A tailored course, built for your situation
Mastering OWASP; A Step-by-Step Guide to Secure AI Systems
Build a compounding library of reusable security patterns for AI-driven systems
$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 work that doesn’t scale across projects
The situation this course is for
Most security implementations are one-offs. They don’t compound. Each new model restarts the risk assessment from zero, creating redundant effort and missed leverage across teams.
Who this is for
Senior Machine Learning Engineer shipping AI systems in high-visibility environments
Who this is not for
Engineers focused only on isolated model tuning without deployment or security context
What you walk away with
- A personal library of OWASP-aligned security patterns tailored to ML systems
- Reusable threat models that reduce scoping time for new deployments
- Validated detection rules for prompt injection, model poisoning, and data leakage
- Cross-team recognition as a security-fluent AI engineer
- A documented portfolio of defences that compounds across projects
The 12 modules (with all 144 chapters)
Module 1. Mapping OWASP Top 10 to AI System Layers
Ground security efforts in the most widely adopted framework by identifying direct parallels between traditional web vulnerabilities and AI system risks such as prompt injection, model inversion, and adversarial inputs. This module helps engineers translate known threats into preventive controls at model, pipeline, and interface levels.
12 chapters in this module
- Identifying injection risks in natural language interfaces
- Mapping broken access controls to model endpoints
- How unvalidated data flows enable model poisoning
- Applying input validation principles to embedding layers
- Detecting insecure output handling in generative models
- Securing model artifacts in distributed environments
- Risk of exposure through vector database queries
- Authentication gaps in federated learning systems
- Configuration drift in deployed model instances
- Cryptographic failures in model signing and verification
- Hardening API gateways for model inference
- Integrating logging and monitoring at inference time
Module 2. Threat Modeling for ML Pipelines
Develop systematic approaches to identify, categorize, and prioritize threats across the ML lifecycle, from data ingestion to inference. Learn to generate repeatable threat models that become assets across teams and projects.
12 chapters in this module
- Decomposing ML systems into trust boundaries
- Identifying data provenance vulnerabilities
- Mapping privilege escalation paths in training jobs
- Detecting model data leakage during inference
- Assessing risks from third-party training datasets
- Evaluating vector embedding privacy risks
- Model extraction through query-based attacks
- Threats from shadow models and distillation
- Securing feature stores and metadata
- Detecting adversarial gradients in distributed training
- Monitoring for abnormal model drift indicators
- Creating reusable threat model templates
Module 3. Designing Secure Prompt Interfaces
Build defences into prompt handling layers to prevent injection, leakage, and privilege escalation. This module delivers practical patterns for sanitizing inputs, validating outputs, and isolating prompt execution contexts.
12 chapters in this module
- Recognizing prompt injection syntax patterns
- Sanitizing user inputs without degrading model performance
- Implementing role-based prompt templates
- Detecting indirect prompt injection attempts
- Securing chatbot memory and context windows
- Validating output content before display
- Preventing command execution through prompts
- Isolating prompt execution in sandboxed environments
- Rate limiting for prompt-based APIs
- Logging prompt interactions for audit
- Mitigating prompt leakage via conversation history
- Building automated prompt validation rules
Module 4. Model Integrity and Supply Chain Controls
Secure the lineage of models from training to deployment, applying software supply chain principles to ML artifacts. Learn to verify model provenance, detect tampering, and enforce policy across deployment pipelines.
12 chapters in this module
- Establishing model provenance with metadata tags
- Signing model checkpoints cryptographically
- Verifying training data sources and licenses
- Detecting unauthorized model modifications
- Implementing model attestation workflows
- Securing transfer learning from public models
- Auditing fine-tuning data for poisoning risks
- Validating model outputs against training intent
- Creating immutable model registries
- Enforcing approval workflows for model promotion
- Monitoring for unauthorized model access
- Integrating model signing into CI/CD
Module 5. Data Protection in AI Workflows
Apply data-centric security controls to training, embedding, and inference stages. This module focuses on preventing leakage, ensuring privacy, and maintaining compliance across AI data pipelines.
12 chapters in this module
- Identifying PII in unstructured text datasets
- Masking sensitive data in training corpora
- Implementing differential privacy in model outputs
- Securing vector embeddings in storage
- Detecting membership inference risks
- Minimizing data retention in model caches
- Applying data minimization principles to prompts
- Encrypting model training data at rest
- Controlling access to feature stores
- Logging data access for compliance audits
- Implementing data deletion workflows
- Validating data anonymization effectiveness
Module 6. Securing Model Inference Endpoints
Harden real-time model serving infrastructure against abuse, denial-of-service, and unauthorized access. Learn to build resilient, monitored, and authenticated inference layers.
12 chapters in this module
- Implementing rate limits for model APIs
- Authenticating service-to-service model calls
- Validating input schema for model endpoints
- Detecting abnormal query patterns
- Securing model output formats
- Isolating multi-tenant model environments
- Mitigating denial-of-service on inference servers
- Monitoring for model output anomalies
- Enforcing geo-based access restrictions
- Auditing inference request logs
- Validating model version in responses
- Building fallback mechanisms for outages
Module 7. Automated Security Testing for AI Systems
Integrate proactive security validation into development workflows using fuzzing, adversarial testing, and rule-based scanners tailored for AI components.
12 chapters in this module
- Generating adversarial inputs for text models
- Fuzzing prompt interfaces with edge cases
- Testing for model bias amplification
- Automating OWASP checks in CI pipelines
- Detecting insecure default configurations
- Validating model explanations for consistency
- Running red team simulations on chatbots
- Testing for prompt leakage in outputs
- Scanning for exposed model endpoints
- Automating policy compliance checks
- Integrating security linters into IDEs
- Reporting security test results to stakeholders
Module 8. Monitoring and Incident Response for AI Systems
Establish real-time detection, alerting, and response protocols for AI-specific security incidents. Turn monitoring into a force multiplier across deployments.
12 chapters in this module
- Defining normal vs. abnormal model behavior
- Detecting prompt injection through log patterns
- Monitoring for model data leakage
- Alerting on unauthorized access attempts
- Responding to model poisoning incidents
- Investigating anomalous output patterns
- Logging model prediction drift over time
- Tracking model retraining events
- Integrating with SOC for AI threat visibility
- Documenting incident playbooks for AI systems
- Conducting post-mortems on security events
- Updating controls based on incident learnings
Module 9. Governance and Compliance for AI Security
Align security practices with regulatory expectations and internal governance frameworks. Learn to document controls, demonstrate compliance, and communicate security posture to leadership.
12 chapters in this module
- Mapping OWASP controls to NIST AI guidelines
- Documenting security controls for audits
- Aligning with ISO 27001 for AI systems
- Preparing for SOC 2 reviews with AI scope
- Demonstrating compliance with privacy laws
- Reporting security metrics to leadership
- Creating audit-ready control narratives
- Integrating AI security into vendor reviews
- Managing third-party model risk
- Establishing AI security review boards
- Training teams on security policies
- Maintaining up-to-date compliance documentation
Module 10. Building Reusable Security Playbooks
Transform one-off fixes into institutional knowledge by designing structured, shareable playbooks that accelerate future responses and reduce incident impact.
12 chapters in this module
- Documenting common attack patterns
- Creating step-by-step incident guides
- Standardizing communication templates
- Defining escalation paths for AI incidents
- Integrating playbooks with monitoring tools
- Updating playbooks from incident feedback
- Sharing playbooks across engineering teams
- Versioning and maintaining playbook accuracy
- Training teams on playbook use
- Automating playbook triggering from alerts
- Measuring playbook effectiveness
- Linking playbooks to control frameworks
Module 11. Scaling Security Across AI Teams
Enable secure innovation at scale by building shareable assets, templates, and standards that reduce duplication and raise baseline security across organizations.
12 chapters in this module
- Creating shared libraries of security rules
- Standardizing threat modeling templates
- Distributing secure model starter kits
- Establishing cross-team security reviews
- Mentoring engineers on secure AI design
- Hosting internal security knowledge shares
- Building centralized model registries
- Enforcing security policy through automation
- Recognizing secure engineering practices
- Integrating security into onboarding
- Tracking team-level security metrics
- Scaling security champions programs
Module 12. Owning the Evolution of AI Security Practice
Position yourself as a leader in the emerging discipline of AI security by contributing to frameworks, sharing insights, and shaping best practices across the field.
12 chapters in this module
- Contributing to open-source AI security tools
- Publishing lessons from real incidents
- Speaking at internal and external forums
- Mentoring next-generation AI security engineers
- Shaping internal AI security policy
- Engaging with standards bodies on AI
- Building public technical credibility
- Creating open educational materials
- Collaborating with academic researchers
- Influencing product roadmap decisions
- Measuring the ROI of security initiatives
- Leaving durable artifacts beyond code
How this maps to your situation
- AI system vulnerabilities and OWASP alignment
- Threat modeling for ML pipelines
- Secure prompt design and implementation
- Model integrity and supply chain security
Before vs. after
Before
Security work resets with every project, no compounding benefit.
After
Each deployment strengthens a growing library of reusable defences.
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: 90 minutes per week over twelve weeks, with immediate application to active projects.
If nothing changes
Without a compounding approach, security effort remains reactive and siloed, limiting influence and requiring redundant work across teams.
How this compares to the alternatives
Generic security courses teach broad principles. This course delivers specific, actionable patterns for AI systems, aligned with OWASP, tested in production, and designed to compound across deployments.
Frequently asked
Is this course focused on web app security or AI systems?
It’s specifically tailored to securing AI and machine learning systems, using OWASP principles adapted to prompt interfaces, model pipelines, and inference endpoints.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-Meta environments?
Yes. The patterns are designed to be portable across organizations and cloud platforms.
$199 one-time. 90 minutes per week over twelve weeks, with immediate application to 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