A tailored course, built for your situation
Mastering OWASP for Senior ML Engineering Leaders
Build secure, production-ready ML systems faster with proven safeguards.
$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 reviews slowing down ML deployments?
Who this is for
Senior engineering leaders in machine learning driving production system delivery with growing regulatory and security expectations.
Who this is not for
Individual contributors not involved in system design, or practitioners focused solely on non-production research.
What you walk away with
- Ship secure ML models 40% faster by integrating OWASP controls early
- Reduce security rework cycles by pre-validating architecture patterns
- Align model design with compliance expectations from day one
- Produce audit-ready documentation as a byproduct of development
- Lead cross-functional security reviews with confidence and precision
The 12 modules (with all 144 chapters)
Module 1. Understanding OWASP's Role in Modern ML Systems
Establish the foundation of OWASP principles as they apply specifically to machine learning pipelines, data integrity, and model deployment risks.
12 chapters in this module
- Mapping OWASP Top 10 to ML system vulnerabilities
- How insecure deserialization affects model loading
- Authentication failures in API-driven ML services
- Broken access controls in model endpoints
- Security misconfigurations in cloud ML platforms
- Sensitive data exposure in training datasets
- XML external entities in model metadata handling
- Broken authentication in inference APIs
- Insufficient logging in model monitoring systems
- Cross-site scripting risks in model visualization tools
- Insecure dependencies in Python-based ML stacks
- How OWASP ASVS aligns with ML security reviews
Module 2. Integrating OWASP into ML Threat Modeling
Learn how to conduct structured threat modeling sessions focused on ML-specific attack surfaces using OWASP frameworks.
12 chapters in this module
- Defining assets in ML pipelines: models, data, APIs
- Identifying threat agents targeting ML infrastructure
- Using STRIDE model with ML system diagrams
- Mapping DREAD scoring to ML risk scenarios
- Threat modeling for feature stores and data pipelines
- Assessing model poisoning risks in collaborative environments
- Evaluating adversarial attack likelihood on vision models
- Prioritizing threats based on exploitability and impact
- Documenting threat model outputs for audit purposes
- Integrating threat models into sprint planning
- Cross-referencing threats with OWASP ASVS controls
- Updating threat models after pipeline changes
Module 3. Securing the ML Development Lifecycle
Embed security checks at every phase of ML development, from data ingestion to model deployment.
12 chapters in this module
- Secure coding practices for Python ML scripts
- Managing secrets in Jupyter notebooks securely
- Hardening container images for ML training
- Using linters to catch OWASP-relevant code issues
- Code review checklists for ML security
- Version control best practices for model artifacts
- Secure handling of PII in training data
- Validating input schemas in preprocessing code
- Detecting hardcoded credentials in model files
- Enforcing least privilege in notebook environments
- Automated scanning of ML repositories
- Building security gates into CI/CD pipelines
Module 4. Data Security and Privacy in ML Workflows
Ensure compliance with privacy standards while maintaining data utility in ML systems.
12 chapters in this module
- Classifying sensitive data in ML pipelines
- Implementing data masking in training workflows
- Tokenization techniques for PII in datasets
- Differential privacy integration in model training
- Federated learning for data locality compliance
- Encryption of data at rest and in transit
- Access logging for data access events
- Data lineage tracking for audit readiness
- Consent validation in user data pipelines
- Anonymization validation for regulatory compliance
- Secure data sharing patterns across teams
- Auditing data flow for OWASP compliance
Module 5. Model Security and Integrity Verification
Protect ML models from tampering, theft, and adversarial manipulation.
12 chapters in this module
- Digital signing of model weights and artifacts
- Model checksum validation in deployment
- Detecting model inversion attacks
- Defending against membership inference
- Securing model repositories with access controls
- Integrity monitoring for inference servers
- Runtime model protection techniques
- Watermarking models for ownership verification
- Model versioning with security metadata
- Securing model update mechanisms
- Detecting adversarial inputs at inference time
- Using model cards for security disclosure
Module 6. API Security for ML Services
Secure model serving endpoints against common and emerging threats.
12 chapters in this module
- Authentication mechanisms for inference APIs
- Rate limiting strategies for public endpoints
- Input validation for JSON prediction requests
- Output encoding to prevent script injection
- Securing gRPC interfaces for internal models
- Implementing OAuth2 for model access
- Using API gateways with security policies
- Detecting abuse patterns in model usage logs
- Securing WebSocket connections for streaming predictions
- Managing API keys securely
- Implementing mutual TLS for internal services
- Auditing API access for compliance
Module 7. Infrastructure Hardening for ML Platforms
Apply OWASP principles to cloud and on-premise ML infrastructure.
12 chapters in this module
- Hardening Kubernetes clusters for ML workloads
- Securing GPU-accelerated computing environments
- Network segmentation for ML training jobs
- Firewall rules for distributed training
- Securing remote access to ML workstations
- Monitoring for unauthorized resource access
- Implementing zero-trust principles in ML networks
- Securing shared storage systems
- Protecting against lateral movement in clusters
- Using service meshes for secure microservices
- Configuring secure remote execution
- Auditing infrastructure changes automatically
Module 8. Monitoring and Incident Response for ML Systems
Detect and respond to security events in real time across ML pipelines.
12 chapters in this module
- Logging model prediction patterns for anomalies
- Detecting data drift as a security signal
- Monitoring for unexpected model behavior
- Integrating logs into SIEM systems
- Setting up alerts for OWASP-relevant events
- Incident response playbooks for ML breaches
- Forensic data collection for model investigations
- Automated rollback procedures for compromised models
- Coordinating response across data science and security teams
- Documenting security incidents for audit
- Post-mortem analysis with security focus
- Improving safeguards based on incident data
Module 9. Compliance and Audit Readiness for ML Systems
Streamline compliance efforts using OWASP as a foundation for audits.
12 chapters in this module
- Mapping OWASP controls to regulatory requirements
- Preparing evidence for SOC 2 audits
- Documenting security controls for ISO 27001
- Aligning with GDPR data protection standards
- Producing model risk management artifacts
- Responding to auditor questions on ML security
- Maintaining continuous compliance posture
- Automating control validation checks
- Versioning compliance documentation
- Integrating compliance checks into sprints
- Demonstrating due diligence in security practices
- Reporting OWASP compliance to leadership
Module 10. Vendor and Third-Party Risk in ML Ecosystems
Assess and manage security risks introduced by third-party tools and libraries.
12 chapters in this module
- Evaluating security practices of ML platform vendors
- Reviewing third-party model marketplace risks
- Analyzing open-source library vulnerabilities
- Using SCA tools for Python dependency scanning
- Assessing supply chain integrity in ML tools
- Validating container images from public registries
- Managing API dependencies securely
- Monitoring for license compliance risks
- Enforcing vendor security questionnaires
- Integrating third-party risk into CI/CD
- Documenting vendor risk assessments
- Requiring security attestations from suppliers
Module 11. Building a Security-First Culture in ML Teams
Foster proactive security behaviors across engineering and data science roles.
12 chapters in this module
- Integrating security into ML team onboarding
- Running secure coding workshops for data scientists
- Creating security champions within teams
- Sharing OWASP updates across departments
- Rewarding secure development practices
- Conducting security brown bags regularly
- Incorporating security into performance goals
- Promoting psychological safety in reporting
- Establishing feedback loops for security tools
- Aligning incentives with secure outcomes
- Measuring team security posture over time
- Recognizing contributions to security improvements
Module 12. Scaling OWASP Practices Across the Organization
Extend proven ML security practices enterprise-wide while maintaining agility.
12 chapters in this module
- Creating reusable security templates for teams
- Standardizing secure ML architecture patterns
- Developing internal certification programs
- Sharing lessons from security incidents
- Automating security policy enforcement
- Integrating security metrics into dashboards
- Establishing centers of excellence
- Driving consistency without stifling innovation
- Adapting OWASP guidance to new domains
- Measuring ROI of security investments
- Evangelizing best practices across engineering
- Future-proofing ML systems against emerging threats
How this maps to your situation
- Early-stage ML projects needing security integration
- Production model deployments under compliance pressure
- Cross-team initiatives requiring shared security standards
- Post-incident environments demanding improved safeguards
Before vs. after
Before
Security concerns slow down ML deployments, leading to rework and compliance gaps.
After
Teams ship secure models faster with built-in safeguards and audit-ready documentation.
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 3-4 hours per module, designed to be completed in parallel with ongoing work.
If nothing changes
Without structured security practices, ML teams face repeated rework, delayed launches, and increased risk of data breaches or model exploitation.
How this compares to the alternatives
Unlike generic security courses, this program is tailored to ML engineering leaders, focusing on OWASP principles applied directly to real-world model development, deployment, and compliance challenges.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if my team uses proprietary ML platforms?
Yes, the principles apply regardless of underlying platform and focus on architectural and process-level safeguards.
Will this help with regulatory audits?
Yes, modules include direct mappings to compliance frameworks and templates for audit evidence.
$199 one-time. Approximately 3-4 hours per module, designed to be completed in parallel with ongoing 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