Skip to main content
Image coming soon

Premium engagement picks with ISO 27001 expertise

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Premium engagement picks with ISO 27001 expertise

Master the framework to unlock higher-margin, strategically aligned AI security projects

$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.
Stuck executing low-visibility compliance tasks instead of leading strategic AI security work

The situation this course is for

Many AI engineers are being asked to implement security controls without the structured authority to shape them. This leads to reactive delivery, undervalued contributions, and missed opportunities to lead high-impact initiatives. The difference between executing checklist tasks and owning the design lies in recognized framework fluency, especially ISO 27001.

Who this is for

AI Engineer with hands-on technical experience and growing exposure to compliance frameworks, seeking to transition from implementer to strategic contributor

Who this is not for

Engineers satisfied with executing predefined security checklists or those without client-facing project exposure

What you walk away with

  • Identify and pursue ISO 27001-aligned AI security projects with built-in margin and strategic weight
  • Lead control mapping discussions with confidence, using precise framework language
  • Differentiate your proposals with documented compliance architecture
  • Position yourself as the default pick for cross-functional audits and client-facing security assurances
  • Deliver client-ready Statements of Applicability faster using reusable, auditable templates

The 12 modules (with all 144 chapters)

Module 1. Scoping ISO 27001 for AI-driven environments
Learn how to define the boundaries and applicability of ISO 27001 in systems using machine learning, data pipelines, and automated decisioning. Adapt traditional control scope to dynamic AI architectures.
12 chapters in this module
  1. Defining information assets in AI systems
  2. Mapping data flows in model training pipelines
  3. Identifying custodians in automated workflows
  4. Establishing ISMS boundaries for real-time inference
  5. Classifying sensitivity in AI-generated outputs
  6. Determining regulatory overlap with AI deployments
  7. Handling third-party model dependencies
  8. Assessing vendor control applicability
  9. Integrating audit trails into model logs
  10. Documenting system scope for certification
  11. Identifying exceptions in algorithmic processes
  12. Versioning control scope with model updates
Module 2. Control mapping for AI infrastructure
Translate ISO 27001 controls into technical configurations for cloud AI platforms, containerized workloads, and MLOps pipelines. Turn policy into deployable safeguards.
12 chapters in this module
  1. Applying access control to model endpoints
  2. Securing model weights and checkpoints
  3. Implementing change management for ML models
  4. Enforcing separation of duties in CI/CD for AI
  5. Logging model inference requests securely
  6. Protecting training data from exfiltration
  7. Applying cryptography to embedded AI
  8. Managing secrets in container orchestration
  9. Hardening API gateways for model serving
  10. Controlling physical access to edge AI devices
  11. Enforcing network segmentation in training clusters
  12. Verifying control implementation via code scans
Module 3. Building the Statement of Applicability
Create a defensible, client-ready SoA that demonstrates thoughtful control selection and justifies exclusions with technical rationale specific to AI systems.
12 chapters in this module
  1. Structuring the SoA for technical reviewers
  2. Justifying exclusions in automated systems
  3. Referencing NIST 800-53 mappings in SoA
  4. Documenting control implementation depth
  5. Using architecture diagrams in SoA
  6. Referencing model cards in control narratives
  7. Versioning SoA with model iterations
  8. Aligning SoA with client procurement checklists
  9. Including third-party attestations in SoA
  10. Writing concise rationale for auditors
  11. Linking SoA to risk assessment outcomes
  12. Automating SoA population from CI/CD
Module 4. Designing AI-specific risk assessments
Lead ISO 27001 risk cycles that account for model drift, data poisoning, inference leakage, and unintended bias using framework-compliant methodology.
12 chapters in this module
  1. Identifying asset value in trained models
  2. Assessing confidentiality of training data
  3. Evaluating integrity risks in fine-tuning
  4. Scoring availability impact on real-time AI
  5. Documenting likelihood for model hijacking
  6. Using FAIR for AI control cost-benefit
  7. Incorporating red team findings into risk register
  8. Linking model monitoring to control triggers
  9. Updating risk treatment plans post-deployment
  10. Prioritizing controls for high-risk models
  11. Integrating bias audits into risk scoring
  12. Maintaining risk register version history
Module 5. Leading internal audits for AI systems
Run ISO 27001 compliance reviews tailored to machine learning pipelines, model deployment, and data governance workflows.
12 chapters in this module
  1. Scheduling audits around model release cycles
  2. Sampling inference logs for compliance
  3. Verifying data masking in test environments
  4. Auditing model retraining workflows
  5. Checking access to model endpoints
  6. Reviewing incident response drills for AI
  7. Validating model rollback procedures
  8. Assessing drift detection thresholds
  9. Interviewing MLOps engineers on controls
  10. Documenting audit findings with precision
  11. Prioritizing corrective actions by risk
  12. Reporting trends across multiple AI projects
Module 6. Client-facing compliance storytelling
Shape how technical compliance work is presented to clients, positioning your team as the trusted authority on secure AI delivery.
12 chapters in this module
  1. Translating control maps for nontechnical buyers
  2. Using SoA as a sales differentiator
  3. Preparing compliance demos for procurement
  4. Anticipating auditor follow-up questions
  5. Structuring vendor review presentations
  6. Creating client-ready compliance summaries
  7. Highlighting automation advantages in bids
  8. Positioning ISO 27001 as innovation enabler
  9. Using past audit outcomes in pitches
  10. Demonstrating continuous control monitoring
  11. Linking compliance to model performance
  12. Building trust through transparency narratives
Module 7. Integrating ISO 27001 with DevSecOps
Embed compliance into automated pipelines, reducing manual overhead and increasing consistency across AI deployments.
12 chapters in this module
  1. Shifting security left in model development
  2. Automating control evidence collection
  3. Validating Dockerfiles against baseline
  4. Scanning for hardcoded secrets in notebooks
  5. Embedding SoA references in CI jobs
  6. Triggering compliance checks on merge
  7. Using IaC to enforce control baselines
  8. Generating audit logs from pipeline runs
  9. Versioning control configurations
  10. Alerting on policy deviation in staging
  11. Integrating SCA tools into model pipelines
  12. Enforcing model signing in deployment
Module 8. Managing third-party risk in AI supply chains
Extend ISO 27001 rigor to vendors providing models, data, or infrastructure, ensuring end-to-end compliance integrity.
12 chapters in this module
  1. Assessing vendor compliance posture
  2. Reviewing model provider SOC 2 reports
  3. Auditing data labeling subcontractors
  4. Evaluating cloud provider certifications
  5. Requiring ISO 27001 from AI API vendors
  6. Conducting remote vendor audits
  7. Mapping vendor controls to your SoA
  8. Managing model dependency risks
  9. Tracking SLAs for security incidents
  10. Enforcing contract clauses for audits
  11. Handling data sovereignty in AI APIs
  12. Documenting vendor risk treatment
Module 9. Designing secure AI development environments
Architect compliant workflows for model experimentation, training, and validation that satisfy ISO 27001 control expectations.
12 chapters in this module
  1. Isolating research environments
  2. Controlling access to training data
  3. Auditing notebook execution activity
  4. Enforcing model code review rules
  5. Securing GPU cluster access
  6. Logging dataset downloads
  7. Applying DLP to model outputs
  8. Protecting model intellectual property
  9. Managing service account privileges
  10. Enabling secure collaboration
  11. Hardening Jupyter server configurations
  12. Tracking experiment lineage for audit
Module 10. Implementing continuous monitoring for AI
Transition from point-in-time compliance to ongoing control validation using telemetry, logs, and model monitoring tools.
12 chapters in this module
  1. Setting up real-time control alerts
  2. Monitoring for unauthorized model access
  3. Detecting drift as control failure
  4. Logging inference API calls
  5. Auditing model configuration changes
  6. Tracking data schema evolution
  7. Using SIEM for anomaly detection
  8. Generating compliance dashboards
  9. Automating evidence for recurring audits
  10. Integrating model performance with controls
  11. Alerting on policy deviation thresholds
  12. Maintaining control uptime metrics
Module 11. Preparing for external certification audits
Lead readiness efforts for ISO 27001 Stage 1 and Stage 2 audits, with a focus on AI-specific control evidence and documentation.
12 chapters in this module
  1. Scheduling audit timelines with release cycles
  2. Compiling evidence packs for AI systems
  3. Briefing auditors on model architecture
  4. Demonstrating control effectiveness in production
  5. Responding to auditor questions on AI risks
  6. Providing access to logs and configurations
  7. Mapping evidence to control objectives
  8. Running internal mock audits
  9. Coordinating cross-team audit support
  10. Addressing findings pre-submission
  11. Maintaining audit trail completeness
  12. Finalizing certification scope for AI
Module 12. Scaling compliance across AI portfolios
Replicate success across multiple AI initiatives using standardized templates, playbooks, and reusable artefacts.
12 chapters in this module
  1. Creating model-specific control profiles
  2. Building template Statements of Applicability
  3. Developing onboarding checklists for new AI teams
  4. Standardizing risk assessment workflows
  5. Maintaining a central control library
  6. Sharing audit findings across projects
  7. Training engineers on ISO 27001 basics
  8. Automating compliance documentation
  9. Managing multi-cloud control consistency
  10. Updating playbooks after audits
  11. Scaling reviewer capacity for AI growth
  12. Tracking compliance debt across models

How this maps to your situation

  • Onboarding a new AI service requiring certification
  • Responding to a client's security questionnaire
  • Preparing for an external ISO 27001 audit
  • Designing a secure MLOps platform

Before vs. after

Before
Reactive participation in compliance tasks, limited influence on project selection, reliance on others for framework interpretation
After
Proactive pursuit of high-value engagements, recognized as the go-to for ISO 27001 in AI contexts, and consistently leading strategically aligned projects

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 be completed alongside active projects.

If nothing changes
Continuing to execute without framework fluency means missing premium project opportunities, staying in technical delivery lanes, and being passed over for leadership roles that require compliance authority.

How this compares to the alternatives

Generic ISO 27001 courses focus on traditional IT. This course is tailored to AI engineers who need to apply the standard to machine learning systems, MLOps pipelines, and automated decisioning , with concrete examples and artefacts relevant to current work.

Frequently asked

Who is this course for?
AI Engineers and MLOps practitioners who want to lead ISO 27001-compliant AI deployments and gain access to higher-margin, strategically positioned projects.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover ISO 27001:the current cycle?
Yes, the course includes updated control mappings and implementation guidance aligned with the the current cycle revision.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active projects..

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
!