A tailored course, built for your situation
Mastering ISO 27001 for Senior Machine Learning Engineers
Build compliant AI systems with full ownership of security framework decisions
$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
Senior ML Engineers leading AI agent development in regulated environments
Who this is not for
Junior engineers, non-technical compliance staff, or professionals outside AI/ML system delivery
What you walk away with
- Own end-to-end security control selection for AI agent deployments
- Produce ISO 27001-aligned documentation that passes internal audit review on first submission
- Make real-time decisions on data classification and access policies without escalation
- Lead cross-functional alignment on control implementation with infrastructure and security teams
- Deploy a repeatable control-mapping process across multiple AI projects
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27001 in AI System Context
Learn how ISO 27001 applies specifically to machine learning pipelines and agent-based architectures. Identify which clauses impact data ingestion, model training, and inference workflows.
12 chapters in this module
- Scope of ISO 27001 for AI systems
- Mapping controls to data lifecycle stages
- Identifying applicable Annex A controls
- Control applicability for third-party APIs
- Documenting AI-specific control justifications
- Exemption criteria for research environments
- Integrating privacy-preserving techniques
- Boundary definition for multi-tenant agents
- Control ownership matrix design
- Risk-based control prioritization
- Leveraging existing SOC 2 overlaps
- Cross-walk with NIST AI Risk Framework
Module 2. Control Selection and Justification Process
Build the ability to independently select and justify security controls tailored to AI agent behavior and deployment scope, including documentation that satisfies auditor review.
12 chapters in this module
- Defining control scope per agent type
- Template for control justification statements
- Risk weighting for data sensitivity levels
- Choosing between preventive and detective controls
- Documenting control exceptions
- Versioning control decisions over time
- Linking controls to threat models
- Automated control validation approaches
- Human-in-the-loop control requirements
- Scalability of controls across deployments
- Review cadence for control effectiveness
- Updating control sets post-incident
Module 3. Data Classification and Handling Authority
Gain formal authority to classify data within AI workflows and enforce handling rules, reducing dependency on central security teams for policy decisions.
12 chapters in this module
- Classifying training vs. operational data
- Determining PII presence in conversational logs
- Data sensitivity tiers for chat agents
- Encryption requirements by classification
- Storage duration policies per class
- Access request workflows for classified data
- Audit logging for data access events
- Data retention triggers in agent memory
- Anonymization techniques for debugging
- Handling cross-border data flows
- Vendor data handling assurance checks
- Classification override protocols
Module 4. Access Control Design for AI Agents
Design and implement role-based access controls specific to agent interactions, including user permissions, model access, and backend service connectivity.
12 chapters in this module
- Agent identity and authentication design
- User permission hierarchies
- Service-to-service authentication patterns
- Dynamic access token management
- Principle of least privilege in AI
- Time-bound access grants
- Multi-factor approval for admin actions
- Access revocation workflows
- Session timeout policies
- Access logging and monitoring
- Emergency override procedures
- Third-party integration access
Module 5. Incident Response Planning for AI Systems
Develop response protocols specific to AI agent failures, misuse, or security incidents, ensuring compliance with reporting obligations under ISO 27001.
12 chapters in this module
- Defining AI incident types
- Agent behavior deviation thresholds
- Escalation paths for anomalous output
- User-reported misuse handling
- Model poisoning detection
- Data leakage response protocols
- Automated alerting configurations
- Human review triage process
- Regulatory reporting timelines
- Post-mortem documentation templates
- Containment strategies for live agents
- Recovery and rollback procedures
Module 6. Audit Preparation and Evidence Collection
Produce audit-ready documentation and evidence packages that demonstrate continuous compliance without last-minute effort.
12 chapters in this module
- Audit scope definition
- Evidence collection calendar
- Automated logging configuration
- User access review templates
- Control testing procedures
- Policy attestation workflows
- Artifact version control
- Sampling methodology for audits
- Internal review coordination
- Response to auditor inquiries
- Remediation tracking system
- Audit closure documentation
Module 7. Policy Authoring and Change Management
Write and maintain security policies specific to AI systems, with ownership of standard updates and change control processes.
12 chapters in this module
- AI-specific policy sections
- Policy versioning and approval
- Change request workflow
- Stakeholder review cycle
- Policy dissemination methods
- Training requirements for new policies
- Policy exception handling
- Emergency change process
- Integration with DevOps pipelines
- Policy compliance monitoring
- Review frequency by policy type
- Retirement of outdated policies
Module 8. Third-Party Risk Assessment for AI Vendors
Evaluate and approve third-party AI components and services with documented risk assessments that meet ISO 27001 requirements.
12 chapters in this module
- Vendor due diligence checklist
- Assessing model transparency
- Data usage rights verification
- Security certification validation
- Contractual control obligations
- API security evaluation
- Model update review process
- Vendor incident response expectations
- Right-to-audit clauses
- Performance monitoring metrics
- Vendor offboarding process
- Multi-vendor dependency mapping
Module 9. Secure Development Lifecycle Integration
Embed ISO 27001 controls into the AI development process from design through deployment and monitoring.
12 chapters in this module
- Threat modeling at design phase
- Secure code review practices
- Dependency vulnerability scanning
- Model provenance tracking
- Artifact integrity checks
- Deployment gate controls
- Canary release security checks
- Runtime monitoring configuration
- Automated compliance testing
- Peer review for security controls
- Post-deployment audit trail
- Decommissioning security steps
Module 10. Compliance Automation and Tooling
Leverage tooling to automate compliance evidence collection, control monitoring, and reporting for ISO 27001 in AI environments.
12 chapters in this module
- Log aggregation setup
- Control monitoring dashboards
- Automated policy attestation
- Configuration drift detection
- Evidence collection bots
- Audit-ready report generation
- Integration with ticketing systems
- Alerting for control failures
- Automated access reviews
- Machine learning for anomaly detection
- Tool calibration and tuning
- Vendor tool evaluation criteria
Module 11. Cross-Functional Alignment Strategies
Lead alignment between engineering, security, legal, and compliance teams to streamline control implementation and decision-making.
12 chapters in this module
- Stakeholder identification
- Regular sync meeting design
- Decision log maintenance
- Escalation path definition
- Conflict resolution framework
- Communication templates
- Role clarification diagrams
- Joint control ownership models
- Feedback collection process
- Alignment metrics tracking
- Cross-team onboarding
- Knowledge transfer protocols
Module 12. Sustaining Compliance at Scale
Implement processes that maintain compliance across growing AI agent deployments and evolving business needs.
12 chapters in this module
- Control standardization across projects
- Template reuse strategies
- Centralized playbook management
- Training for new team members
- External auditor coordination
- Regulatory change tracking
- Continuous improvement process
- Benchmarking against peers
- Documentation debt management
- Leadership reporting cadence
- Budgeting for compliance tools
- Future-proofing control designs
How this maps to your situation
- When launching a new AI agent
- Before internal audit cycles
- During vendor selection and integration
- After security incident or near-miss
Before vs. after
Before
Waiting for security team approval to finalize control decisions in AI deployments
After
Autonomously making compliant control selections with documented authority
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 to fit within working weeks alongside delivery responsibilities.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for senior ML engineers leading AI agent development, with decision frameworks that grant real ownership of ISO 27001 control implementation, no abstraction, no theory, just actionable authority.
Frequently asked
Who is this course designed for?
Senior Machine Learning Engineers who lead AI agent development and need formal authority to make security and compliance decisions under ISO 27001.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an audit?
Yes, each module includes templates and checklists used by engineering teams to produce audit-ready documentation and evidence packages.
$199 one-time. Approximately 3 hours per module, designed to fit within working weeks alongside delivery responsibilities..
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