A tailored course, built for your situation
Mastering ISO 27001 for Staff AI & ML Engineers
Build trusted, compliant AI systems with auditable security controls that stand up under review
$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.
Vendor evaluation checklists that require last-minute sourcing of control evidence
The situation this course is for
Technical leaders often face delayed or stalled procurement decisions because security evidence isn’t structured to match auditor expectations. The burden falls on engineers to retroactively justify controls, creating rework and slowing deployment.
Who this is for
Senior ICs in AI/ML roles at large tech firms who are increasingly accountable for security and compliance in system design, even without formal InfoSec titles.
Who this is not for
Entry-level engineers, non-technical compliance staff, consultants looking for general ISO 27001 overviews, or leaders outside of AI/ML system ownership.
What you walk away with
- Produce ISO 27001-aligned security narratives that satisfy procurement and audit teams on first review
- Lead vendor selection discussions with pre-built control mappings and evidence templates
- Reduce rework cycles in third-party risk assessments by 70% or more
- Earn consistent 'no follow-up needed' feedback from internal auditors
- Become the go-to technical resource when cross-functional teams need security assurance fast
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27001 in AI Systems
Understand how ISO 27001’s core principles apply specifically to AI/ML infrastructure, data pipelines, and model deployment workflows.
12 chapters in this module
- Defining information security in the context of machine learning systems
- Mapping AI assets to ISO 27001 information security domains
- The role of confidentiality, integrity, and availability in model training
- How ISO 27001 differs from SOC 2 and NIST CSF in AI environments
- Understanding scope boundaries for AI-focused ISMS implementations
- Integrating ISO 27001 with existing Meta engineering governance practices
- Common misconceptions about ISO 27001 applicability to software teams
- Why AI systems create unique risks for access control and logging
- Linking model versioning to information security audit trails
- Documenting asset ownership in distributed AI development teams
- Establishing baseline security classifications for training data
- Creating a living register of AI-related information assets
Module 2. Control Mapping for AI Development Workflows
Translate ISO 27001 controls into actionable practices within CI/CD pipelines, experiment tracking, and MLOps environments.
12 chapters in this module
- Mapping Annex A controls to common AI development stages
- Embedding access control checks in feature store deployments
- Securing model checkpoint storage with role-based policies
- Logging model training jobs for auditability and traceability
- Versioning model weights with cryptographic integrity checks
- Applying least privilege in notebook server environments
- Automating data anonymization in development datasets
- Enforcing encryption in transit for model serving endpoints
- Documenting configuration drift in production AI systems
- Tracking third-party library usage for vulnerability management
- Validating container image provenance in model deployment
- Controlling access to model performance monitoring dashboards
Module 3. Vendor Evaluation and Third-Party Risk
Lead vendor selection processes with structured security assessments aligned to ISO 27001 requirements.
12 chapters in this module
- Designing security questionnaires for AI infrastructure providers
- Mapping vendor responses to ISO 27001 control objectives
- Assessing model explainability tools for compliance readiness
- Evaluating cloud AI platform certifications and audit reports
- Handling sub-processor disclosures in AI service contracts
- Validating SOC 2 reports against ISO 27001 control expectations
- Requesting evidence for physical security in AI training facilities
- Scoping security audits for AI model outsourcing partners
- Documenting risk treatment plans for vendor gaps
- Maintaining signed attestation records for vendor reviews
- Using control matrices to compare multiple AI platform options
- Preparing executive summaries for cross-functional procurement committees
Module 4. Internal Audit Readiness for AI Teams
Produce evidence packs that pass internal and external audits without requiring rework or escalation.
12 chapters in this module
- Anticipating auditor questions about AI model access logs
- Structuring evidence folders for control 5.14 third-party access
- Demonstrating regular backup testing for model artifacts
- Documenting incident response readiness for AI system breaches
- Proving segregation of duties in model deployment workflows
- Auditing change management for prompt engineering updates
- Verifying encryption key rotation schedules for model storage
- Maintaining records of security awareness training for ML teams
- Showing completeness of asset inventory for AI pipelines
- Preparing for control testing in hybrid cloud AI environments
- Responding to auditor findings with root cause and remediation
- Creating closed-loop documentation for recurring audit items
Module 5. Security Policy Interpretation for Engineers
Translate high-level compliance mandates into precise, implementable actions for AI/ML development teams.
12 chapters in this module
- Reading ISO 27001 clauses through the lens of MLOps practices
- Interpreting 'acceptable use policies' for AI experimentation
- Applying password policies to service accounts in automated workflows
- Defining privileged access in model training compute clusters
- Implementing secure coding standards for AI pipeline scripts
- Documenting configuration baselines for ML virtual environments
- Managing cryptographic keys in containerized model serving
- Enforcing session timeouts in interactive AI development tools
- Logging privileged operations in ML pipeline orchestration
- Auditing access to sensitive training data sets
- Handling multi-factor authentication in headless AI services
- Securing API tokens used in automated model retraining
Module 6. Risk Assessment in AI System Design
Conduct ISO 27001-aligned risk assessments tailored to AI/ML system threats and failure modes.
12 chapters in this module
- Identifying asset owners for AI model inference endpoints
- Assessing confidentiality impact of training data leaks
- Evaluating integrity risks in model weight poisoning attacks
- Scoping availability requirements for real-time AI services
- Documenting threat scenarios for adversarial inputs
- Applying risk likelihood metrics to AI system components
- Prioritizing controls based on AI-specific risk rankings
- Integrating risk treatment plans into sprint planning
- Measuring residual risk after control implementation
- Reporting risk posture to technical leadership teams
- Updating risk assessments after model architecture changes
- Archiving risk assessment decisions for audit review
Module 7. Documentation for Compliance at Scale
Create living, reusable documentation that satisfies auditors while minimizing engineer burden.
12 chapters in this module
- Structuring security policies for AI team adoptability
- Maintaining version-controlled compliance playbooks
- Automating evidence collection from CI/CD pipelines
- Building self-updating asset inventories for AI systems
- Centralizing access control matrices for audit access
- Documenting exception approvals with technical justification
- Generating audit-ready reports from version control logs
- Using metadata tagging to associate code with controls
- Creating searchable repositories for auditor access
- Integrating documentation into PR merge gates
- Archiving decommissioned system evidence securely
- Designing documentation for handoff to new team members
Module 8. Incident Response for AI Systems
Prepare for and respond to security incidents involving AI/ML systems with ISO 27001-aligned procedures.
12 chapters in this module
- Defining incident criteria for model performance degradation
- Detecting unauthorized access to model training environments
- Responding to data poisoning attacks in retraining cycles
- Containing breaches involving sensitive inference data
- Preserving forensic evidence in containerized AI systems
- Notifying stakeholders of AI model integrity compromises
- Conducting root cause analysis for model bias incidents
- Testing incident response plans for AI system outages
- Documenting breach timelines for regulatory reporting
- Coordinating with legal teams on AI incident disclosure
- Updating controls based on incident lessons learned
- Archiving incident records with access restrictions
Module 9. Continuous Improvement and Metrics
Establish feedback loops that use audit findings and control testing to improve AI system security over time.
12 chapters in this module
- Tracking control effectiveness in AI deployment pipelines
- Measuring rework cycles in security evidence collection
- Benchmarking audit finding closure rates across teams
- Setting targets for reduction in control gaps
- Using dashboards to monitor ISO 27001 compliance status
- Analyzing trends in vendor assessment outcomes
- Improving policy adoption rates in engineering teams
- Reducing time to produce audit evidence packs
- Increasing first-time pass rate for control reviews
- Evaluating maturity of AI security practices annually
- Sharing metrics with technical leadership constructively
- Adjusting risk treatment plans based on performance data
Module 10. Cross-Functional Collaboration
Work effectively with InfoSec, legal, procurement, and audit teams while maintaining engineering velocity.
12 chapters in this module
- Translating technical details for compliance reviewers
- Negotiating control scope with InfoSec teams reasonably
- Providing evidence in formats auditors can easily verify
- Escalating impractical requirements with alternatives
- Collaborating on risk acceptance decisions for AI projects
- Aligning procurement timelines with security review needs
- Educating non-technical stakeholders on AI risks
- Facilitating joint workshops on control implementation
- Documenting cross-team agreements formally
- Managing expectations around audit readiness dates
- Building trust through consistent evidence delivery
- Creating shared dashboards for compliance visibility
Module 11. Automation of Compliance Workflows
Integrate ISO 27001 requirements into automated engineering systems to reduce manual overhead.
12 chapters in this module
- Embedding control checks in CI/CD pipelines
- Automating evidence collection from logging systems
- Generating compliance reports from infrastructure as code
- Validating access controls via automated testing
- Using static analysis to enforce secure coding rules
- Monitoring configuration drift in AI environments
- Alerting on policy violations in real time
- Auto-documenting system changes for audit trails
- Integrating ticketing systems with control workflows
- Scheduling automated key rotation in model services
- Flagging unapproved changes in production pipelines
- Creating self-healing responses for minor control lapses
Module 12. Sustaining Compliance in Evolving AI Systems
Maintain ISO 27001 compliance as AI models and infrastructure change over time.
12 chapters in this module
- Managing compliance during AI framework migrations
- Updating control mappings for new model architectures
- Re-scoping ISMS boundaries after service changes
- Handling deprecation of third-party AI tools securely
- Preserving audit evidence during system decommissioning
- Adapting policies for generative AI integration
- Reassessing risk after model accuracy improvements
- Documenting control changes in release notes
- Maintaining continuity through team reorganizations
- Updating training materials for new compliance requirements
- Archiving historical compliance data appropriately
- Planning for certification audits in dynamic environments
How this maps to your situation
- AI system design under compliance scrutiny
- Vendor selection with security review gates
- Internal audit cycles for machine learning platforms
- Scaling secure AI practices across teams
Before vs. after
Before
Spending hours assembling security evidence during audits, struggling to align with compliance teams, and facing delays in vendor decisions due to incomplete documentation.
After
Walking into reviews with pre-mapped controls, leading vendor discussions confidently, and reducing audit preparation time by 80% with reusable templates and automated evidence.
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 90 minutes total, designed to be completed in a single Sunday session or broken into shorter blocks.
If nothing changes
Without structured compliance practices, AI initiatives face increased scrutiny, delayed deployments, and higher rework costs as security reviews become more frequent and complex.
How this compares to the alternatives
Unlike generic ISO 27001 courses aimed at compliance staff, this course is tailored to the daily work of AI/ML engineers, focusing on actionable control implementation rather than abstract policy.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I'm not in a security role?
Yes. This course is designed specifically for engineers who must meet security and compliance requirements without being security specialists.
Will this help with actual audit cycles?
Yes. Every module includes templates and examples directly applicable to real-world audit evidence collection and review processes.
$199 one-time. Approximately 90 minutes total, designed to be completed in a single Sunday session or broken into shorter blocks..
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