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AIG0657 Mastering ISO 27001 for Senior Machine Learning Engineers

$199.00
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A tailored course, built for your situation

Mastering ISO 27001 for Senior Machine Learning Engineers

Build auditable, enterprise-grade security integration into ML systems with confidence and precision

$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.
Security documentation for compliance reviews shouldn’t require rework cycles.

Who this is for

Senior ML Engineer at a high-growth tech firm who bridges advanced model development and enterprise compliance requirements, often pulled into cross-functional reviews without formal ownership.

Who this is not for

Junior developers new to ML systems, auditors focused only on checklists, or executives setting top-down policy without technical involvement.

What you walk away with

  • Produce ISO 27001 control mappings directly from ML system design decisions
  • Reduce time spent on compliance evidence cycles by 60, 70%
  • Lead internal conversations on secure AI deployment with authority
  • Anticipate auditor questions and structure documentation proactively
  • Turn security integration into a repeatable pattern across model deployments

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 27001 in Machine Learning Contexts
Understand how ISO 27001 applies specifically to ML infrastructure and model lifecycle management.
12 chapters in this module
  1. Why ISO 27001 matters for AI and ML systems today
  2. Mapping AI risks to information security domains
  3. The role of ML engineers in security compliance
  4. How auditors assess AI-driven systems
  5. Key terms: Information asset, control objective, risk treatment
  6. Common misconceptions about ISO 27001 for technical teams
  7. Regulatory drivers behind secure AI adoption
  8. Integration points between DevSecOps and ISO 27001
  9. Case example: A cloud ML platform’s compliance journey
  10. Scope definition for AI projects under ISO 27001
  11. Understanding Statement of Applicability (SoA) requirements
  12. Setting expectations for cross-functional collaboration
Module 2. Identifying Information Assets in ML Workflows
Learn to classify data, models, and infrastructure as formal assets subject to control.
12 chapters in this module
  1. What counts as an information asset in ML systems
  2. Classifying training data by sensitivity level
  3. Model weights as confidential intellectual property
  4. Tracking data lineage for compliance purposes
  5. Storage locations of intermediate artifacts
  6. API endpoints serving ML models as controlled interfaces
  7. Version control repositories as asset inventories
  8. Labeling data flows in distributed environments
  9. Documenting asset ownership across teams
  10. Automating asset discovery in CI/CD pipelines
  11. Handling third-party data dependencies securely
  12. Maintaining asset registers for audit readiness
Module 3. Risk Assessment for ML Infrastructure
Apply ISO 27001 risk methodology to real ML deployment scenarios.
12 chapters in this module
  1. Adapting risk assessment templates to ML use cases
  2. Identifying threats to model integrity and confidentiality
  3. Evaluating impact of data leakage in AI systems
  4. Likelihood scoring for adversarial attacks on models
  5. Using threat modeling frameworks alongside ISO 27001
  6. Involving ML engineers in formal risk workshops
  7. Quantifying risk exposure for executive summaries
  8. Prioritizing risks based on business impact
  9. Aligning with NIST CSF when applicable
  10. Documenting risk treatment decisions transparently
  11. Avoiding over-engineering low-probability risks
  12. Updating assessments after model retraining
Module 4. Defining Applicable Controls from Annex A
Select and justify controls relevant to AI and ML environments.
12 chapters in this module
  1. Navigating ISO 27001 Annex A control categories
  2. Control A.5.1: Policies for AI system development
  3. A.7.1: User access control in model training environments
  4. A.8.1: Classification of ML-related data
  5. A.8.2: Labelling models and datasets securely
  6. A.8.3: Handling encrypted model storage
  7. A.8.23: Protecting against model inversion attacks
  8. A.13.1: Secure transmission of model updates
  9. A.14.1: Secure development lifecycle for AI
  10. A.14.2: Security testing in model pipelines
  11. A.14.3: Protection of test environments
  12. A.15.1: Vendor control for AI platform providers
Module 5. Building a Statement of Applicability (SoA)
Create a defensible, engineer-led SoA that reflects ML-specific decisions.
12 chapters in this module
  1. Purpose and structure of a compliant SoA
  2. Justifying inclusion or exclusion of controls
  3. Including ML-specific rationale for control selection
  4. Writing technical justifications that pass audit
  5. Versioning SoA documents with model releases
  6. Linking SoA entries to implementation evidence
  7. Using automation to track SoA compliance status
  8. Collaborating with compliance teams on wording
  9. Common pitfalls in SoA drafting for AI
  10. Updating SoA after infrastructure changes
  11. Aligning with COBIT control objectives when needed
  12. Presenting SoA in internal review meetings
Module 6. Security in Model Development Lifecycle
Embed controls into ML design, training, and deployment.
12 chapters in this module
  1. Integrating security gates in model development
  2. Secure coding practices for ML scripts
  3. Access control for notebook environments
  4. Version control for model parameters and code
  5. Hardening container images for training jobs
  6. Securing GPU cluster access
  7. Environment segregation for ML stages
  8. Logging model training activity for audit
  9. Monitoring unauthorized model exports
  10. Secure deletion of obsolete models
  11. Handling personal data in training sets
  12. Privacy-preserving techniques in model design
Module 7. Access Control and Identity Management
Implement least privilege and authentication in ML systems.
12 chapters in this module
  1. Role-based access for ML teams
  2. Managing service accounts for model servers
  3. Authentication for real-time model APIs
  4. Secrets management in model deployment
  5. Multi-factor authentication for admin access
  6. Audit trails for access changes
  7. Just-in-time access for debugging
  8. Revocation policies for departing team members
  9. Federated identity for cross-org collaboration
  10. Monitoring anomalous access patterns
  11. Integrating with existing IAM systems
  12. Balancing security with experimentation speed
Module 8. Incident Management for AI Systems
Prepare for and respond to security events involving ML.
12 chapters in this module
  1. Defining incidents in ML context
  2. Detecting model drift as a security signal
  3. Responding to adversarial input attacks
  4. Data poisoning incident triage
  5. Model exfiltration detection methods
  6. Incident reporting procedures for engineers
  7. Forensic data collection from training runs
  8. Communication plan during AI-related breaches
  9. Post-mortem documentation for compliance
  10. Updating controls after incident review
  11. Simulating AI-specific incident scenarios
  12. Coordination with SOC and legal teams
Module 9. Auditing and Continuous Monitoring
Automate evidence collection and ensure audit readiness.
12 chapters in this module
  1. Automated log aggregation for compliance
  2. Tracking model deployment approvals
  3. Monitoring for unauthorized model changes
  4. Continuous control validation in production
  5. Generating auditor-ready reports automatically
  6. Integrating with GRC platforms
  7. Sampling frequency for audit evidence
  8. Alerting on control deviations
  9. Time-stamped configuration snapshots
  10. Maintaining immutable logs for AI systems
  11. Audit trail retention policies
  12. Demonstrating control effectiveness over time
Module 10. Vendor and Third-Party Risk in AI
Assess and manage security in external AI tools and platforms.
12 chapters in this module
  1. Evaluating security posture of AI SaaS vendors
  2. Reviewing SOC 2 reports for cloud ML providers
  3. Managing dependencies in open-source AI libraries
  4. Contractual obligations for model explainability
  5. Data processing agreements for training data
  6. Right-to-audit clauses in AI vendor contracts
  7. Managing model cards and transparency reports
  8. Security reviews for API-based AI services
  9. Monitoring third-party libraries for vulnerabilities
  10. Tracking license compliance for ML frameworks
  11. Handling model updates from external providers
  12. Exit strategies for AI vendor lock-in
Module 11. Training and Awareness for ML Teams
Foster a culture of compliance within technical teams.
12 chapters in this module
  1. Communicating ISO 27001 relevance to engineers
  2. Designing role-specific security training
  3. Integrating compliance into onboarding
  4. Creating internal documentation standards
  5. Hosting brown-bag sessions on AI security
  6. Building shared ownership of security goals
  7. Encouraging reporting of security concerns
  8. Gamifying secure development practices
  9. Measuring security awareness improvement
  10. Linking performance reviews to security behavior
  11. Recognizing secure engineering contributions
  12. Scaling training across distributed teams
Module 12. Maintaining Certification and Continuous Improvement
Sustain ISO 27001 compliance across evolving AI projects.
12 chapters in this module
  1. Preparing for internal and external audits
  2. Scheduling surveillance assessments
  3. Updating documentation with system changes
  4. Revisiting risk assessments annually
  5. Tracking non-conformities and remediation
  6. Implementing corrective actions efficiently
  7. Leveraging audits to improve ML systems
  8. Benchmarking against peer organizations
  9. Demonstrating continuous improvement
  10. Renewing certification smoothly
  11. Sharing best practices across teams
  12. Scaling ISO 27001 to new AI initiatives

How this maps to your situation

  • Mid-career ML engineer in a regulated environment
  • Technical lead responsible for system design
  • Individual contributor shaping security practices
  • Bridge between engineering and compliance teams

Before vs. after

Before
Spending extra hours translating technical work into compliance language, reacting to audit requests, and defending security choices.
After
Producing audit-ready security documentation proactively, leading design conversations with authority, and expanding influence from your current role.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • 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 80 minutes of content, designed to be consumed in focused 6, 8 minute blocks.

If nothing changes
Without structured integration of security frameworks, even strong technical work may be questioned during compliance reviews, limiting your ability to lead in AI security conversations.

How this compares to the alternatives

Unlike generic compliance trainings or broad ISO 27001 overviews, this course is tailored to the technical reality of ML engineers, focusing on actionable integration of controls into real systems, not theoretical checklists.

Frequently asked

Is this course suitable for someone without formal security training?
Yes. It’s designed for ML engineers who need to engage confidently with security and compliance without becoming full-time auditors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an actual ISO 27001 audit?
Yes. The course teaches you how to produce evidence and documentation that aligns with auditor expectations for technical systems.
$199 one-time. Approximately 80 minutes of content, designed to be consumed in focused 6, 8 minute 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
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