A tailored course, built for your situation
Mastering OWASP for Associate Principal Scientists in AI Modeling
Build a repeatable, self-reinforcing asset in AI security that compounds across every model deployment
$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 scientists rebuild security from scratch each time, wasting cycles and exposing teams to preventable risk.
The situation this course is for
Without a structured approach, security efforts stall across projects. Manual reviews pile up, audit trails thin out, and hard-won insights don’t transfer. Each deployment feels like starting over.
Who this is for
Senior AI scientists leading model design and deployment in regulated, innovation-driven environments
Who this is not for
Junior data analysts, IT auditors, or developers outside AI/ML model delivery
What you walk away with
- A personal library of OWASP-aligned threat models reusable across AI projects
- Standardized validation scripts that cut review time by 50% on subsequent deployments
- Compliance-ready documentation templates used across DSM-Firmenich AI releases
- First-hand artefacts to share when peer teams question model security assumptions
- A documented playbook that survives team reshuffles and leadership changes
The 12 modules (with all 144 chapters)
Module 1. Foundations of OWASP in AI Systems
Establish core principles of the OWASP AI Security and Privacy Guide and map them to model lifecycle stages.
12 chapters in this module
- What OWASP AI covers
- Where AI differs from web app security
- Model vs pipeline boundaries
- Threat actor profiles in AI
- Common misconfigurations
- Regulatory touchpoints
- Risk tolerance bands
- Asset classification schema
- Data lineage basics
- Model card prerequisites
- Stakeholder maps
- First documentation artefact
Module 2. Threat Modeling for Model Design
Apply STRIDE and DREAD to AI workflows and generate reusable threat trees.
12 chapters in this module
- Designing with STRIDE
- DREAD scoring live
- Input manipulation examples
- Model inversion patterns
- Prompt injection paths
- Feature leakage risks
- Output poisoning vectors
- Model drift triggers
- Boundary definition
- Trust zone mapping
- Ownership assignment
- First threat model output
Module 3. Secure Model Development Workflow
Integrate security checks into Jupyter notebooks and CI/CD pipelines with traceable artefacts.
12 chapters in this module
- Pre-commit hooks setup
- Code scanning integration
- Dependency vetting
- Model signing steps
- Environment isolation
- Credential handling
- Role-based access design
- Audit trail generation
- Pipeline validation
- Log integrity checks
- Version control tagging
- Build documentation
Module 4. Data Integrity and Privacy Controls
Implement data validation, anonymization, and leakage protection aligned with OWASP
12 chapters in this module
- Data provenance tagging
- PII detection scripts
- Anonymization thresholds
- Differential privacy basics
- Membership inference tests
- Label leakage checks
- Bias audit integration
- Consent tracking
- Data drift monitoring
- Retention policy alignment
- Cross-team data handoff
- Final checklist
Module 5. Model Hardening Techniques
Defend against adversarial attacks and ensure robust predictions
12 chapters in this module
- Adversarial example generation
- Gradient masking pitfalls
- Input sanitization filters
- Perturbation detection
- Model distillation use
- Defensive distillation
- Ensemble robustness
- Certified defenses
- Runtime monitoring
- Response thresholding
- Fallback mechanisms
- Validation artifact
Module 6. Security Testing Automation
Build automated test suites for OWASP-aligned scenarios
12 chapters in this module
- Test case design
- Synthetic attack data
- Fuzzing pipelines
- Model robustness score
- Penetration testing scope
- Red team coordination
- API exposure checks
- Prompt jailbreaking
- Output conformance
- Logging test results
- Automated reporting
- Test suite packaging
Module 7. Compliance Documentation for AI
Generate audit-ready artefacts using OWASP and internal standards
12 chapters in this module
- Model inventory setup
- Control mapping
- Evidence collection
- SOC 2 alignment
- GDPR integration
- Internal policy links
- Version history format
- Audit trail structure
- Sign-off workflow
- Stakeholder review cycle
- Update triggers
- Final document output
Module 8. Incident Response for AI Models
Define detection, containment, and recovery steps specific to AI systems
12 chapters in this module
- Breach detection signals
- Model rollback protocol
- Data quarantine
- Stakeholder notification
- Forensic data capture
- Model version snapshot
- Root cause analysis
- Regulatory reporting
- Post-mortem process
- Lessons captured
- Playbook update
- Team simulation
Module 9. Cross-Team Collaboration Framework
Align security practices across data science, engineering, and compliance teams
12 chapters in this module
- Shared vocabulary
- Handoff documentation
- Review meeting format
- Stakeholder expectations
- Escalation paths
- Feedback integration
- Compliance liaison
- Security champion role
- Training materials
- Tooling alignment
- Roadmap sync
- Working group setup
Module 10. Sustaining Security Over Time
Implement monitoring and refresh cycles to keep models secure
12 chapters in this module
- Drift detection setup
- Retraining triggers
- Control expiration
- Patch management
- Version compatibility
- Model decommissioning
- Legacy model tracking
- Knowledge transfer
- Documentation updates
- Audit prep rhythm
- Team onboarding
- Lifecycle closure
Module 11. Personal IP Library Development
Curate reusable assets, templates, and checklists across projects
12 chapters in this module
- Template design
- Checklist optimization
- Example curation
- Folder structure
- Searchability
- Version control
- Access control
- Peer review cycle
- Contribution process
- Integration into onboarding
- Cross-project reuse
- Library maintenance
Module 12. Compounding Security Across Deliveries
Leverage past work to accelerate future AI deployments
12 chapters in this module
- Knowledge transfer design
- Pattern reuse
- Baseline acceleration
- Stakeholder trust building
- Credibility scaling
- Effort reduction
- Quality increase
- Risk reduction
- Cross-domain application
- Mentorship opportunities
- Leadership visibility
- Final library delivery
How this maps to your situation
- First 100 days in role
- Stabilizing AI modeling function
- Building cross-functional credibility
- Deploying first firm-wide model
Before vs. after
Before
Starting security from zero on each AI model, with fragmented documentation and no reusable assets
After
Leveraging a growing personal IP library that accelerates every new AI delivery and strengthens cross-functional trust
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 AI delivery cycles
If nothing changes
Without a compounding security practice, each model takes longer, exposes more risk, and fails to build lasting credibility across teams
How this compares to the alternatives
Unlike generic cybersecurity courses, this focuses on AI-specific OWASP practices with real deployment artefacts. Unlike internal playbooks, it builds a personal, portable IP library that compounds across roles and projects.
Frequently asked
Is this aligned with the OWASP AI Security and Privacy Guide?
Yes, every module maps directly to OWASP AI Security and Privacy Guide controls and use cases, using exact terminology and structure.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work if my team uses different tools?
Yes, the principles and artefacts are tool-agnostic and have been applied across AWS, GCP, and on-prem environments.
$199 one-time. Approximately 3 hours per module, designed to fit around active AI delivery 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