audit scrutiny because control evidence isn’t embedded early enough
The situation this course is for
AI projects advance quickly, until compliance teams raise concerns about data handling, access controls, or auditability. By then, rework slows delivery and weakens client confidence. Teams that wait to align with ISO 27001 lose budget cycles and strategic influence.
Who this is for
Senior AI or technical governance lead at a consulting or systems integration firm, responsible for structuring compliant, scalable AI deployments that win repeat business
Who this is not for
Junior auditors, dedicated compliance officers without technical AI background, or practitioners focused exclusively on non-commercial AI research
What you walk away with
- Lead ISO 27001-aligned AI engagements that close 30% faster due to upfront control clarity
- Position yourself as the go-to advisor for clients needing compliant AI at commercial scale
- Deliver documentation packages that pass internal and client audit reviews the first time
- Structure milestone-based deliverables that justify premium billing tiers
- Anticipate cross-functional objections with pre-built control mapping tied to real AWS architecture patterns
The 12 modules (with all 144 chapters)
Module 1. Aligning AI Governance with ISO 27001 Control Objectives
Establish the foundational link between AI system design and ISO 27001 control domains, focusing on information security policies that scale across client environments. Learn how to map data flows, access controls, and audit trails from the outset, ensuring compliance is architectural, not administrative.
12 chapters in this module
- Defining the scope of AI systems under ISO 27001
- Mapping data classification to asset inventory requirements
- Linking AI model access controls to user access management
- Documenting security roles within AI project teams
- Establishing clear ownership for information security in AI initiatives
- Integrating security policies with DevOps workflows
- Creating audit-ready records of policy enforcement
- Using ISO 27001 clauses to justify AI security budget
- Aligning AI risk assessments with Clause 6.1.2
- Embedding compliance into sprint planning cycles
- Translating technical decisions into control evidence
- Avoiding common misapplications of A.12 controls in AI
Module 2. Control Mapping for AI-Specific Threat Scenarios
Address real-world AI threats, like model inversion, data poisoning, and prompt leakage, through precise ISO 27001 control mapping. This module equips you to anticipate regulator questions and client concerns with documented responses rooted in proven control logic.
12 chapters in this module
- Identifying high-risk AI components for control focus
- Applying A.14.1 controls to model training pipelines
- Protecting training data integrity under A.8.2.3
- Securing model weights and parameters as intellectual property
- Mapping prompt injection risks to access control policies
- Documenting anomaly detection in AI behaviour
- Using logging standards to meet A.12.4 requirements
- Ensuring model version traceability for audits
- Applying change control to fine-tuning workflows
- Securing model APIs under network access controls
- Handling third-party model dependencies securely
- Auditing AI-specific exceptions to standard policies
Module 3. Designing ISO 27001-Compliant AI Architectures
Build secure AI systems from the ground up using AWS-native patterns aligned with ISO 27001. This module covers infrastructure choices, encryption strategies, and access design that satisfy both technical and compliance requirements.
12 chapters in this module
- Choosing AWS services that simplify ISO 27001 alignment
- Designing VPC isolation for AI inference endpoints
- Encrypting model artifacts at rest and in transit
- Applying least privilege to SageMaker roles
- Securing data lakes feeding AI systems
- Using AWS Key Management Service for model access
- Configuring audit trails with CloudTrail and S3
- Integrating AWS Config with compliance dashboards
- Validating network controls against A.13.1
- Architecting failover for AI-dependent services
- Balancing performance with logging overhead
- Designing immutable evidence pipelines for audits
Module 4. Risk Assessment Integration for AI Projects
Conduct risk assessments that speak to both technical teams and compliance reviewers. This module teaches a repeatable method for identifying AI-specific risks and linking them directly to ISO 27001 controls.
12 chapters in this module
- Adapting ISO 27001 risk methodology for AI use cases
- Identifying model drift as a security risk
- Assessing bias and fairness under information integrity
- Evaluating supply chain risks in pre-trained models
- Scoring risks using likelihood and business impact
- Linking risk findings to specific control clauses
- Documenting risk treatment plans for auditors
- Using heat maps to prioritise remediation
- Integrating risk registers with project management tools
- Reporting residual risk to leadership
- Justifying control investments based on risk exposure
- Updating assessments after model retraining
Module 5. Evidence Generation for AI Governance Audits
Produce clean, consistent evidence packages that satisfy auditors without slowing delivery. This module focuses on automating documentation and structuring artefacts to pass review on first submission.
12 chapters in this module
- Defining audit evidence requirements early
- Creating standard templates for AI control documentation
- Automating evidence collection from CI/CD pipelines
- Generating SoA statements for AI components
- Populating control implementation records reliably
- Using version control for audit trails
- Capturing change approvals in Jira or equivalent
- Linking code commits to control assertions
- Compiling access review records from IAM logs
- Producing training completion reports for AI teams
- Maintaining third-party assessment records
- Preparing consolidation packages for group audits
Module 6. Client Engagement Structuring for Premium Billing
Structure consulting engagements that justify higher margins through clear compliance deliverables. This module shows how to position ISO 27001 work as a value driver, not a cost centre.
12 chapters in this module
- Positioning governance as an accelerant for AI adoption
- Packaging compliance into phased delivery plans
- Creating milestone-based billing tiers
- Selling control mapping as a strategic service
- Differentiating from commoditised audit support
- Using ISO 27001 as a client onboarding lever
- Demonstrating ROI through audit cycle reduction
- Including compliance playbooks as IP deliverables
- Negotiating retainers for ongoing governance
- Bundling AI ethics with security controls
- Pricing based on risk surface complexity
- Extending engagements through compliance maintenance
Module 7. AI Governance in M&A and Due Diligence Contexts
Lead due diligence efforts involving AI systems, ensuring security and compliance positions don’t undermine valuation. This module prepares you to lead technical assessments and articulate risk posture clearly.
12 chapters in this module
- Assessing ISO 27001 posture in target companies
- Reviewing AI model documentation during due diligence
- Identifying compliance gaps that impact valuation
- Mapping data lineage for acquisition targets
- Evaluating third-party AI vendor risks
- Benchmarking security controls against industry peers
- Conducting rapid ISO 27001 gap assessments
- Prioritising post-merger integration activities
- Documenting compliance debt for leadership
- Negotiating indemnities for known control gaps
- Integrating security policies across entities
- Creating unified audit reporting post-acquisition
Module 8. Automating Compliance in CI/CD for AI Systems
Integrate control checks directly into development pipelines to enforce compliance without slowing innovation. This module bridges DevOps and governance through automation.
12 chapters in this module
- Injecting security gates into CI/CD workflows
- Using Infrastructure as Code to enforce controls
- Validating S3 bucket policies before deployment
- Scanning for hardcoded credentials in model code
- Running automated checks against ISO 27001 clauses
- Failing builds on non-compliant configurations
- Generating compliance reports from pipeline outputs
- Integrating SonarQube with security rules
- Enforcing tagging standards for auditability
- Automating access reviews for model endpoints
- Using pre-commit hooks to block risky changes
- Creating audit trails for every pipeline execution
Module 9. Vendor and Third-Party Risk in AI Ecosystems
Manage risks introduced by external AI tools, APIs, and models. This module provides a structured approach to assessing third parties against ISO 27001 requirements.
12 chapters in this module
- Assessing SaaS providers for AI governance fit
- Reviewing model cards for transparency and bias
- Evaluating API security posture for AI services
- Conducting ISO 27001-aligned supplier assessments
- Using SIG questionnaires effectively
- Mapping third-party controls to internal gaps
- Requiring audit reports from key vendors
- Monitoring compliance through continuous assessment
- Managing open-source model risks
- Ensuring data processing agreements cover AI use
- Enforcing right-to-audit clauses
- Building exit strategies for third-party AI
Module 10. Communicating AI Governance to Executive Stakeholders
Translate technical control work into strategic narratives that resonate with leadership. This module teaches how to position compliance as business enabler.
12 chapters in this module
- Framing ISO 27001 as competitive advantage
- Telling the story of secure AI innovation
- Using metrics to show governance impact
- Relating control posture to client trust
- Presenting risk reduction as value creation
- Avoiding technical jargon in leadership updates
- Linking compliance to revenue protection
- Benchmarking against industry peers
- Highlighting audit success in business terms
- Positioning governance as innovation enabler
- Demonstrating resilience to regulators
- Connecting AI controls to ESG outcomes
Module 11. Continuous Monitoring and Improvement of AI Controls
Move beyond point-in-time compliance to continuous assurance. This module shows how to monitor AI systems and update controls as threats evolve.
12 chapters in this module
- Setting up dashboards for control health
- Tracking model performance as security signal
- Using drift detection to trigger control reviews
- Scheduling periodic access recertification
- Updating risk assessments after incidents
- Integrating threat intelligence into control reviews
- Conducting tabletop exercises for AI breaches
- Measuring control effectiveness over time
- Aligning with NIST CSF for continuous monitoring
- Using automated tools for control validation
- Reporting improvement trends to leadership
- Planning annual control refresh cycles
Module 12. Scaling AI Governance Across Global Engagements
Extend proven governance patterns across multiple clients and regions. This module focuses on reuse, consistency, and local adaptation of ISO 27001 approaches.
12 chapters in this module
- Creating modular control frameworks for reuse
- Adapting ISO 27001 for regional data laws
- Standardising documentation across projects
- Training teams on core governance principles
- Building internal communities of practice
- Capturing lessons from client engagements
- Developing playbooks for common AI patterns
- Scaling through managed templates
- Ensuring consistency across delivery teams
- Localising compliance for EU, UK, and APAC
- Maintaining version control for frameworks
- Packaging governance IP for productised offerings
How this maps to your situation
- AI governance in consulting-led environments
- ISO 27001 application to modern cloud-native AI
- Compliance as a client value driver
- Technical leadership in regulated AI deployment
Before vs. after
Before
Spending cycles justifying compliance work, reworking deliverables for audit, and losing premium engagements to firms that position governance as value
After
Leading high-margin AI governance projects from day one, delivering clean audit outcomes, and commanding pricing based on control clarity and risk reduction
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 3, 4 hours per module, designed for completion over 6, 8 weeks with real-world application between modules.
If nothing changes
Without sharpening the commercial positioning of AI governance, there’s a growing risk of being seen as gatekeeper rather than growth partner, leading to commoditised work and marginalised influence in strategic AI initiatives.
How this compares to the alternatives
Unlike generic ISO 27001 courses, this programme is tailored to AI governance leaders in consulting roles, blending technical depth, client engagement strategy, and compliance precision. No other course integrates AWS architecture patterns, ISO 27001 controls, and premium billing structuring for AI projects.
Frequently asked
Is this course focused on technical or strategic aspects?
It balances both, technical control mapping for AI systems and strategic structuring of client engagements to justify higher margins.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive templates I can use with clients?
Yes, every module includes downloadable templates and worked examples designed for real consulting use.
$199 one-time. Approximately 3, 4 hours per module, designed for completion over 6, 8 weeks with real-world application between modules..
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