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GEN0739 Mastering OWASP for Data Scientists Building Secure High Quality ML Pipelines

$199.00
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A tailored course, built for your situation

Mastering OWASP for Data Scientists Building Secure High Quality ML Pipelines

Elevate your models with proven security practices.

$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.
Deliver ML models that are secure and accurate from day one.

The situation this course is for

Many data teams struggle to embed security without sacrificing model performance, leading to results that require costly rework before they can be trusted by stakeholders.

Who this is for

Associate Data Scientists building production ML pipelines at tech companies.

Who this is not for

Data engineers focused solely on infrastructure without modeling responsibilities.

What you walk away with

  • Design OWASP‑aligned data pipelines that resist common attacks
  • Implement security tests that improve model reliability
  • Produce defensible ML outputs that pass audit review the first time
  • Create reproducible, high‑quality documentation for governance
  • Integrate automated security checks into CI/CD for data science

The 12 modules (with all 144 chapters)

Module 1. Foundations of OWASP for Data Science
This module introduces the core OWASP principles and maps them to the data science lifecycle. You will learn how security considerations fit naturally into feature engineering, model training, and deployment, establishing a mindset that quality and security are inseparable. By the end of the module you will have a concrete security checklist that can be applied to any ML project.
12 chapters in this module
  1. Introduction to OWASP principles for machine learning pipelines
  2. Mapping OWASP Top Ten to data science workflows
  3. Assessing threat vectors in feature engineering stages
  4. Identifying common injection risks in model training
  5. Securing data storage with OWASP guidance
  6. Applying authentication controls to data access layers
  7. Implementing logging and monitoring per OWASP standards
  8. Risk rating methodology tailored for analytics projects
  9. Building a security checklist for ML pipeline design
  10. Integrating OWASP compliance into agile data science sprints
  11. Case study: Secure model deployment in a SaaS environment
  12. Preparing documentation for audit and governance reviews
Module 2. Secure Data Collection and Ingestion
Data quality begins at the source. This module covers how to collect, ingest, and validate data while adhering to OWASP guidelines. You will practice techniques for protecting data in transit, validating input schemas, and establishing provenance records that make downstream analysis trustworthy and repeatable.
12 chapters in this module
  1. Establishing trusted data sources with OWASP ingest controls
  2. Validating input schemas to prevent injection attacks
  3. Encrypting data in transit using industry‑standard protocols
  4. Implementing source authentication and authorization mechanisms
  5. Creating immutable audit trails for data acquisition events
  6. Designing data provenance metadata for downstream reproducibility
  7. Applying rate‑limiting and throttling to protect ingestion endpoints
  8. Using OWASP secure coding patterns for custom data connectors
  9. Testing data ingestion pipelines with automated security scanners
  10. Documenting ingestion security controls for compliance reviews
  11. Balancing performance and security in high‑volume data streams
  12. Case examples of secure data collection in enterprise environments
Module 3. Secure Feature Engineering Practices
Feature engineering can unintentionally introduce vulnerabilities. This module teaches you how to apply OWASP safeguards while transforming raw data into model‑ready features, ensuring that each transformation step is both accurate and protected against manipulation.
12 chapters in this module
  1. Analyzing feature transformation code for OWASP injection risks
  2. Applying input sanitization techniques during feature extraction
  3. Securing feature storage with access control policies
  4. Versioning feature sets to maintain reproducibility and auditability
  5. Implementing checksum verification for feature datasets
  6. Detecting anomalous feature values using statistical guards
  7. Automating feature validation tests within CI pipelines
  8. Documenting feature lineage to satisfy governance requirements
  9. Managing feature pipelines with container security best practices
  10. Evaluating third‑party feature libraries for OWASP compliance
  11. Integrating feature‑level security checks into data notebooks
  12. Real‑world example of secure feature engineering in a recommendation system
Module 4. Secure Model Training and Hyperparameter Tuning
Training models on clean, protected data is essential for trustworthy outcomes. This module walks you through securing the training environment, protecting model artifacts, and using OWASP‑inspired testing to catch subtle quality defects before they propagate.
12 chapters in this module
  1. Hardening compute environments for secure model training workloads
  2. Restricting training data access with role‑based permissions
  3. Encrypting model checkpoints and intermediate artifacts at rest
  4. Applying adversarial testing to evaluate model robustness
  5. Integrating OWASP security scans into hyperparameter search workflows
  6. Ensuring reproducible random seed management across training runs
  7. Logging training metadata for traceability and audit purposes
  8. Validating model input boundaries to prevent out‑of‑range errors
  9. Automating model quality gates that enforce security criteria
  10. Documenting training configurations for compliance documentation
  11. Managing secrets for third‑party libraries used during training
  12. Case study: Secure end‑to‑end training of a fraud detection model
Module 5. Secure Model Evaluation and Validation
Evaluation metrics must be reliable and tamper‑proof. This module shows how to embed OWASP checks into model validation, ensuring that performance reports are accurate, defensible, and ready for stakeholder review.
12 chapters in this module
  1. Designing evaluation pipelines that enforce data integrity checks
  2. Applying OWASP data leakage tests during model validation
  3. Securing evaluation datasets against unauthorized modifications
  4. Automating reproducible metric calculations with versioned code
  5. Detecting bias and fairness issues using secure validation frameworks
  6. Recording evaluation artifacts for full audit trails
  7. Implementing threshold alerts for anomalous performance deviations
  8. Integrating security test results into model scorecards
  9. Documenting evaluation methodology for regulatory compliance
  10. Using container scanning to verify evaluation environment security
  11. Presenting validated performance results to cross‑functional stakeholders
  12. Real‑world example of secure model evaluation in a churn prediction project
Module 6. Secure Model Deployment and Serving
Deploying models securely prevents exploitation and maintains quality in production. This module covers container hardening, API security, and OWASP‑aligned monitoring so that your models remain reliable and trustworthy once they go live.
12 chapters in this module
  1. Containerizing models with minimal attack surface configurations
  2. Applying OWASP API security controls to model serving endpoints
  3. Encrypting model payloads in transit using TLS best practices
  4. Establishing role‑based access for model invocation services
  5. Implementing runtime integrity checks to detect tampering attempts
  6. Automating security scans of deployment artifacts before release
  7. Configuring observability dashboards to monitor model health securely
  8. Setting up automated rollback procedures for security incidents
  9. Documenting deployment topology for governance and audit reviews
  10. Validating model outputs against security policy thresholds
  11. Integrating post‑deployment security testing into continuous delivery
  12. Case study: Secure production rollout of a real‑time recommendation engine
Module 7. Continuous Monitoring and Incident Response
Ongoing vigilance is key to maintaining quality. This module teaches you how to monitor model behavior, detect security anomalies, and respond quickly using OWASP‑guided incident response playbooks.
12 chapters in this module
  1. Setting up real‑time monitoring of model input and output streams
  2. Defining anomaly detection rules based on OWASP threat models
  3. Logging security events with structured, searchable formats
  4. Establishing incident response runbooks for model compromise scenarios
  5. Automating alerting workflows to notify data science owners instantly
  6. Conducting regular security drills to validate response effectiveness
  7. Performing root‑cause analysis of detected security incidents
  8. Updating security controls based on lessons learned from incidents
  9. Maintaining compliance evidence for continuous audit readiness
  10. Integrating monitoring alerts into existing data‑ops dashboards
  11. Measuring mean time to detection and resolution for model incidents
  12. Real‑world example of rapid response to a model injection attack
Module 8. Compliance Reporting and Governance
Regulators and internal auditors expect clear evidence of security controls. This module equips you with templates and processes to produce concise, defensible reports that showcase the quality and security of your ML assets.
12 chapters in this module
  1. Mapping OWASP controls to common regulatory frameworks for data science
  2. Preparing evidence packages that demonstrate secure model lifecycle practices
  3. Creating executive‑level dashboards summarizing security posture of ML pipelines
  4. Automating generation of compliance artifacts from CI/CD pipelines
  5. Documenting risk assessments and mitigation actions for each model
  6. Building a governance matrix linking features to security requirements
  7. Conducting internal audits using OWASP‑based checklists
  8. Responding to auditor inquiries with ready‑made security documentation
  9. Maintaining versioned policy documents aligned with model updates
  10. Ensuring data privacy considerations are addressed alongside OWASP controls
  11. Providing transparent audit trails for model training and deployment events
  12. Case study: Successful audit of a regulated financial forecasting model
Module 9. Automated Security Testing for ML Code
Automation reduces manual effort and improves consistency. This module shows how to embed static analysis, dependency scanning, and dynamic testing into your ML development workflow, guaranteeing high‑quality, secure code at every commit.
12 chapters in this module
  1. Integrating static code analysis tools that cover OWASP rules into notebooks
  2. Scanning third‑party Python packages for known vulnerabilities
  3. Running dynamic security tests against model inference APIs
  4. Configuring pre‑commit hooks to enforce security standards on code changes
  5. Generating actionable security findings reports for data science teams
  6. Automating remediation suggestions based on OWASP best practices
  7. Validating that security tests do not degrade model performance
  8. Scheduling regular dependency updates to minimize exposure risk
  9. Embedding security test results into pull‑request review dashboards
  10. Documenting test coverage metrics for compliance evidence
  11. Training team members to interpret and act on security findings
  12. Real‑world example of continuous security testing in a large‑scale ML project
Module 10. Governance and Documentation Practices
Clear, up‑to‑date documentation is the backbone of defensible ML work. This module guides you through creating living documents that capture security decisions, model provenance, and quality metrics, making audits painless.
12 chapters in this module
  1. Establishing a centralized knowledge base for ML security policies
  2. Documenting model lineage from raw data to production artifact
  3. Recording security decisions and rationales for each pipeline stage
  4. Maintaining change logs that track security‑related modifications
  5. Creating templates for security impact assessments of new features
  6. Ensuring documentation is version‑controlled alongside code repositories
  7. Linking model performance dashboards to documented quality standards
  8. Conducting peer reviews of documentation to enforce consistency
  9. Preparing executive summaries that highlight security and quality outcomes
  10. Automating documentation updates from CI/CD pipeline metadata
  11. Providing audit‑ready evidence of governance processes
  12. Case study: Streamlined audit preparation through structured documentation
Module 11. Scaling Secure ML Practices Across Teams
As your organization grows, security must scale. This module teaches you how to propagate OWASP‑driven quality standards across multiple data science squads, ensuring consistent high‑quality outputs enterprise‑wide.
12 chapters in this module
  1. Defining organization‑wide security standards based on OWASP guidelines
  2. Creating reusable security templates for new ML projects
  3. Establishing a center of excellence to mentor teams on secure practices
  4. Implementing shared CI/CD pipelines that enforce security gates
  5. Measuring cross‑team compliance with security and quality KPIs
  6. Facilitating knowledge‑sharing workshops on OWASP best practices
  7. Scaling audit readiness through centralized evidence collection mechanisms
  8. Aligning security metrics with business outcomes for executive buy‑in
  9. Coordinating rollout plans for security updates across model portfolios
  10. Managing dependencies and shared libraries with organization‑wide scanning
  11. Ensuring consistent documentation standards across distributed teams
  12. Success story: Enterprise‑wide adoption of secure ML pipelines at a global tech firm
Module 12. Future‑Proofing ML Quality and Security
The final module looks ahead, helping you anticipate emerging threats and evolve your security posture. You will leave with a roadmap to keep your ML assets resilient, high‑quality, and ready for future regulatory expectations.
12 chapters in this module
  1. Monitoring emerging OWASP threat trends relevant to data science
  2. Assessing the impact of new privacy regulations on model pipelines
  3. Designing a continuous improvement loop for security and quality controls
  4. Building a risk register that evolves with technology and business changes
  5. Investing in skill development to keep the team ahead of security challenges
  6. Aligning future security investments with strategic business priorities
  7. Creating a roadmap for incremental adoption of advanced OWASP controls
  8. Evaluating the ROI of security enhancements in ML project budgets
  9. Preparing for next‑generation audit frameworks that emphasize AI ethics
  10. Establishing partnerships with external security experts for periodic reviews
  11. Communicating long‑term security vision to senior leadership and stakeholders
  12. Action plan template for sustaining high‑quality, secure ML operations

How this maps to your situation

  • ml_pipeline_security
  • model_deployment
  • audit_preparation
  • continuous_monitoring

Before vs. after

Before
Model outputs may contain hidden vulnerabilities and require rework before they can be trusted.
After
Your ML models are hardened, reproducible, and audit‑ready, delivering polished results the first 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 6, 8 hours per week over six weeks.

If nothing changes
Skipping OWASP best practices can erode confidence in data‑driven decisions and invite costly remediation later.

How this compares to the alternatives

Compared to generic data‑science courses, this program embeds security directly into every stage of your ML workflow, delivering measurable quality lifts.

Frequently asked

What prior knowledge is required?
Basic familiarity with Python, machine learning concepts, and data pipelines is sufficient.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive practical artifacts?
Yes, each module includes downloadable templates, checklists, and a hand‑built implementation playbook.
$199 one-time. Approximately 6, 8 hours per week over six weeks..

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
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