A tailored course, built for your situation
Mastering ISO 27001 for ML/AI Infrastructure Engineers
Build trusted, auditable AI systems with precision and consistency
$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 for AI systems that span teams, regions, and infra layers takes weeks to reconcile, just as scaling pressure intensifies.
The situation this course is for
At Meta and similar organizations, ML infra engineers are increasingly on the hook for security documentation that crosses silos. The challenge isn’t technical capability, it’s producing coherent, consistent, and reusable trust artefacts under tight cycles, especially when audit teams come calling. Without a standardized approach, evidence collection becomes a last-minute scramble across model registries, access logs, and deployment manifests.
Who this is for
IC-level infrastructure engineer at a major tech firm, focused on scalable, secure AI systems. Deep in the weeds of deployment pipelines, access controls, and model lifecycle governance. Values precision, quiet influence, and technical credibility across teams.
Who this is not for
This course isn’t for consultants selling compliance, policy writers without engineering experience, or leaders looking for board-level summaries. It’s for practitioners who ship code and own systems.
What you walk away with
- Produce ISO 27001-aligned System of Authority (SoA) documentation in under 6 hours per release cycle
- Standardize control mappings across AI model repositories and infra layers
- Automate evidence collection for A.9, A.12, and A.14 controls in CI/CD pipelines
- Gain peer recognition as a trusted source on cross-team security reviews
- Reduce rework in audit cycles by shipping pre-validated control packages
The 12 modules (with all 144 chapters)
Module 1. Why ISO 27001 Matters for AI Infrastructure
Understand how ISO 27001’s structure supports not just compliance, but system clarity and cross-team trust in AI deployments.
12 chapters in this module
- How ISO 27001 maps to AI system boundaries
- The difference between policy and implementation evidence
- Control A.5.1 and infrastructure ownership clarity
- Linking security roles to CI/CD stage gates
- Why auditors focus on change logs in model pipelines
- Building trust across security and ML teams
- The cost of inconsistent control tagging
- Security as a feature of reliable systems
- How Meta’s scale creates unique evidence challenges
- Where ISO 27001 aligns with internal red team expectations
- The role of documentation in incident response speed
- From reactive fixes to proactive design
Module 2. Mapping AI Systems to Clauses 4, 7
Define scope, context, leadership, and planning for AI infra in alignment with ISO 27001’s foundational clauses.
12 chapters in this module
- Identifying AI system boundaries in hybrid cloud environments
- Documenting external dependencies for audit readiness
- Assigning information security roles in large teams
- Creating a risk treatment plan for model drift
- Planning for AI-specific availability requirements
- Integrating security into AI project charters
- How clause 6.1 applies to automated pipelines
- Defining acceptable risk thresholds for model access
- Tracking control ownership across time zones
- Aligning infrastructure logging with clause 6.2
- The role of asset inventories in containerized systems
- When to escalate deviations to cross-functional leads
Module 3. Designing Controls for A.9 Access Management
Secure AI systems with precise access controls that satisfy auditors and scale across teams.
12 chapters in this module
- Implementing role-based access in model registries
- Enforcing least privilege in training clusters
- Using service accounts securely in pipelines
- Logging access to model endpoints
- Rotating keys in automated workflows
- Managing access revocation during team transitions
- Control A.9.2.3 in API gateway configurations
- Multi-factor authentication for admin actions
- Detecting and blocking brute-force attempts
- Maintaining access reviews under high velocity
- Integrating identity providers at scale
- Auditing access changes in CI/CD systems
Module 4. Automating A.12 Operational Security
Embed audit-ready logging, monitoring, and change control into AI infrastructure workflows.
12 chapters in this module
- Logging model training jobs end-to-end
- Standardizing log formats across services
- Detecting unauthorized changes in production
- Implementing immutable logs for compliance
- Using checksums to validate model binaries
- Setting thresholds for anomaly detection
- Controlling software installation in clusters
- Managing technical vulnerabilities in dependencies
- Scheduling automated compliance checks
- Integrating SIEM alerts with on-call rotations
- Documenting exception handling procedures
- Validating remediation steps for audit trails
Module 5. Securing AI Development Lifecycle (A.14)
Apply security controls across model ideation, development, testing, and deployment.
12 chapters in this module
- Integrating security into model development charters
- Controlling access to training data sets
- Securing model validation environments
- Using secure coding practices in AI scripts
- Managing third-party libraries in ML projects
- Enforcing code reviews for model pipelines
- Protecting intellectual property in open environments
- Securing model export and serialization steps
- Validating deployment packages pre-release
- Documenting security decisions in model cards
- Testing for data leakage in inference paths
- Tracking model version provenance
Module 6. Protecting Data with A.10 Cryptography
Implement encryption in transit and at rest for AI systems handling sensitive data.
12 chapters in this module
- Choosing encryption standards for model payloads
- Managing keys in distributed AI systems
- Using envelope encryption for model parameters
- Securing model checkpoints in cloud storage
- Implementing TLS for inter-service communication
- Avoiding hardcoded credentials in scripts
- Auditing cryptographic control usage
- Updating cipher suites in legacy pipelines
- Handling key rotation in automated systems
- Validating cryptographic implementations
- Documenting encryption exceptions
- Aligning with internal crypto review boards
Module 7. Building Resilience with A.17 Availability
Ensure AI systems remain available and recoverable under stress and failure.
12 chapters in this module
- Defining uptime requirements for inference APIs
- Implementing redundancy in model serving layers
- Backups for model metadata and configurations
- Recovery testing for training pipelines
- Monitoring resource exhaustion in clusters
- Documenting failover procedures
- Protecting against denial-of-service in APIs
- Capacity planning for model scaling
- Incident response roles in outages
- Testing recovery under real traffic
- Aligning recovery time objectives with product needs
- Logging system state during outages
Module 8. Vendor and Supply Chain Controls (A.15)
Manage third-party risks in AI tooling, platforms, and data providers.
12 chapters in this module
- Assessing security posture of AI platform vendors
- Reviewing contractual SLAs for compliance
- Managing access for vendor support staff
- Auditing third-party code in pipelines
- Tracking open-source license compliance
- Securing APIs used in model workflows
- Validating data provenance from external sources
- Monitoring vendor changes in production
- Enforcing security requirements in onboarding
- Documenting vendor risk decisions
- Handling breaches in third-party components
- Renewal cycles with security reassessment
Module 9. Incident Management and A.16
Prepare for, respond to, and document AI system security incidents.
12 chapters in this module
- Detecting model poisoning attempts
- Logging suspicious inference patterns
- Incident escalation paths in large orgs
- Containing compromised training jobs
- Documenting incident timelines
- Conducting post-mortems with compliance teams
- Reporting to internal stakeholders
- Testing incident playbooks
- Integrating with central security teams
- Preserving evidence for audits
- Updating controls after incidents
- Communicating fixes without panic
Module 10. Creating Reusable Audit Artefacts
Design documentation and evidence packages that survive team changes and scale across systems.
12 chapters in this module
- Structuring System of Authority (SoA) documents
- Versioning control narratives
- Using templates for consistency
- Automating narrative generation from logs
- Linking controls to CI/CD stages
- Tagging evidence by region and team
- Building reviewer-friendly artefacts
- Using diagrams to show control flows
- Generating compliance reports automatically
- Maintaining artefacts across releases
- Aligning with internal auditor expectations
- Reducing evidence refresh effort
Module 11. Integrating ISO 27001 into CI/CD Pipelines
Embed compliance checks and evidence generation into deployment workflows.
12 chapters in this module
- Inserting control validation in pre-merge checks
- Running automated security scans in CI
- Enforcing documentation as code
- Validating access controls before deployment
- Checking for unapproved dependencies
- Signing off releases with audit trails
- Blocking rollouts missing evidence
- Using gates for high-risk changes
- Logging pipeline decisions
- Reconciling production changes
- Updating asset inventories automatically
- Generating compliance dashboards
Module 12. Sustaining Compliance Across Evolving AI Systems
Keep ISO 27001 alignment during rapid iteration and architectural shifts.
12 chapters in this module
- Updating SoAs for model architecture changes
- Tracking control drift in agile teams
- Revalidating controls after refactors
- Auditing new data sources quickly
- Scaling documentation with team growth
- Onboarding new engineers to compliance standards
- Using feedback from auditors to improve
- Maintaining versioned control mappings
- Planning for annual certification cycles
- Aligning with cross-functional security leads
- Balancing speed and compliance rigor
- Documenting exceptions with accountability
How this maps to your situation
- Audit evidence readiness
- Cross-team control alignment
- Automated compliance in pipelines
- Incident-ready system design
Before vs. after
Before
Spending cycles stitching together audit evidence across distributed AI systems, responding to reviewer requests, and explaining control choices after deployment.
After
Shipping pre-validated, ISO 27001-aligned system documentation with every release, recognized as a trusted source across security, audit, and engineering teams.
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: 90 minutes of focused reading, plus optional deep dives into templates and implementation examples.
If nothing changes
Without a structured approach, audit cycles will consume increasing time, control inconsistencies will grow across teams, and security incidents may expose gaps that erode trust in AI systems.
How this compares to the alternatives
Unlike generic compliance courses, this is tailored to ML infrastructure engineers. It skips board-level summaries and focuses on artefacts you actually produce: control mappings, SoA narratives, CI/CD gates, and audit packages. No fluff, no theory, just what ships.
Frequently asked
Is this relevant if I don’t own compliance at my company?
Yes. This course is for engineers who need to produce compliant systems, not for compliance officers. It helps you build systems that pass review without rework.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need prior ISO 27001 experience?
No. The course assumes technical infrastructure knowledge but walks you through ISO 27001 in concrete, engineer-friendly terms.
$199 one-time. 90 minutes of focused reading, plus optional deep dives into templates and implementation examples..
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