A tailored course, built for your situation
Mastering OWASP for Machine Learning Engineers in High-Trust Environments
Build secure, auditable AI systems with confidence using proven OWASP methodologies tailored to ML infrastructure
$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.
Most AI security training is generic or bolted on after development. Engineers end up reworking models to meet security review standards, delaying deployment and weakening credibility.
The situation this course is for
Security reviews slow down AI innovation when they come late. Engineers without a clear security framework spend cycles justifying design choices instead of improving models. The result? Missed timelines, repeated audits, and lost influence.
Who this is for
Senior machine learning engineers working in regulated or high-exposure environments who want to lead AI security discussions, not just respond to them.
Who this is not for
Junior developers looking for introductory AI courses or non-technical leaders seeking high-level overviews.
What you walk away with
- Produce model documentation that proactively addresses OWASP Top 10 for LLMs and ML systems
- Anticipate and counter common security review objections before they're raised
- Integrate threat modeling into model development cycles without slowing delivery
- Speak confidently about security controls in cross-functional meetings with security and compliance teams
- Establish your reputation as the internal authority on secure ML system design
The 12 modules (with all 144 chapters)
Module 1. Foundations of Secure Machine Learning
Establish a shared language between ML engineering and security teams using OWASP principles mapped to model lifecycle stages.
12 chapters in this module
- Understanding the evolving threat model for AI systems
- Key differences between traditional app security and ML security
- OWASP's role in modern AI assurance frameworks
- How Meta's scale amplifies small security missteps
- Mapping OWASP Top 10 for LLMs to real-world use cases
- Security expectations from cross-functional partners
- Common misconceptions about model robustness
- Why 'secure by default' fails without proactive design
- Integrating security into the model ideation phase
- Threat actors targeting ML infrastructure today
- Case study: From research prototype to production risk
- Building credibility through early security alignment
Module 2. Threat Modeling for ML Pipelines
Systematically identify and prioritize risks in data flows, training jobs, and inference endpoints.
12 chapters in this module
- Decomposing ML systems into attack surfaces
- Data poisoning vectors and detection mechanisms
- Model theft through API exposure patterns
- Inference-time attacks and mitigation layers
- Dependency chains in containerized training
- Supply chain risks in pre-trained models
- Adversarial examples in high-stakes classification
- Logging gaps that hide exploitation attempts
- Privilege escalation in distributed training
- Third-party library vulnerabilities in PyTorch stacks
- Mapping OWASP STRIDE to ML system components
- Creating reusable threat models for standard architectures
Module 3. Data Integrity and Provenance Controls
Ensure training data authenticity and traceability from source to model weights.
12 chapters in this module
- Data lineage requirements for security review
- Cryptographic signing of dataset versions
- Detecting subtle distribution shifts from tampering
- Access patterns that suggest data exfiltration
- Version-controlled data with immutable references
- Audit-ready documentation for data pipelines
- Preventing label flipping in collaborative datasets
- Metadata tagging for compliance classification
- Automated checks for data drift and skew
- Integrity verification at model checkpoint save
- Balancing transparency with intellectual property
- Documenting data decisions for external reviewers
Module 4. Model Robustness and Input Validation
Design models that resist manipulation through malformed or malicious inputs.
12 chapters in this module
- Input validation strategies for unstructured data
- Token-level filtering for language model inputs
- Sanitizing embeddings before inference
- Adversarial training techniques for resilience
- Detecting prompt injection patterns in real time
- Rate limiting and quota enforcement at API level
- Model confidence monitoring under stress
- Fallback mechanisms when inputs appear suspicious
- Behavioral baselines for anomaly detection
- Logging inputs for forensic reconstruction
- Preventing jailbreak attempts in chat interfaces
- Performance under targeted perturbation testing
Module 5. Secure Model Deployment Patterns
Implement deployment architectures that minimize exposure while maintaining performance.
12 chapters in this module
- Zero-trust principles for model serving environments
- Network segmentation for inference endpoints
- Encrypted model weights at rest and in transit
- Just-in-time access for debugging sessions
- Immutable containers for production models
- Secrets management in distributed inference
- Role-based access control for model APIs
- Monitoring for abnormal query patterns
- Automated rollback triggers for security events
- Compliance logging for model access events
- Secure update mechanisms for model versioning
- Disaster recovery with security state preservation
Module 6. Model Monitoring and Runtime Protection
Detect and respond to runtime threats using telemetry and automated safeguards.
12 chapters in this module
- Real-time anomaly detection in prediction traffic
- Monitoring for model drift and concept shift
- Logging inference inputs with privacy safeguards
- Detecting brute-force attacks on model APIs
- Rate limiting strategies without degrading UX
- Automated alerts for suspicious activity patterns
- Model watermarking for attribution and tracking
- Behavioral profiling of legitimate users
- Incident response playbooks for ML systems
- Forensic data retention policies
- Runtime application self-protection patterns
- Integrating with enterprise SIEM tools
Module 7. Compliance and Audit Readiness
Generate documentation and evidence that passes internal and external review.
12 chapters in this module
- Mapping model decisions to OWASP controls
- Preparing for security review with complete artefacts
- Documenting trade-offs in model design
- Version-controlled model passports
- Standardized threat model templates
- Audit trails for model updates and retraining
- Articulating risk appetite in technical terms
- Responding to findings from red team exercises
- Preparing executive summaries without oversimplifying
- Evidence workflows for automated compliance
- Maintaining living documentation between audits
- Versioning security decisions alongside code
Module 8. Secure Collaboration Across Functions
Lead cross-functional security discussions with clarity and authority.
12 chapters in this module
- Translating ML risks for security teams
- Speaking confidently about control boundaries
- Anticipating questions from compliance officers
- Building trust through consistent terminology
- Facilitating joint threat modeling sessions
- Negotiating scope with platform security
- Documenting assumptions for external reviewers
- Presenting trade-offs in model security vs. performance
- Leading post-mortems with security stakeholders
- Incorporating feedback without losing velocity
- Establishing review checkpoints in sprints
- Creating shared ownership of security outcomes
Module 9. Vulnerability Management for ML Systems
Integrate vulnerability scanning and patching into ML development workflows.
12 chapters in this module
- Dependency scanning for ML libraries
- Vulnerability databases for AI frameworks
- Automated testing for known CVEs in model stacks
- Prioritizing fixes based on exposure level
- Patch management for long-running models
- Zero-day response protocols for ML components
- Integrating SAST into CI/CD for notebooks
- Container scanning for training images
- Firmware-level risks in AI accelerators
- Coordinating disclosures with open-source maintainers
- Responsible disclosure for model-specific flaws
- Maintaining vulnerability history for audits
Module 10. Incident Response for AI Systems
Respond effectively to security incidents involving ML models.
12 chapters in this module
- Identifying signs of model compromise
- Containment strategies for poisoned models
- Forensic analysis of training data integrity
- Rolling back to known-good model versions
- Communicating incidents to internal teams
- Legal considerations in AI incident disclosure
- Post-incident review with security leadership
- Updating threat models based on real events
- Hardening systems after breach detection
- Reviewing access logs for anomalous patterns
- Preserving evidence for root cause analysis
- Improving detection thresholds after incidents
Module 11. Security Culture and Leadership
Shape team practices to prioritize security without slowing innovation.
12 chapters in this module
- Modeling secure behavior as a senior engineer
- Mentoring peers on security best practices
- Introducing lightweight security checks in code reviews
- Celebrating security wins in team meetings
- Reducing friction in security processes
- Building psychological safety for reporting issues
- Advocating for security tooling investments
- Balancing speed and rigor in fast-moving teams
- Leading by example in documentation quality
- Creating rituals for security knowledge sharing
- Recognizing contributors to security improvements
- Sustaining vigilance during product crunch times
Module 12. Future-Proofing ML Security Practices
Stay ahead of emerging threats and organizational expectations.
12 chapters in this module
- Tracking new OWASP guidance for AI systems
- Evaluating research on novel attack vectors
- Participating in red team exercises proactively
- Contributing to internal security standards
- Sharing lessons across engineering orgs
- Advancing the state of secure ML at scale
- Mentoring the next generation of secure AI builders
- Shaping policy around automated decision-making
- Influencing procurement with security requirements
- Building reputation beyond immediate team
- Establishing go-to status for complex AI security
- Leaving a legacy of secure, trustworthy systems
How this maps to your situation
- Initial development phase with new AI product
- Preparing for first internal security review
- Scaling an existing model to new regions
- Responding to new compliance requirements
Before vs. after
Before
Spending extra cycles reworking models for security review, reacting to feedback, and justifying design choices.
After
Producing secure-by-design models with documentation that passes review the first time and positions you as the trusted advisor.
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: 90 minutes per module, designed to be completed over 12 weeks with consistent progress.
If nothing changes
Continuing without a structured security framework means repeated rework, delayed deployments, and missed opportunities to lead in AI security discussions.
How this compares to the alternatives
Unlike generic cybersecurity courses, this program is tailored specifically to machine learning engineers working in high-exposure environments, with concrete examples and templates aligned to OWASP standards and Meta-scale challenges.
Frequently asked
Is this course focused on web application security or ML-specific threats?
It's specifically about ML system security, using OWASP principles adapted to model training, deployment, and monitoring.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass internal security reviews?
Yes, each module builds toward producing documentation and designs that meet and exceed standard review expectations.
$199 one-time. 90 minutes per module, designed to be completed over 12 weeks with consistent progress..
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