A tailored course, built for your situation
Mastering OWASP for ML Infrastructure Engineers
Build secure, production-ready machine learning systems with confidence
$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.
Tired of last-minute security rework on ML pipeline designs?
The situation this course is for
Even strong ML infrastructure designs get delayed by late-stage security findings. Without a structured way to anticipate threats, engineers face rework, extended review cycles, and diluted velocity, all avoidable with upfront quality.
Who this is for
Senior ML Infrastructure Engineer shipping production-grade AI systems in high-velocity environments
Who this is not for
Engineers focused only on model accuracy or data pipelines without security integration
What you walk away with
- Produce OWASP-aligned threat models for ML systems on first submission
- Document security controls that pass peer and compliance review without revision
- Reduce rework cycles by embedding secure design patterns early
- Deliver polished, auditable architecture narratives for ML pipelines
- Build repeatable templates for secure model deployment workflows
The 12 modules (with all 144 chapters)
Module 1. Introduction to OWASP in ML Systems
Understand how OWASP principles apply to machine learning infrastructure, including data flow threats, model integrity, and pipeline vulnerabilities unique to AI systems.
12 chapters in this module
- What is OWASP
- AI Security Threat Landscape
- ML Pipeline Attack Surfaces
- Secure Design Mindset
- OWASP Top 10 for AI
- Threat Modeling Basics
- Security as Code Overview
- Risk Prioritization Framework
- Compliance Alignment Points
- Integrating Security Early
- Common Anti-Patterns
- Security Review Expectations
Module 2. Threat Modeling for ML Pipelines
Learn to systematically identify and document risks in data ingestion, preprocessing, model training, and serving layers using structured OWASP-backed techniques.
12 chapters in this module
- Data Flow Diagramming
- Identifying Trust Boundaries
- Abuse Case Development
- Threat Categorization
- STRIDE Mapping
- Model Inversion Risks
- Data Poisoning Vectors
- Feature Store Exposures
- Serving Layer Attacks
- Logging Gaps
- Access Control Failures
- Threat Register Creation
Module 3. Secure Architecture Patterns
Adopt proven architectural controls that harden ML systems against OWASP-identified threats, from data validation to inference protection.
12 chapters in this module
- Zero Trust for ML
- Input Sanitization Methods
- Model Signing Techniques
- Environment Isolation
- Secure Feature Stores
- Encrypted Model Serving
- Rate Limiting APIs
- Audit Trail Design
- Role-Based Access
- Secrets Management
- Network Hardening
- Fail-Safe Defaults
Module 4. Security Documentation Standards
Create clear, defensible documentation that aligns with OWASP expectations and passes compliance and peer review on first submission.
12 chapters in this module
- Architecture Decision Records
- Security Narrative Structure
- Control Mapping Tables
- Evidence Gathering
- Version-Controlled Docs
- Review-Ready Formats
- Cross-Team References
- Assumptions Tracking
- Exception Justification
- Compliance Alignment
- Internal Audit Prep
- Living Document Practices
Module 5. Secure CI/CD for ML
Integrate security checks into ML pipeline automation to catch issues before deployment, reducing rework and increasing trust in releases.
12 chapters in this module
- Pipeline Security Gates
- Automated Threat Scanning
- Model Provenance Checks
- Data Drift Monitoring
- Policy-as-Code Tools
- SBOM Generation
- Integration Testing
- Vulnerability Scanners
- Approval Workflows
- Rollback Safeguards
- Logging Enrichment
- Post-Deploy Validation
Module 6. Model Integrity & Robustness
Ensure ML models resist manipulation and maintain performance under adversarial conditions using OWASP-backed validation techniques.
12 chapters in this module
- Model Stealing Risks
- Adversarial Input Testing
- Model Fingerprinting
- Integrity Verification
- Bias Auditing
- Confidence Calibration
- Output Monitoring
- Anomaly Detection
- Model Sandboxing
- Integrity Signaling
- Re-Training Triggers
- Model Deletion Protocols
Module 7. Data Protection in ML Systems
Apply OWASP data security principles to protect sensitive data throughout the ML lifecycle, from ingestion to inference.
12 chapters in this module
- Data Classification
- PII Detection Methods
- Differential Privacy
- Encryption in Transit
- Encryption at Rest
- Tokenization Patterns
- Access Logging
- Data Retention Rules
- Anonymization Quality
- Synthetic Data Use
- Consent Tracking
- Data Subject Rights
Module 8. Third-Party Risk in AI
Evaluate and manage risks from open-source libraries, pre-trained models, and vendor tools used in ML infrastructure.
12 chapters in this module
- Open Source License Risks
- Pre-Trained Model Audits
- Vendor Security Questionnaires
- Dependency Scanning
- Model Provenance
- Artifact Signing
- Patch Management
- Fallback Strategies
- Vendor SLAs
- Exit Clauses
- Audit Rights
- Compliance Mapping
Module 9. Incident Response for ML Systems
Prepare response playbooks for security incidents involving ML pipelines, models, or data breaches using OWASP guidelines.
12 chapters in this module
- Incident Detection
- Model Misuse Identification
- Data Leak Response
- Model Rollback Plans
- Forensic Readiness
- Communication Protocols
- Legal Reporting
- Stakeholder Updates
- Post-Incident Review
- Blameless Culture
- Improvement Tracking
- Runbook Maintenance
Module 10. Audit Readiness & Compliance
Structure ML infrastructure work to meet audit requirements and compliance standards informed by OWASP principles.
12 chapters in this module
- Evidence Collection
- Control Testing
- Internal Audit Prep
- External Audit Support
- SOC 2 Alignment
- ISO 27001 Mapping
- GDPR Considerations
- Regulator Engagement
- Findings Response
- Remediation Tracking
- Continuous Monitoring
- Compliance Dashboards
Module 11. Secure Collaboration Across Teams
Bridge security, MLOps, and data science teams with shared language and processes rooted in OWASP best practices.
12 chapters in this module
- Cross-Functional Workflows
- Security Champion Role
- Peer Review Processes
- Shared Playbooks
- Toolchain Integration
- Joint Design Sessions
- Feedback Loops
- Escalation Paths
- Knowledge Sharing
- Training Materials
- Performance Metrics
- Success Stories
Module 12. Sustaining Security Quality
Build systems that maintain security quality over time through automation, culture, and continuous improvement.
12 chapters in this module
- Automated Security Testing
- Quality Gate Enforcement
- Security Metrics
- Retrospective Practices
- Lessons Learned
- Improvement Backlog
- Security Culture
- Leadership Communication
- Resource Advocacy
- Tool Investment
- Training Programs
- Maturity Assessments
How this maps to your situation
- Early design phase
- Peer review and feedback
- Compliance audit cycle
- Post-incident reflection
Before vs. after
Before
ML infrastructure designs face late-stage security reviews, requiring rework and delaying deployment.
After
Security is embedded from the start, outputs are accurate, defensible, and approved the first time.
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 3 hours per module, designed to fit around full-time engineering work.
If nothing changes
Continuing without structured security integration means recurring rework, extended timelines, and diminished credibility on complex ML projects.
How this compares to the alternatives
Unlike generic security courses, this is tailored specifically to ML infrastructure engineers working on production AI systems. It focuses on practical, immediate application, not theory.
Frequently asked
Is this course focused on application security or ML systems?
It’s focused entirely on applying OWASP principles to machine learning infrastructure, including pipelines, models, and deployment systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass internal audits?
Yes, each module builds toward producing auditable, review-ready outputs that demonstrate compliance with security best practices.
$199 one-time. Approximately 3 hours per module, designed to fit around full-time engineering work..
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