A tailored course, built for your situation
Mastering OWASP for Data Science Leaders in Product-Centric Engineering Orgs
Turn security debt into strategic leverage without slowing innovation velocity.
$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.
High-performing data science teams ship models fast, but too often inherit security gaps that only surface post-deployment, risking recalls, compliance flags, and erosion of cross-team trust.
The situation this course is for
Data scientists are increasingly responsible for secure by design practices, yet lack structured guidance on where security controls belong in the model lifecycle. Traditional security frameworks feel alien, while OWASP’s Application Security Verification Standard (ASVS) and Threat Modelling guidelines remain underutilized in ML contexts. The result: inconsistent implementation, rework, and delayed release cycles when security finally reviews.
Who this is for
Senior data scientists and ML leads in engineering-driven orgs who influence architecture and deployment decisions but aren’t security specialists , yet are now expected to own security outcomes.
Who this is not for
Dedicated AppSec engineers, compliance auditors, or junior analysts using pre-packaged ML tools without deployment authority.
What you walk away with
- Produce threat models that integrate directly into PR reviews and architecture approvals
- Implement automated security gates in MLOps pipelines using OWASP ASVS benchmarks
- Document model security posture for internal reviewers and external partners
- Anticipate and resolve common OWASP Top 10 for Machine Learning flaws pre-incident
- Earn inclusion in early product design sprints due to trusted risk articulation
The 12 modules (with all 144 chapters)
Module 1. Mapping OWASP Principles to the ML Lifecycle
Align OWASP’s core security concepts with data science workflows , from data ingestion to model serving , to identify critical control points.
12 chapters in this module
- Understanding OWASP’s role in modern software supply chains
- How data leakage violates security and valuation integrity
- Differences between application and ML threat surfaces
- Integrating security checkpoints into model development sprints
- Using SHAP and feature importance for anomaly detection
- Detecting overfitting as a potential security risk
- When high accuracy masks hidden data dependencies
- Validating input sanitization in feature pipelines
- Securing model APIs against injection attacks
- Reviewing training data provenance for contamination risks
- Auditing model drift as a security control
- Documenting assumptions for peer security review
Module 2. Threat Modeling for Predictive Systems
Apply STRIDE and DREAD frameworks to data products, focusing on spoofing, tampering, and information disclosure risks in model outputs.
12 chapters in this module
- Defining assets in machine learning pipelines
- Identifying trust boundaries in feature engineering
- Threat tree generation for model deployment scenarios
- Assigning risk severity using data-specific heuristics
- Mitigation strategies for high-risk model endpoints
- Integrating threat models into sprint planning
- Visualizing data flow with security annotations
- Automating threat detection in CI pipelines
- Prioritizing fixes based on business impact
- Cross-walking threats to OWASP ASVS controls
- Engaging security teams with structured findings
- Updating models after threat re-assessment
Module 3. OWASP ASVS for Machine Learning APIs
Adapt OWASP’s Application Security Verification Standard to validate model endpoints, focusing on authentication, input validation, and error handling.
12 chapters in this module
- Mapping OWASP ASVS v4 to ML API endpoints
- Enforcing API key rotation and scope controls
- Validating input payloads for adversarial patterns
- Rate limiting and abuse prevention for prediction endpoints
- Securing model outputs against data leakage
- Hardening containerized model deployments
- Configuring TLS and mutual authentication
- Logging and monitoring for suspicious queries
- Implementing circuit breakers for denial-of-service
- Auditing API access patterns over time
- Documenting security controls for internal review
- Generating compliance-ready artefacts from test results
Module 4. Securing Data Pipelines and Feature Stores
Apply security controls to data ingestion, transformation, and storage layers to prevent contamination and unauthorized access.
12 chapters in this module
- Classifying data sensitivity in feature engineering
- Securing training data access with role-based controls
- Validating upstream data sources for integrity
- Detecting data poisoning through statistical monitoring
- Masking PII in development and test environments
- Encrypting data at rest and in transit
- Managing secrets in pipeline orchestration tools
- Auditing access to feature store snapshots
- Versioning datasets with security metadata
- Automating pipeline scanning for known vulnerabilities
- Integrating data validation with schema enforcement
- Establishing rollback procedures for compromised data
Module 5. Model Integrity and Anti-Tampering Controls
Ensure models remain unaltered from training to inference using cryptographic signing, hashing, and runtime integrity checks.
12 chapters in this module
- Signing models during CI/CD pipeline builds
- Using hash verification in deployment automation
- Detecting unauthorized model replacements
- Secure model registry design with access controls
- Implementing model watermarking techniques
- Monitoring for unexpected prediction shifts
- Validating model inputs against known distributions
- Generating tamper-evident audit logs
- Protecting model weights in memory
- Hardening model servers against reverse engineering
- Using hardware security modules for signing
- Recovering from detected model compromise
Module 6. Security Testing in MLOps Workflows
Embed automated security scanning into CI/CD pipelines to catch vulnerabilities before deployment.
12 chapters in this module
- Integrating SAST tools into model repository workflows
- Scanning for known vulnerabilities in dependencies
- Validating container images with vulnerability databases
- Testing model APIs for common OWASP flaws
- Automating penetration testing for prediction endpoints
- Generating security test reports for pull requests
- Setting security quality gates for deployment
- Using fuzzy testing to expose model edge cases
- Validating input sanitization with adversarial examples
- Monitoring for resource exhaustion patterns
- Integrating security findings into issue trackers
- Prioritizing remediation based on exploitability
Module 7. Privacy and Data Protection in Model Design
Incorporate privacy-by-design principles into model development to meet evolving regulatory expectations.
12 chapters in this module
- Identifying personally identifiable information in features
- Applying differential privacy techniques to training
- Minimizing data retention in model pipelines
- Anonymizing training data while preserving utility
- Conducting privacy impact assessments
- Documenting data lineage for compliance
- Ensuring right to explanation in model outputs
- Designing for data subject access requests
- Auditing model decisions for bias and fairness
- Implementing data deletion workflows
- Securing model explanations against leakage
- Aligning with GDPR and CCPA expectations
Module 8. Secure Collaboration Across Data and Security Teams
Bridge communication gaps between data scientists and AppSec teams using shared frameworks and artefacts.
12 chapters in this module
- Translating model risks into security language
- Creating joint review checklists for deployment
- Facilitating threat modeling workshops
- Documenting assumptions for peer review
- Using shared dashboards for risk visibility
- Establishing escalation paths for critical flaws
- Building trust through consistent security posture
- Integrating security feedback into sprint retros
- Co-developing secure-by-default templates
- Standardizing security documentation format
- Measuring improvement over time
- Recognizing cross-functional contributions
Module 9. Incident Response for Model-Driven Systems
Prepare for and respond to security incidents involving machine learning components.
12 chapters in this module
- Defining incident scope for model-based systems
- Detecting anomalous model behavior in production
- Containing compromised models or data pipelines
- Investigating root cause of security breaches
- Notifying stakeholders of model-related incidents
- Restoring services with clean data and models
- Conducting post-mortems with cross-functional teams
- Updating controls to prevent recurrence
- Documenting lessons learned
- Sharing anonymized findings internally
- Engaging legal and compliance teams
- Rebuilding trust after an incident
Module 10. Security Documentation and Artefact Generation
Produce clear, actionable security documentation that supports audit readiness and stakeholder alignment.
12 chapters in this module
- Writing security READMEs for model repositories
- Generating threat model diagrams
- Documenting control implementations
- Creating security test plans
- Producing compliance mapping matrices
- Maintaining versioned artefacts in source control
- Automating documentation from code comments
- Using templates for consistency
- Structuring artefacts for non-technical reviewers
- Integrating documentation into CI pipelines
- Archiving artefacts for long-term retention
- Reviewing artefacts during team onboarding
Module 11. Governance and Compliance Integration
Align data science practices with organizational governance requirements.
12 chapters in this module
- Mapping model risks to enterprise risk frameworks
- Integrating model oversight into GRC platforms
- Reporting security posture to leadership
- Managing model inventory for compliance
- Establishing model review cycles
- Documenting ethical AI use policies
- Aligning with NIST AI Risk Management Framework
- Supporting third-party audits
- Demonstrating due diligence in design
- Tracking control effectiveness over time
- Engaging legal counsel on AI liability
- Updating policies based on incident trends
Module 12. Scaling Secure Practices Across Teams
Extend secure model development beyond individual contributors to influence team-wide practices.
12 chapters in this module
- Identifying champions for security adoption
- Creating reusable security templates
- Developing internal training materials
- Establishing peer review practices
- Measuring adoption across teams
- Sharing success stories and metrics
- Integrating security into onboarding
- Providing feedback loops for tooling
- Recognizing secure development publicly
- Standardizing secure deployment patterns
- Reducing cycle time for security fixes
- Earning recognition as a trusted partner
How this maps to your situation
- After model design approval but before CI integration
- During sprint planning with security dependencies
- Before first external audit cycle
- When expanding model access to new user groups
Before vs. after
Before
Security reviews feel like gatekeeping events that delay releases, and documentation is reactive , produced only when auditors ask.
After
Security considerations are embedded early, artefacts are generated proactively, and your team earns trust as a fast-moving but disciplined partner.
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 90 minutes per module, designed for completion over 12 weeks with weekend reading.
If nothing changes
Without structured security practices, data science teams risk model recalls, compliance findings, and erosion of trust from engineering and security partners , slowing innovation long-term.
How this compares to the alternatives
Generic cybersecurity courses lack specificity for data science workflows. This course focuses exclusively on OWASP integration in ML contexts , actionable, role-tailored, and production-tested.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical?
Yes , it’s designed for practitioners who ship models and want to integrate security without sacrificing velocity.
Can I use the templates in my current projects?
Yes , all templates are license-free and ready for immediate adaptation to your tooling and standards.
$199 one-time. Approximately 90 minutes per module, designed for completion over 12 weeks with weekend reading..
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