A tailored course, built for your situation
Mastering OWASP for Senior AI and Data Technology Executives
Build defensible AI security decision-making with source-backed reasoning and framework fluency
$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.
Peers question your AI security stance and you lack the referenced examples to defend it
The situation this course is for
Even experienced leaders hesitate when challenged on AI security choices, not because they’re wrong, but because they can’t instantly cite the standards, precedents, or control logic that justify their approach. In fast-moving AI environments, authority erodes without verifiable depth.
Who this is for
Senior technology executive operating at the intersection of AI, data infrastructure, and security governance, responsible for justifying high-impact technical decisions to cross-functional stakeholders.
Who this is not for
Junior engineers, compliance auditors, or IT generalists looking for entry-level OWASP training. This is not a certification prep course. It’s for decision-makers who must defend architecture, not implement controls.
What you walk away with
- Reference OWASP guidelines with precision when debating AI security tradeoffs
- Walk through the reasoning behind security decisions using documented examples from peer implementations
- Anticipate and neutralize common challenges to AI system design using framework-aligned logic
- Maintain consistent control expectations across distributed AI and data teams
- Produce clear, referenced narratives for vendor reviews, internal escalations, and cross-team alignment
The 12 modules (with all 144 chapters)
Module 1. Understanding OWASP AI Security Top Risks
Establish foundational fluency in the OWASP AI Security Top 10, with real-world breach examples and documented failure patterns across AI systems.
12 chapters in this module
- OWASP project mission and structure
- AI security vs traditional application security
- Threat modeling for generative AI
- Insecure output handling risks
- Model denial of service patterns
- Prompt injection case studies
- Data poisoning vectors
- Authentication gaps in AI APIs
- Model evasion techniques
- Supply chain risks in pretrained models
- Adversarial input testing
- Real-world impact of OWASP Top 10 items
Module 2. Mapping OWASP Controls to AI System Architecture
Align OWASP control objectives to distributed AI and data systems, ensuring design decisions are traceable to security outcomes.
12 chapters in this module
- Control mapping methodology
- Secure model deployment patterns
- Input validation layers
- Role-based access for AI workloads
- Model integrity monitoring
- Secure API gateways for AI services
- Embedding security into MLOps
- Data pipeline hardening
- Model signing and attestation
- Trusted execution environments
- Secure update mechanisms
- Architecture review checklist
Module 3. Sourcing Precedents from Industry Implementations
Collect and apply real-world examples where OWASP principles guided AI security decisions in regulated environments.
12 chapters in this module
- Finding public implementation reports
- Analysing AI security disclosures
- Reverse-engineering vendor controls
- Case: Financial services AI gateway
- Case: Healthcare diagnostic model
- Case: Cloud provider AI firewall
- Extracting reusable patterns
- Vendor assessment benchmarks
- Public bug bounty reports
- Open-source model audits
- Regulator-accepted controls
- Cross-industry precedent tracking
Module 4. Building Reusable Security Narratives
Develop defensible, repeatable explanations for high-stakes AI design choices using OWASP-aligned logic.
12 chapters in this module
- Narrative structure for technical leaders
- Framing tradeoffs objectively
- Linking decisions to OWASP controls
- Documenting assumptions formally
- Creating decision memos
- Versioning security rationale
- Presenting to non-technical stakeholders
- Deprecating outdated justifications
- Storing narrative artifacts
- Cross-team reference libraries
- Handling dissent respectfully
- Updating narratives post-review
Module 5. Aligning Teams Around OWASP Benchmarks
Ensure consistent interpretation of OWASP guidance across data, AI, and infrastructure teams.
12 chapters in this module
- Team alignment workshop design
- Translating controls to team goals
- Security KPIs for engineering
- Shared terminology framework
- Cross-functional control ownership
- Regular benchmark assessments
- Internal audit readiness
- Training materials for onboarding
- Feedback loops from incidents
- Control drift detection
- Performance vs security balance
- Leadership escalation paths
Module 6. Vendor Evaluation Using OWASP Standards
Apply OWASP-based criteria to third-party AI tools and platforms during procurement and integration.
12 chapters in this module
- Vendor assessment rubric design
- OWASP compliance questionnaires
- Third-party model risk review
- API security validation
- Model provenance verification
- Transparency documentation review
- Penetration test requirements
- Contractual control obligations
- Post-deployment monitoring clauses
- Exit strategy security triggers
- Multi-vendor consistency
- Benchmarking toolchain security
Module 7. Documenting Control Implementation Decisions
Create auditable, referenced records of how OWASP controls were applied, or adapted, within specific AI systems.
12 chapters in this module
- Control implementation logs
- Justification for deviations
- Evidence collection framework
- Version-controlled documentation
- Linking code to controls
- Automated compliance tagging
- Audit trail design
- Change management integration
- Review cycle documentation
- Stakeholder sign-off tracking
- Retention policies
- Cross-system consistency checks
Module 8. Responding to Peer Challenges with Precision
Use OWASP fluency to confidently counter technical objections with sourced reasoning and documented precedent.
12 chapters in this module
- Common pushback patterns
- Deconstructing technical objections
- Citing framework sections directly
- Using public breach data
- Presenting comparative analysis
- Avoiding defensiveness
- Acknowledging valid concerns
- Offering iterative improvements
- Escalating appropriately
- Maintaining technical credibility
- Building reputation as reference
- Tracking resolved challenges
Module 9. Integrating OWASP into MLOps Workflows
Embed security checks and documentation requirements into CI/CD pipelines for AI models.
12 chapters in this module
- Security gates in deployment
- Automated vulnerability scanning
- Model signature validation
- Data drift and security linkage
- Model explainability integration
- Access control automation
- Incident response triggers
- Rollback procedures
- Monitoring for adversarial input
- Compliance-as-code templates
- Audit logging enrichment
- Pipeline ownership models
Module 10. Preparing for External Reviews and Audits
Organize documentation, narratives, and evidence to demonstrate OWASP alignment under scrutiny.
12 chapters in this module
- Audit preparation checklist
- Regulator communication strategy
- Document organization framework
- Control mapping matrix
- Evidence tagging system
- Mock review facilitation
- Gap remediation planning
- Third-party validation options
- Scope definition for auditors
- Timeline management
- Post-audit follow-up
- Improvement tracking
Module 11. Maintaining Fluency as OWASP Evolves
Stay current with OWASP AI Security updates and contribute to the practitioner community.
12 chapters in this module
- OWASP project contribution model
- Tracking working group updates
- Participating in public discussions
- Implementing draft controls
- Version comparison techniques
- Community benchmarking
- Internal change notifications
- Training refresh cycles
- Lessons learned sharing
- Cross-organization collaboration
- Feedback to OWASP teams
- Maintaining organization-level fluency
Module 12. Leading Defensible AI Security Strategy
Synthesize OWASP fluency, team alignment, and precedent into a sustainable security leadership model.
12 chapters in this module
- Setting security vision
- Balancing innovation and control
- Resource allocation strategy
- Measuring security maturity
- Executive communication
- Crisis response planning
- Long-term roadmap development
- Talent development
- Cross-functional influence
- Lessons from past incidents
- Scaling defensible practices
- Building organizational memory
How this maps to your situation
- When designing a new AI service and need to justify security architecture
- During vendor selection where security claims need verification
- Facing peer challenge on model deployment decisions
- Preparing for external audit or regulatory review
Before vs. after
Before
Reactively explaining AI security decisions, often without ready access to standards or precedents
After
Proactively referencing OWASP controls and real-world examples to lead confident, defensible discussions
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 for integration with real-world decision cycles.
If nothing changes
Continuing to rely on intuition or institutional memory leaves AI security decisions vulnerable to challenge, slows cross-team alignment, and increases exposure to regulatory or operational risk when justification is demanded.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses exclusively on OWASP fluency for AI/ML systems, with concrete examples from distributed data environments. No other course provides this level of targeted, defensible reasoning for senior technology leaders.
Frequently asked
Is this course about OWASP certification?
No. This is not a certification prep course. It’s designed for senior leaders who must defend architectural decisions using OWASP principles, not pass an exam.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this apply to my work with AI databases?
Yes. The course addresses AI/ML systems integrated with data infrastructure, including security patterns relevant to AI-enhanced database environments.
$199 one-time. Approximately 3 hours per module, designed for integration with real-world decision 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