A tailored course, built for your situation
Mastering OWASP for Generative AI and LLM Systems Engineers
Build a compounding library of secure, reusable AI control patterns
$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.
Who this is for
AI/ML Engineer working on generative AI and LLM systems within enterprise environments, focused on secure, repeatable deployment patterns and proactive risk control.
Who this is not for
Entry-level developers without deployment responsibilities, non-technical compliance staff, or engineers working exclusively on non-AI systems.
What you walk away with
- Design OWASP-aligned security controls tailored to generative AI attack surfaces
- Reuse proven safeguards across multiple LLM deployments without re-architecture
- Document modular security artefacts that accelerate audit and review cycles
- Integrate threat modelling into CI/CD pipelines for agentic AI systems
- Build a personal IP library of secure design patterns that compound over time
The 12 modules (with all 144 chapters)
Module 1. Foundations of OWASP in AI Systems
Establish core principles of OWASP relevance in generative AI, including threat categories, LLM-specific risks, and control mapping fundamentals.
12 chapters in this module
- AI-specific OWASP top ten
- LLM inference vulnerabilities
- Prompt injection anatomy
- Data leakage vectors
- Model poisoning pathways
- Authentication bypass in AI agents
- Session management flaws
- Misuse of AI APIs
- Overreliance risk patterns
- Output manipulation vectors
- Training data integrity
- Security-by-design mindset
Module 2. Threat Modelling for Agentic Workflows
Apply structured threat modelling to multi-step AI agent executions, identifying failure points before deployment.
12 chapters in this module
- Agent decision chains
- Autonomous action risks
- Memory persistence threats
- Tool misuse detection
- Cross-agent contamination
- State manipulation attacks
- Permission escalation paths
- Goal hijacking vectors
- Observability gaps
- Feedback loop exploits
- Input validation failures
- Action chaining weaknesses
Module 3. Secure Prompt Design Patterns
Engineer prompts that resist injection, maintain context integrity, and enforce role boundaries.
12 chapters in this module
- Context boundary enforcement
- Role isolation techniques
- Prompt shielding methods
- Template sanitization
- Output formatting guards
- Instruction leakage prevention
- Delimiter collision avoidance
- System prompt hardening
- User intent validation
- Adversarial prompt detection
- Response length controls
- Recursive call prevention
Module 4. Input Validation and Sanitization
Implement robust validation layers to neutralize malicious inputs before they reach the model.
12 chapters in this module
- Input schema design
- Content-type filtering
- Encoding normalization
- Length-based constraints
- Character set whitelisting
- Metadata inspection
- Embedded command detection
- Recursive payload scanning
- Rate limiting strategies
- Origin verification
- Trusted source lists
- Context-aware filtering
Module 5. Model Output Controls
Validate and sanitize model outputs to prevent harmful, inaccurate, or policy-violating content.
12 chapters in this module
- Harmful content filters
- PII redaction techniques
- Factuality scoring
- Source attribution
- Response consistency checks
- Tone enforcement
- Compliance alignment
- Policy violation detection
- External reference validation
- Confidence thresholding
- Output schema enforcement
- Fallback response design
Module 6. Authentication in AI Systems
Secure identity flows across AI agents, APIs, and data systems to prevent unauthorized access.
12 chapters in this module
- Agent identity design
- Token lifecycle management
- OAuth integration
- Role-based access
- Service-to-service auth
- Short-lived credentials
- API key rotation
- Multi-factor for AI actions
- Identity propagation
- Federation patterns
- Zero-trust for agents
- Session binding
Module 7. Data Flow Security
Protect data as it moves through AI pipelines, from ingestion to output.
12 chapters in this module
- Data provenance tracking
- In-transit encryption
- At-rest protection
- Cross-system boundaries
- Data minimization
- Retention policies
- Anonymization techniques
- Data masking
- Access logging
- Audit trail design
- Sensitivity labelling
- Data lineage mapping
Module 8. Integration with Azure and Databricks
Apply OWASP controls within Microsoft Azure and Databricks environments used in production.
12 chapters in this module
- Azure AI hardening
- Databricks workspace security
- Synapse integration
- ADF pipeline safeguards
- Managed identity use
- Network isolation
- Private endpoints
- Secrets management
- Role assignment
- Monitoring setup
- Compliance scanning
- Patch management
Module 9. Automated Security Testing
Implement continuous security testing in CI/CD pipelines for AI systems.
12 chapters in this module
- Test case generation
- Fuzzing for prompts
- Vulnerability scanning
- Regression testing
- Penetration testing
- Canary deployments
- Security gates
- Automated review
- Threat simulation
- Red team integration
- Bug bounty prep
- Incident replay
Module 10. Audit and Compliance Packaging
Generate clear, reusable documentation for internal and external audits.
12 chapters in this module
- Control mapping
- Evidence collection
- Audit trail generation
- Compliance narratives
- Framework alignment
- Policy references
- Test results packaging
- Executive summaries
- Technical appendices
- Gap analysis
- Remediation tracking
- Version control
Module 11. Reusable Security Artefacts
Build a personal library of templates, playbooks, and checklists that compound across projects.
12 chapters in this module
- Template standardization
- Playbook creation
- Checklist automation
- Knowledge base design
- Versioned libraries
- Cross-project reuse
- Peer review process
- Update mechanisms
- Integration with Jira
- Searchable repositories
- Onboarding acceleration
- Succession planning
Module 12. Scaling Secure AI Practices
Extend personal expertise into team-wide patterns and organizational impact.
12 chapters in this module
- Mentorship strategies
- Cross-team collaboration
- Security champion programs
- Training materials
- Incident response
- Lessons learned
- Pattern dissemination
- Feedback loops
- Tool standardization
- Policy evolution
- Leadership communication
- Strategic influence
How this maps to your situation
- Initial deployment planning
- Mid-cycle security integration
- Pre-audit preparation
- Post-mortem improvement
Before vs. after
Before
Security controls are rebuilt from scratch for each AI deployment, leading to inconsistent coverage and audit delays.
After
A growing library of reusable, OWASP-aligned safeguards accelerates every new project and strengthens compliance posture over 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 3 hours per module, designed to fit around active project cycles.
How this compares to the alternatives
Unlike generic security courses, this program is tailored specifically to the attack surfaces of generative AI and agentic systems, with concrete patterns applicable in Databricks, Synapse, and Azure environments.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I work with proprietary models?
Yes, controls are framework-based and apply regardless of model origin.
Can I use this with non-Microsoft data platforms?
Core principles transfer; examples focus on Azure but patterns are platform-agnostic.
$199 one-time. Approximately 3 hours per module, designed to fit around active project cycles..
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