A tailored course, built for your situation
Mastering OWASP for AI Systems Engineers
Build defensible, accurate, and polished AI security outputs from the first draft
$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 validation packages that stall and loop during auditor review
The situation this course is for
AI engineers spend excessive time reworking security documentation under tight deadlines, especially when outputs don’t meet auditor expectations on clarity, traceability, or alignment with control frameworks.
Who this is for
AI Engineer at a large tech enterprise, working at the intersection of machine learning, system architecture, and security compliance
Who this is not for
Entry-level developers, non-technical security analysts, or teams focused solely on traditional web app security without AI components
What you walk away with
- Produce security validation packages that pass expert review the first time
- Embed OWASP ASVS and AI-specific controls directly into development workflows
- Reduce rework cycles by minimizing gaps in artefact completeness and defensibility
- Gain confidence in producing polished, audit-ready outputs without cross-team chasing
- Strengthen credibility with security and compliance reviewers through consistent, structured deliverables
The 12 modules (with all 144 chapters)
Module 1. Introducing OWASP in AI System Design
Establish the foundation of OWASP principles as applied to AI/ML systems, focusing on threat modeling and secure-by-design intent.
12 chapters in this module
- Understanding OWASP’s role in modern AI architecture
- Mapping OWASP ASVS to AI system boundaries
- Identifying high-risk components in ML pipelines
- Differentiating traditional app security from AI-specific risks
- Integrating security early in the AI development lifecycle
- Leveraging OWASP resources for engineering teams
- Common misalignments between AI models and security controls
- Building cross-functional security awareness in AI teams
- Using threat trees to anticipate adversarial attacks
- Documenting assumptions for AI model behavior
- Aligning with NIST AI Risk Management Framework
- Creating a baseline security posture for AI services
Module 2. AI-Specific Threat Modeling
Apply structured methods to identify and prioritize threats unique to machine learning systems.
12 chapters in this module
- Defining assets and trust boundaries in AI systems
- Modeling data poisoning risks across training pipelines
- Detecting model inversion and membership leakage
- Assessing prompt injection vulnerabilities in generative models
- Evaluating model stealing and IP exposure risks
- Threat modeling for inference-time adversarial inputs
- Using STRIDE to classify AI-specific threats
- Prioritizing threats based on impact and exploitability
- Incorporating feedback loops from red teaming
- Mapping threats to OWASP Top 10 for LLMs
- Automating threat identification in CI/CD pipelines
- Documenting threat model review outcomes
Module 3. Secure AI Development Lifecycle
Integrate security checkpoints into AI development phases, from design to deployment.
12 chapters in this module
- Establishing gated reviews in AI project timelines
- Enforcing code signing and artifact provenance
- Validating model inputs against known malicious patterns
- Implementing secure model training environments
- Monitoring for unauthorized access during development
- Hardening APIs used by AI models
- Applying least privilege to model training jobs
- Auditing configuration drift in model infrastructure
- Ensuring reproducibility of model builds
- Controlling access to sensitive model weights
- Securing model versioning and rollback processes
- Integrating security linters into ML pipelines
Module 4. Data Integrity and Model Robustness
Ensure model reliability through defensive data handling and resilience testing.
12 chapters in this module
- Protecting training data from contamination
- Validating data provenance and lineage
- Detecting bias in input datasets
- Testing model robustness under perturbed inputs
- Mitigating drift in production data distributions
- Implementing input sanitization layers
- Using adversarial training to improve model resilience
- Monitoring for concept drift in real-time
- Setting up data quality gates pre-inference
- Logging and auditing model data flows
- Enforcing schema compliance for model inputs
- Building redundancy into data pipelines
Module 5. Authentication and Access Control for AI Systems
Apply zero-trust principles to protect AI endpoints and model interactions.
12 chapters in this module
- Designing role-based access to AI APIs
- Implementing OAuth2 for model invocation
- Managing service identities in distributed AI systems
- Securing model inference endpoints from abuse
- Preventing privilege escalation in AI components
- Enforcing mutual TLS between AI services
- Rotating credentials used in model pipelines
- Auditing access to sensitive model outputs
- Protecting against prompt flooding attacks
- Rate limiting AI endpoints effectively
- Validating caller identity in multi-tenant models
- Building audit trails for model access events
Module 6. Model Output Integrity and Interpretability
Ensure AI outputs are trustworthy, explainable, and protected from manipulation.
12 chapters in this module
- Validating model outputs against expected ranges
- Implementing output filtering for harmful content
- Using explainability tools to support audit narratives
- Logging decisions made by AI systems
- Detecting model hallucination in real time
- Providing traceability from input to output
- Enabling human-in-the-loop review triggers
- Building confidence scores into AI responses
- Securing model interpretation data
- Protecting model metadata from tampering
- Documenting model uncertainty for stakeholders
- Integrating model cards into deployment packages
Module 7. AI Supply Chain Security
Secure third-party dependencies, model libraries, and pre-trained components.
12 chapters in this module
- Assessing risk in open-source ML frameworks
- Tracking dependencies in AI model environments
- Scanning for known vulnerabilities in ML packages
- Validating provenance of pre-trained models
- Using SBOMs for AI model artifacts
- Enforcing signed binaries in model deployment
- Auditing third-party fine-tuning providers
- Hardening container images for model serving
- Monitoring for dependency drift in production
- Establishing approval workflows for new libraries
- Mitigating risks from model inversion attacks
- Creating inventory of external model components
Module 8. Monitoring and Incident Response for AI
Build proactive detection and response mechanisms tailored to AI system behaviors.
12 chapters in this module
- Setting up anomaly detection for model outputs
- Monitoring for unauthorized model access
- Detecting prompt injection attempts in logs
- Establishing baselines for normal model behavior
- Creating incident playbooks for AI-specific breaches
- Responding to data poisoning incidents
- Investigating model performance degradation
- Containing compromised AI endpoints
- Preserving forensic data from AI systems
- Coordinating response across ML and security teams
- Reporting AI incidents to compliance teams
- Conducting post-mortems on model failures
Module 9. Compliance and Audit Preparation
Structure documentation and evidence to meet security review requirements.
12 chapters in this module
- Mapping OWASP controls to internal audit checklists
- Preparing model risk assessment narratives
- Documenting control effectiveness for reviewers
- Generating SOC 2-relevant artefacts for AI systems
- Aligning with ISO 27001 control objectives
- Organizing evidence for periodic reviews
- Responding to auditor questions confidently
- Using templates to standardize audit packages
- Versioning compliance documentation
- Demonstrating due diligence in model governance
- Linking code changes to control updates
- Creating executive summaries from technical details
Module 10. Automating Security Validation
Implement tooling to enforce quality and consistency in security outputs.
12 chapters in this module
- Integrating linting tools into AI development
- Automating OWASP control checks in CI pipelines
- Validating model cards for completeness
- Enforcing documentation templates
- Running schema validation on model inputs
- Scanning for hardcoded secrets in ML code
- Using static analysis for model logic
- Detecting misconfigurations in deployment scripts
- Generating compliance reports automatically
- Flagging deviations from security baselines
- Enforcing access policy via IaC checks
- Auditing model changes in version control
Module 11. Cross-Functional Collaboration Strategies
Improve alignment between engineering, security, and compliance teams.
12 chapters in this module
- Translating technical details for non-engineers
- Facilitating joint threat modeling sessions
- Aligning security sprints with product goals
- Creating shared definitions of done for AI features
- Establishing feedback loops with auditors
- Running tabletop exercises with compliance teams
- Documenting decisions for cross-team visibility
- Building trust through consistent delivery
- Managing scope disagreements respectfully
- Incorporating reviewer feedback into workflows
- Balancing innovation speed with control rigor
- Creating living documentation for stakeholders
Module 12. Sustaining Security Excellence in AI
Embed continuous improvement and knowledge sharing into team culture.
12 chapters in this module
- Conducting regular security retrospectives
- Updating threat models with new intelligence
- Sharing lessons learned across projects
- Maintaining up-to-date model documentation
- Rotating security champions in engineering teams
- Tracking control evolution over time
- Benchmarking against industry standards
- Improving response time to new vulnerabilities
- Investing in ongoing security training
- Recognizing secure engineering practices
- Scaling best practices across teams
- Measuring maturity in AI security posture
How this maps to your situation
- AI system design and deployment
- Security validation for auditors
- Cross-functional collaboration in large tech orgs
- Continuous compliance in fast-moving environments
Before vs. after
Before
Spending cycles reworking security packages under auditor review pressure
After
Producing polished, defensible outputs that pass scrutiny the first time
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 6 hours total, designed to be completed in focused Sunday sessions.
If nothing changes
Continuing to deliver security artefacts that require rework creates inefficiency, delays deployment, and weakens credibility with compliance reviewers.
How this compares to the alternatives
Unlike generic cybersecurity courses, this program focuses specifically on AI engineering context, OWASP integration, and producing auditor-ready outputs without over-engineering.
Frequently asked
Is this course relevant if I don’t work directly on web applications?
Yes. The content is tailored to AI and machine learning systems, with a strong focus on OWASP's application to ML pipelines and model security.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass internal audits more smoothly?
Yes. The course teaches how to build validation packages that meet reviewer expectations the first time, reducing rework.
$199 one-time. Approximately 6 hours total, designed to be completed in focused Sunday sessions..
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