A tailored course, built for your situation
Mastering ISO 27001 for AI-ML Developers in Global Enterprise Roles
Build defensible, production-grade AI systems with structured information security governance
$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.
Who this is for
AI-ML Developer at a global systems integrator working on GenAI and Agentic AI projects requiring compliance-aware system design
Who this is not for
Entry-level developers, non-technical stakeholders, or practitioners focused solely on consumer AI applications without enterprise compliance requirements
What you walk away with
- Produce complete ISO 27001-compliant documentation for AI systems on the first attempt
- Structure risk assessments that align with enterprise security expectations
- Map technical AI controls directly to ISO 27001 Annex A domains
- Generate audit-ready System of Controls narratives without revision loops
- Integrate compliance thinking into early-stage AI development workflows
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27001 in AI Contexts
Ground ISO 27001 principles in real-world AI-ML development cycles and enterprise integration patterns.
12 chapters in this module
- Scope definition for AI systems
- Risk assessment tailored to GenAI
- Asset identification in agentic workflows
- Threat modeling for autonomous agents
- Vulnerability mapping in LLM pipelines
- Control objectives for data provenance
- Compliance boundary setting
- Documentation structure basics
- Role-based access in AI teams
- Change management for AI models
- Third-party AI vendor risks
- Audit readiness fundamentals
Module 2. Building the Information Security Manual
Step-by-step creation of an auditable Information Security Manual specific to AI development.
12 chapters in this module
- Title page and version control
- Purpose and scope statements
- Governance framework overview
- Roles and responsibilities matrix
- Risk assessment methodology
- Control selection rationale
- Compliance reporting structure
- Incident response linkage
- Change control process
- Audit schedule integration
- Continuous improvement loop
- Document maintenance policy
Module 3. Risk Assessment for AI Systems
Apply ISO 27001 risk methodology to AI-specific threats and data flows.
12 chapters in this module
- AI asset categorization
- Data classification in LLM contexts
- Model drift as security risk
- Prompt injection threats
- Fine-tuning data leakage
- Output confidentiality controls
- Model inversion risks
- Adversarial attack surfaces
- Human-in-the-loop safeguards
- Third-party model dependencies
- Supply chain transparency
- Risk treatment plan drafting
Module 4. Control Mapping for GenAI Workflows
Align technical AI safeguards with ISO 27001 Annex A controls.
12 chapters in this module
- Access control for model endpoints
- Authentication in agent networks
- Model version control policies
- Environment segregation strategies
- Secure API design for AI services
- Logging for autonomous agents
- Monitoring for anomalous behavior
- Data retention in vector stores
- Encryption for model weights
- Key management for AI systems
- Secure development lifecycle
- Vendor risk in model APIs
Module 5. Documenting the Statement of Applicability
Create a defensible, clear SoA that aligns AI controls with ISO 27001 requirements.
12 chapters in this module
- SoA structure and format
- Mandatory control justification
- Applicable control selection
- Exclusion rationale writing
- Control implementation status
- Ownership assignment
- Review and approval workflow
- Linking to risk register
- Version control practices
- Audit trail setup
- Stakeholder alignment
- Maintenance responsibilities
Module 6. Internal Audit Preparation
Structure evidence collections and narratives for AI system audits.
12 chapters in this module
- Audit scope definition
- Evidence collection planning
- Interview preparation
- Control testing procedures
- Non-conformance tracking
- Remediation planning
- Audit report drafting
- Follow-up timelines
- Cross-functional coordination
- Executive summary writing
- Lessons learned capture
- Audit schedule integration
Module 7. AI System Risk Treatment Plans
Develop actionable plans that close security gaps in AI deployments.
12 chapters in this module
- Risk treatment options
- Mitigation vs acceptance
- Transfer considerations
- Avoidance strategies
- Action item prioritization
- Owner assignment
- Timeline development
- Resource planning
- Progress tracking
- Status reporting
- Escalation paths
- Closure criteria
Module 8. Secure AI Development Lifecycle
Integrate ISO 27001 practices into GenAI development workflows.
12 chapters in this module
- Requirements gathering with security
- Design phase controls
- Secure coding standards
- Model training safeguards
- Testing for compliance
- Deployment gate reviews
- Post-deployment monitoring
- Incident response linkage
- Model update procedures
- Decommissioning steps
- Documentation updates
- Lifecycle audit trails
Module 9. Third-Party AI Vendor Management
Apply ISO 27001 vendor controls to external AI providers and tools.
12 chapters in this module
- Vendor selection criteria
- Due diligence process
- Contractual security terms
- API security assessment
- Model licensing compliance
- Data handling assurances
- Sub-processor transparency
- Penetration testing rights
- Incident notification SLAs
- Audit rights negotiation
- Performance monitoring
- Exit strategy planning
Module 10. Incident Response for AI Systems
Design response plans tailored to AI-specific failure modes and attacks.
12 chapters in this module
- Incident classification
- Alerting mechanisms
- Model rollback procedures
- Adversarial input analysis
- Data poisoning response
- Reputation damage control
- Legal and compliance notification
- Forensic data collection
- Root cause investigation
- Remediation timeline
- Communication plan
- Post-incident review
Module 11. Continuous Improvement in AI Security
Implement feedback loops to evolve AI controls based on audits and incidents.
12 chapters in this module
- Performance metric design
- Control effectiveness reviews
- Audit finding follow-up
- Lessons learned integration
- Policy update procedures
- Training refresh cycles
- Stakeholder feedback
- Benchmarking against peers
- Technology change adaptation
- Regulatory update tracking
- Maturity assessment
- Roadmap development
Module 12. Integration with Enterprise Security Frameworks
Position AI-ML security within broader organizational compliance programs.
12 chapters in this module
- Linking to SOC 2
- Alignment with NIST CSF
- Mapping to GDPR
- Coordination with IT security
- Enterprise risk reporting
- Board-level summaries
- Cross-functional alignment
- Shared service models
- Centralized logging
- Unified policy enforcement
- Compliance automation
- Enterprise maturity goals
How this maps to your situation
- Preparing for first enterprise AI audit
- Responding to client compliance requests
- Leading AI security within development team
- Transitioning from prototype to production
Before vs. after
Before
Spending extra cycles revising AI system documentation to meet compliance review standards
After
Producing accurate, polished, and auditable ISO 27001-aligned outputs on first 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 3 hours per module, designed for evening or weekend study around client commitments.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI-ML developers working in enterprise environments, with concrete examples from GenAI and agentic AI implementations.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I don't work in security?
Yes. This course teaches how to build better AI systems by integrating security and compliance thinking early, directly applicable to AI-ML developers.
Will I receive a certification?
No. This course builds practical implementation skills, not exam preparation.
$199 one-time. Approximately 3 hours per module, designed for evening or weekend study around client commitments..
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