A tailored course, built for your situation
Mastering OWASP for AI and Data Science Leaders
Build secure, production-ready AI systems with full control over security decisions.
$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 data scientists inherit security frameworks, they don’t shape them. That slows deployment and dilutes ownership.
The situation this course is for
Security is often bolted on after model development, creating rework, delayed launches, and misaligned controls. Practitioners lose leverage when they can’t approve core security artefacts.
Who this is for
Senior technical leaders driving AI innovation in regulated or product-facing environments who need to own security integration without deferring to separate AppSec teams.
Who this is not for
Entry-level developers, compliance auditors, or consultants without hands-on AI deployment responsibilities.
What you walk away with
- Own approval of OWASP ASVS checklist adoption for ML pipelines
- Set scope and sign off on DAST and SAST testing for AI services
- Define risk thresholds for automated vulnerability triage in CI/CD
- Finalise threat model artefacts for AI architecture reviews
- Lead secure-by-design patterns that become standard across data science teams
The 12 modules (with all 144 chapters)
Module 1. OWASP Landscape for AI Systems
Understand how OWASP principles apply to AI/ML workflows, from data ingestion to model serving, with emphasis on ownership over security integration points.
12 chapters in this module
- AI attack surfaces overview
- Mapping OWASP Top 10 to ML pipelines
- Security ownership in MLOps
- Threat actors targeting data science
- Risk ownership vs governance
- Secure development lifecycle
- AI-specific vulnerabilities
- Model integrity controls
- Data poisoning pathways
- Inference-time exploits
- API exposure risks
- Access control in model serving
Module 2. Threat Modelling Authority
Gain confidence to lead and finalise threat models for AI systems without escalation, using structured OWASP methodologies.
12 chapters in this module
- Threat modelling scope
- Defining system boundaries
- Data flow mapping for ML
- Identifying trust zones
- Threat categorisation
- Risk ranking methodology
- Ownership of output
- Reviewing attacker personas
- Documenting mitigations
- Stakeholder alignment
- Versioning threat models
- Audit readiness
Module 3. Vulnerability Assessment Ownership
Take complete control over DAST, SAST, and hybrid testing scopes for AI-enabled applications.
12 chapters in this module
- SAST integration in CI/CD
- DAST coverage thresholds
- Pen testing scoping
- Scope sign-off process
- Tool selection criteria
- False positive governance
- Automated triage rules
- Severity threshold setting
- Remediation timelines
- Reporting cadence
- Executive summary input
- Vendor assessment criteria
Module 4. Secure AI Development Lifecycle
Embed security decisions at each stage of AI development with ownership over process gates.
12 chapters in this module
- Security gates overview
- Data validation rules
- Model training checks
- Artifact signing
- Version control policies
- Environment segregation
- Access control design
- Secrets management
- Model explainability
- Bias detection integration
- Compliance documentation
- Release approval
Module 5. Security Framework Integration
Lead integration of OWASP ASVS into internal development standards with authority to finalise controls.
12 chapters in this module
- ASVS applicability levels
- Mapping to internal policies
- Control ownership
- Exemption process
- Cross-team alignment
- Developer onboarding
- Audit trail requirements
- Version control
- Control validation
- Metrics tracking
- Continuous improvement
- Leadership reporting
Module 6. Incident Response Authority
Define and own security incident response protocols specific to AI systems.
12 chapters in this module
- Incident classification
- Response team roles
- Communication plan
- Breach scenario playbook
- Forensic readiness
- Data preservation
- Model rollback procedure
- Stakeholder notification
- Regulatory reporting
- Post-mortem process
- Lessons learned
- Process update
Module 7. Vendor Security Oversight
Control security requirements and evaluations for third-party AI and data vendors.
12 chapters in this module
- Vendor selection criteria
- Security questionnaire
- Contractual terms
- Audit rights
- Pen testing rights
- Data handling rules
- Compliance expectations
- Onboarding process
- Ongoing monitoring
- Incident response SLA
- Exit protocols
- Relationship ownership
Module 8. Risk Threshold Decision Authority
Set acceptable risk levels for AI system vulnerabilities without escalation.
12 chapters in this module
- Risk tolerance definition
- Business impact analysis
- Likelihood assessment
- Risk register ownership
- Acceptance documentation
- Stakeholder consultation
- Escalation criteria
- Re-evaluation frequency
- Threshold communication
- Change management
- Audit trail
- Leadership alignment
Module 9. Security Documentation Leadership
Own creation and final approval of security artefacts used in audits and compliance reviews.
12 chapters in this module
- Security policy authoring
- Architecture diagrams
- Control mappings
- Compliance matrices
- Audit evidence packs
- SoA development
- Internal review process
- Version control
- Storage policy
- Access permissions
- Update cadence
- Decommissioning
Module 10. Security Training Enablement
Lead security upskilling for data science teams with approved content and delivery ownership.
12 chapters in this module
- Training needs analysis
- Curriculum design
- Content development
- Delivery format
- Hands-on labs
- Assessment design
- Attendance tracking
- Feedback mechanism
- Refresher cycles
- Metrics reporting
- Leadership updates
- Continuous content update
Module 11. Compliance Integration
Align OWASP practices with regulatory and internal compliance frameworks.
12 chapters in this module
- GDPR mapping
- DPDPA the current cycle alignment
- SOX controls
- ISO 27001 linkage
- NIST CSF integration
- Internal audit prep
- Regulator engagement
- Evidence package
- Remediation ownership
- Cross-functional coordination
- Reporting cadence
- Continuous monitoring
Module 12. Leadership Communication
Shape security messaging and reporting for executive audiences with final sign-off.
12 chapters in this module
- Executive summary writing
- Risk dashboard design
- KPI selection
- Incident update protocol
- Board-level reporting
- Leadership consultation
- Crisis messaging
- Stakeholder updates
- Success metrics
- Progress reporting
- Resource requests
- Strategic alignment
How this maps to your situation
- Threat model sign-off
- Penetration test scope approval
- Vulnerability triage rules
- Security incident response leadership
Before vs. after
Before
Security decisions require cross-team alignment and senior approval, slowing deployment and reducing ownership.
After
You lead security integration with direct sign-off on threat models, testing scope, and incident response, no escalations needed.
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 be completed in parallel with active projects.
If nothing changes
Without ownership of security decisions, AI initiatives remain dependent on AppSec teams, creating bottlenecks and deployment delays. Missed opportunities to shape secure-by-design culture.
How this compares to the alternatives
Generic security courses teach compliance checklists. This course teaches how to own security decisions, including scope, thresholds, and approvals, for AI systems.
Frequently asked
Is this course focused on web app security or AI systems?
It’s focused on applying OWASP principles specifically to AI and data science systems, including ML pipelines, model serving, and data infrastructure.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course include hands-on labs?
No, this is a text-based, decision-focused course. It includes templates and playbooks for immediate implementation.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with active projects..
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