A tailored course, built for your situation
Mastering ISO 27001 for AI/ML Engineers in Data Science
Build trusted AI systems with compliance-ready security controls
$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.
Stop reworking security controls every audit cycle
Who this is for
AI/ML Engineer in large-scale data science teams operating under enterprise compliance mandates
Who this is not for
Engineers working on non-enterprise AI use cases without compliance exposure, or those not involved in deployment lifecycle ownership
What you walk away with
- Produce ISO 27001-compliant documentation as a natural byproduct of model development
- Own the security narrative in cross-functional AI governance reviews
- Reduce audit preparation time by automating control evidence generation
- Speak confidently to security stakeholders with framework-backed justification
- Design AI systems with embedded compliance, not retrofitted controls
The 12 modules (with all 144 chapters)
Module 1. Why ISO 27001 Matters for AI/ML Engineers
Understand how information security standards directly impact model deployment velocity and team autonomy in enterprise settings.
12 chapters in this module
- The rising cost of insecure AI deployments in regulated industries
- How ISO 27001 intersects with MLOps pipelines today
- Real cases where security controls blocked model rollouts
- The engineer's role in preventing compliance drift
- Mapping model lifecycle phases to ISO 27001 clauses
- Common misconceptions about compliance and AI innovation
- Where AI security differs from traditional IT assets
- The business impact of control failures in model inference
- How compliance builds stakeholder trust in AI outputs
- The link between documentation quality and review speed
- Security as a force multiplier for AI adoption
- Building compliance into your definition of 'done'
Module 2. AI System Boundaries and Asset Classification
Define what counts as an information asset in an AI pipeline and how to classify it under ISO 27001.
12 chapters in this module
- Identifying data flows in training and inference paths
- Classifying datasets by sensitivity and retention needs
- Mapping model weights as protected information assets
- Defining API endpoints as controlled access points
- Documenting third-party dependencies in model stacks
- Creating asset inventories that survive team changes
- Versioning models and their associated metadata
- Tracking temporary assets in CI/CD pipelines
- Setting classification rules for synthetic data outputs
- Handling model decay as an asset lifecycle issue
- Integrating asset tracking with existing data catalogs
- Automating asset discovery in cloud ML environments
Module 3. Access Control Design for Model Development
Architect role-based access that satisfies both security requirements and collaboration needs.
12 chapters in this module
- Designing least-privilege access for ML engineers
- Securing model registry access with least-permission
- Controlling access to training data repositories
- Managing service accounts for automated pipelines
- Role definitions for cross-functional AI teams
- Temporary access patterns for incident response
- Authentication mechanisms for model APIs
- Audit logging requirements for access changes
- Handling contractor access in agile projects
- Balancing security with experimentation freedom
- Automated access reviews for dormant accounts
- Integrating IAM with model deployment gates
Module 4. Secure Development Lifecycle for AI
Integrate security checks into model development without slowing innovation.
12 chapters in this module
- Embedding threat modeling in sprint planning
- Secure coding practices for Python and PySpark
- Dependency scanning for ML libraries and packages
- Unit testing with security validation hooks
- Static analysis tailored to Jupyter notebooks
- Secrets management in containerized training jobs
- Code signing requirements for model artifacts
- Vulnerability management in open-source AI tools
- Patch management for GPU-enabled environments
- Secure model serialization formats
- Automated security gates in CI/CD pipelines
- Version control discipline for reproducible results
Module 5. Data Protection in Model Training and Inference
Implement data protection controls that meet ISO 27001 without compromising model performance.
12 chapters in this module
- Anonymization techniques that preserve model utility
- Data masking strategies for development environments
- Encryption requirements for data at rest and in transit
- Tokenization of sensitive features in training sets
- Differential privacy considerations for AI models
- Data retention policies for training artifacts
- Handling PII in real-time inference pipelines
- Data subject rights fulfillment in ML systems
- Auditable data lineage tracking
- Consent management integration for regulated data
- Data minimization techniques in feature engineering
- Automated data classification in streaming pipelines
Module 6. Security Controls for Model Deployment
Ensure deployed models meet organizational security baselines.
12 chapters in this module
- Hardening container images for model serving
- Network segmentation for model endpoints
- Rate limiting and abuse prevention for APIs
- Input validation for adversarial attack resistance
- Model monitoring for unexpected behavior
- Secure logging of model predictions
- Fail-safe mechanisms during model degradation
- Automated rollback triggers based on security events
- Zero-trust architecture patterns for AI services
- Certificate management for model endpoints
- Patch compliance for inference servers
- Secure update mechanisms for remote models
Module 7. Incident Response for AI Systems
Prepare for security incidents specific to machine learning environments.
12 chapters in this module
- Defining incidents in AI/ML contexts
- Model poisoning attack detection
- Data pipeline compromise scenarios
- Unauthorized model access attempts
- Adversarial input detection strategies
- Forensic readiness for model investigations
- Model version rollback as incident response
- Communication protocols during AI incidents
- Coordinating with security operations center
- Documenting incident response playbooks
- Post-mortem analysis for AI-specific failures
- Integrating AI incidents into broader IR plans
Module 8. Audit-Ready Documentation for AI
Generate the evidence needed for ISO 27001 audits without manual rework.
12 chapters in this module
- Automated generation of control evidence packs
- Linking model documentation to ISO clauses
- Version-controlled policy implementation records
- Audit trails for model parameter changes
- Evidence collection for third-party model use
- Standardized templates for control narratives
- Maintaining living documentation
- Automated compliance reporting dashboards
- Evidence for model validation and testing
- Documenting assumptions in model design
- Cross-referencing controls across systems
- Preparing for auditor interviews
Module 9. Vendor Management for AI Tools
Assess and manage third-party risks in the AI technology stack.
12 chapters in this module
- Evaluating cloud ML platforms for compliance
- Assessing open-source model risks
- Managing dependencies on external APIs
- Contractual requirements for model hosting
- Audit rights for third-party AI services
- Data processing agreements for model vendors
- Security certifications required for AI tools
- Due diligence for pre-trained model sources
- Monitoring vendor compliance status
- Exit strategies for proprietary AI platforms
- Managing supply chain risks in AI
- Vendor risk scoring for AI components
Module 10. Change Management for Model Updates
Implement controlled processes for modifying deployed models.
12 chapters in this module
- Change request workflows for model updates
- Impact assessment for model retraining
- Approval chains for production changes
- Version control for model configurations
- Rollback planning for failed deployments
- Communication plans for downstream users
- Testing requirements for updated models
- Documentation updates with each change
- Scheduling changes during maintenance windows
- Automated change validation
- Post-deployment monitoring triggers
- Audit trail requirements for changes
Module 11. Continuous Monitoring and Improvement
Maintain compliance as AI systems evolve over time.
12 chapters in this module
- Automated control validation schedules
- Model performance monitoring with security alerts
- Logging and monitoring for compliance
- Anomaly detection in model behavior
- Regular review of access permissions
- Updating documentation with system changes
- Tracking control effectiveness metrics
- Feedback loops from audit findings
- Adapting controls to new threats
- Security patch application tracking
- Continuous compliance dashboards
- Annual control review processes
Module 12. Leading AI Security from the Front Lines
Position yourself as the go-to expert in secure AI development.
12 chapters in this module
- Communicating security needs to non-technical stakeholders
- Influencing architecture choices with control insights
- Mentoring peers on compliance by design
- Contributing to AI security standards
- Building repeatable patterns across projects
- Documenting lessons learned systematically
- Earning trust through consistency
- Sharing control implementations across teams
- Advancing your role through security leadership
- Creating templates that outlive projects
- Establishing credibility with security teams
- Shaping policy through practical experience
How this maps to your situation
- New AI projects requiring ISO 27001 alignment
- Preparing for internal or external security audits
- Responding to client inquiries about AI security
- Building reusable security patterns across engagements
Before vs. after
Before
Spending weeks reconstructing security narratives for each audit cycle, reacting to compliance requests, and explaining technical decisions to non-technical reviewers.
After
Producing audit-ready documentation as a natural output of development, leading security discussions with confidence, and reducing compliance overhead by 85%.
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 per week for 6 weeks, designed to fit around project demands.
If nothing changes
Continuing to treat compliance as a separate phase creates bottlenecks in AI deployment, increases rework, and cedes influence over critical decisions to non-technical reviewers.
How this compares to the alternatives
Unlike generic compliance courses, this is built specifically for AI/ML engineers who need to ship secure models without slowing innovation. Unlike consulting reports, it provides executable patterns you can implement immediately.
Frequently asked
Is this relevant if I don't work directly on security?
Yes. This course teaches how to build security into your existing workflow so you can ship faster and with greater autonomy.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with client audits?
Yes. The course includes templates and patterns specifically designed to satisfy client security review requests.
$199 one-time. Approximately 90 minutes per week for 6 weeks, designed to fit around project demands..
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