A tailored course, built for your situation
Mastering OWASP; A Step-by-Step Guide to Secure ML Deployment
Build and ship compliant, attack-resistant AI systems with confidence using field-tested security patterns.
$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.
Security rework delaying ML model deployment
The situation this course is for
ML engineers invest significant effort building models, only to face last-minute security blockers during integration. Without a structured way to anticipate and address OWASP risks early, teams face rework, delayed releases, and misalignment with AppSec. This course eliminates that friction by embedding OWASP compliance directly into the ML deployment lifecycle.
Who this is for
ML Engineer working at a large tech company shipping AI-driven features under tight security and compliance scrutiny.
Who this is not for
Engineers focused only on theoretical AI research with no deployment pipeline involvement, or security generalists not involved in ML system integration.
What you walk away with
- Own final OWASP alignment decisions for new model deployments
- Produce security documentation that passes red team review on first submission
- Reduce time spent on security rework by automating risk mapping
- Integrate OWASP controls directly into CI/CD pipelines for ML models
- Standardize secure deployment patterns across AI projects
The 12 modules (with all 144 chapters)
Module 1. Foundations of OWASP in Machine Learning Systems
Establish the core link between OWASP principles and ML-specific attack surfaces, including data poisoning, model inversion, and adversarial inputs.
12 chapters in this module
- Understanding OWASP Top 10 relevance to ML pipelines
- Mapping traditional web vulnerabilities to ML components
- Identifying high-risk interfaces in model serving layers
- Threat modeling for AI inference endpoints
- Common misconfigurations in containerized ML workloads
- Authentication gaps in model APIs
- Data validation failures in feature stores
- Session management flaws in interactive AI tools
- Access control weaknesses in model dashboards
- Encryption risks in model parameter storage
- Logging blind spots during model execution
- Error handling leaks in AI service responses
Module 2. OWASP Risk Assessment for Model Development
Apply OWASP risk scoring to ML development phases, focusing on training data integrity, model provenance, and dependency hygiene.
12 chapters in this module
- Assessing data source trustworthiness for model training
- Detecting poisoned samples in public datasets
- Verifying model lineage and version provenance
- Scanning third-party libraries for known vulnerabilities
- Evaluating pre-trained model risk from external sources
- Hardening notebook environments against code injection
- Securing model checkpoint storage locations
- Validating input schema robustness
- Testing for prompt injection susceptibility
- Benchmarking model resilience to adversarial examples
- Documenting model assumptions for audit readiness
- Integrating threat intelligence into model design
Module 3. Secure Model Deployment Architecture
Design deployment patterns that enforce OWASP compliance by default, including network segmentation, API gateways, and runtime protection.
12 chapters in this module
- Architecting zero-trust access for model endpoints
- Implementing rate limiting for inference APIs
- Configuring WAF rules for AI service traffic
- Isolating model execution environments
- Enforcing mTLS between model components
- Hardening Kubernetes deployments for ML workloads
- Securing model update mechanisms
- Validating model signatures before loading
- Monitoring for unauthorized model access
- Applying least privilege to model service accounts
- Automating security policy enforcement
- Integrating with centralized identity providers
Module 4. OWASP Compliance in CI/CD Pipelines
Embed OWASP checks directly into ML CI/CD workflows to catch vulnerabilities before deployment.
12 chapters in this module
- Integrating SAST tools into model build processes
- Running DAST scans on staging model endpoints
- Automating OWASP Top 10 validation at merge
- Scanning model artifacts for secrets
- Validating container images for known CVEs
- Enforcing code quality gates for ML scripts
- Generating compliance reports automatically
- Blocking high-risk merges programmatically
- Integrating security unit tests with model code
- Tracking technical debt in model repositories
- Versioning security policies alongside models
- Auditing pipeline changes for security impact
Module 5. Data Security in Machine Learning Workflows
Protect sensitive data throughout the ML lifecycle, from ingestion to inference, aligned with OWASP data protection guidance.
12 chapters in this module
- Classifying data sensitivity in feature stores
- Masking PII in model training datasets
- Encrypting data in transit for distributed training
- Securing data access credentials
- Implementing data retention policies
- Auditing data access patterns
- Detecting anomalous data queries
- Preventing data leakage via model outputs
- Applying differential privacy techniques
- Managing data sharing agreements
- Validating data anonymization effectiveness
- Documenting data flow for compliance
Module 6. Model Integrity and Anti-Tampering Controls
Ensure model integrity from training to production using cryptographic and procedural safeguards.
12 chapters in this module
- Signing models to prevent unauthorized modification
- Verifying model checksums at load time
- Detecting model drift as a security signal
- Monitoring for unexpected model behavior
- Implementing model rollback safeguards
- Securing model update distribution channels
- Auditing model configuration changes
- Protecting against model inversion attacks
- Hardening model extraction defenses
- Applying watermarking to proprietary models
- Logging model access and execution events
- Enabling tamper-evident logging
Module 7. Attack Surface Management for AI Systems
Systematically identify and reduce the attack surface of deployed ML systems using OWASP methodologies.
12 chapters in this module
- Mapping all entry points to ML models
- Identifying exposed debugging interfaces
- Securing model monitoring dashboards
- Reducing API surface area
- Disabling unused model endpoints
- Hardening model logging infrastructure
- Protecting against denial-of-model attacks
- Securing model explanation interfaces
- Limiting access to model metadata
- Monitoring for reconnaissance activity
- Applying network egress controls
- Documenting system dependencies
Module 8. Incident Response for Compromised Models
Prepare for and respond to security incidents involving ML models using OWASP-aligned playbooks.
12 chapters in this module
- Detecting model poisoning attempts
- Identifying adversarial input patterns
- Responding to data leakage incidents
- Containing compromised model endpoints
- Investigating unauthorized access
- Preserving forensic evidence
- Notifying stakeholders appropriately
- Activating incident response teams
- Documenting root cause analysis
- Implementing post-incident controls
- Updating training data after attacks
- Communicating remediation steps
Module 9. Third-Party and Supply Chain Risk in AI
Manage risks from external dependencies in ML systems, including pre-trained models and open-source libraries.
12 chapters in this module
- Assessing vendor security posture
- Validating third-party model claims
- Auditing open-source library usage
- Monitoring for dependency vulnerabilities
- Enforcing software bills of materials
- Evaluating model marketplace risks
- Securing API-based model services
- Managing license compliance
- Tracking model update frequency
- Assessing model bias disclosures
- Verifying performance claims
- Documenting third-party integrations
Module 10. Security Automation for ML Operations
Automate OWASP compliance checks in production ML environments using monitoring and policy-as-code tools.
12 chapters in this module
- Automating vulnerability scanning schedules
- Implementing policy-as-code for model deployment
- Alerting on anomalous model behavior
- Enforcing security baselines in production
- Automating compliance report generation
- Integrating security tools with observability
- Detecting configuration drift
- Applying automated remediation
- Monitoring for unauthorized changes
- Validating runtime security controls
- Tracking security metric trends
- Auditing automated actions
Module 11. Compliance Documentation for Audits
Generate audit-ready documentation that demonstrates OWASP compliance for ML systems.
12 chapters in this module
- Creating model security design documents
- Documenting risk assessment outcomes
- Producing architecture decision records
- Maintaining security control mappings
- Generating compliance evidence packages
- Preparing for red team exercises
- Responding to auditor inquiries
- Updating documentation after changes
- Archiving versioned security records
- Demonstrating continuous compliance
- Aligning with internal policies
- Integrating with enterprise GRC tools
Module 12. Scaling Secure ML Practices Across Teams
Extend OWASP-aligned security practices across multiple ML teams using standardized templates and shared tooling.
12 chapters in this module
- Creating reusable security blueprints
- Developing team onboarding materials
- Sharing threat models across projects
- Standardizing security review checklists
- Establishing center of excellence
- Conducting peer security reviews
- Running cross-team workshops
- Maintaining security knowledge base
- Tracking security metrics organization-wide
- Recognizing secure development practices
- Integrating with developer tooling
- Evolving practices based on feedback
How this maps to your situation
- ML model development lifecycle
- Secure deployment architecture
- CI/CD integration
- Compliance and audit readiness
Before vs. after
Before
Spending cycles on last-minute security fixes, escalating decisions, and rework during ML deployment.
After
Finalizing OWASP alignment independently, shipping secure models faster, and owning architecture decisions.
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 to be completed at your pace over several weeks.
If nothing changes
Without structured OWASP integration, ML teams face recurring security escalations, delayed deployments, and increased exposure to adversarial attacks.
How this compares to the alternatives
Unlike generic cybersecurity courses, this program is tailored to ML engineers, focusing on OWASP application in AI systems rather than theoretical frameworks.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course focused on web security or ML systems?
It bridges OWASP principles with ML-specific risks, covering model integrity, data security, and deployment hardening.
Can I apply this to non-AI systems?
While focused on ML, the OWASP integration patterns are adaptable to other software systems.
$199 one-time. Approximately 90 minutes per module, designed to be completed at your pace over several 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