Skip to main content
Image coming soon

Deeper command of the OWASP Top 10 framework for AI and data systems

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Deeper command of the OWASP Top 10 framework for AI and data systems

Build unshakable depth in security engineering for high-impact environments

$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 guidance feels generic and hard to apply to AI-driven data systems

The situation this course is for

Most engineers get handed OWASP as a checklist, not a living framework. Without deep context, it's hard to prioritize which risks matter most in AI-infused architectures, especially when compliance expectations, audit scrutiny, and technical debt pull in different directions.

Who this is for

Senior technical engineer working at the intersection of AI, data, and security who needs to apply standards with precision and influence

Who this is not for

This is not for junior developers looking for code snippets or entry-level certification prep. It’s for experienced engineers who shape system design and need to command the underlying logic of security frameworks.

What you walk away with

  • Internalize the OWASP Top 10 at a pattern level, not just a list
  • Map OWASP risks directly to data pipeline and model inference layers
  • Build repeatable validation workflows for secure AI deployments
  • Anticipate auditor and security team questions with sourced reasoning
  • Lead secure-by-design discussions with confidence and structure

The 12 modules (with all 144 chapters)

Module 1. Understanding OWASP's role in modern AI systems
Learn how the OWASP Top 10 applies uniquely to AI-driven data environments, including model endpoints and inference APIs. Establish context for why foundational security standards still anchor advanced deployments.
12 chapters in this module
  1. What OWASP solves in AI contexts
  2. Where OWASP aligns with AI risk
  3. When to apply each control
  4. AI-specific threat actors
  5. Mapping OWASP to model lifecycle
  6. Security debt in training pipelines
  7. API gateways and injection risks
  8. Data poisoning as an OWASP vector
  9. Authentication in MLOps
  10. Session handling in real-time AI
  11. Logging for attack detection
  12. From checklist to framework
Module 2. Injection risks in data and model layers
Examine SQL, command, and code injection risks across data ingestion, transformation, and model execution layers. Learn how AI systems expand the attack surface and where OWASP control 1 applies most critically.
12 chapters in this module
  1. SQLi in ETL pipelines
  2. Command injection in preprocessing
  3. Code eval risks in notebooks
  4. Model loading vulnerabilities
  5. Container startup scripts
  6. API endpoint parsing flaws
  7. Log injection techniques
  8. Input validation strategies
  9. Context-aware escaping
  10. Parameterized queries in AI
  11. Safe deserialization patterns
  12. Detecting live injection
Module 3. Authentication breakdowns in AI platforms
Analyze failures in identity enforcement across data access, model deployment, and API layers. See how OWASP control 2 maps to token handling, session persistence, and privilege escalation in AI systems.
12 chapters in this module
  1. Weak password policies
  2. Multi-factor bypass paths
  3. Session fixation risks
  4. Token leakage in logs
  5. OAuth misconfigurations
  6. Role confusion in pipelines
  7. Impersonation flaws
  8. Service account sprawl
  9. Short-lived token use
  10. API key storage
  11. Brute force protections
  12. Session timeout design
Module 4. Sensitive data exposure in AI workflows
Track PII and proprietary model logic across training, inference, and reporting. Apply OWASP control 3 to data pipelines with high-throughput, low-latency requirements.
12 chapters in this module
  1. PII in training sets
  2. Model memorization risks
  3. Log redaction failures
  4. Inference result leakage
  5. Data retention policies
  6. Encryption in transit
  7. Encryption at rest
  8. Key management flaws
  9. Debug endpoints exposing data
  10. Third-party data sharing
  11. Model inversion attacks
  12. Output filtering design
Module 5. XML External Entity risks in data parsers
Investigate how legacy parsing methods in data ingestion systems create exploitable paths. Apply OWASP control 4 to modern data pipelines with XML, YAML, or serialized input.
12 chapters in this module
  1. XXE in config ingestion
  2. YAML deserialization risks
  3. DTD parsing dangers
  4. Entity expansion attacks
  5. Log file parsing flaws
  6. Metadata injection vectors
  7. Schema validation gaps
  8. Secure parser configuration
  9. Input sanitization layers
  10. Memory exhaustion via XXE
  11. Remote entity fetching
  12. Testing for XXE exposure
Module 6. Broken access control in AI service layers
Map OWASP control 5 to API gateways, model endpoints, and dashboard interfaces. Learn how permission checks fail in distributed environments and how to harden them systematically.
12 chapters in this module
  1. Direct object reference flaws
  2. Function-level access gaps
  3. Privilege escalation paths
  4. Admin panel exposure
  5. Inference rate limiting
  6. Model update permissions
  7. Metadata access leaks
  8. Role-based filter bypass
  9. Cross-tenant data access
  10. CORS misconfigurations
  11. Access token validation
  12. Audit trail completeness
Module 7. Security misconfigurations in deployment pipelines
Trace how default settings, verbose errors, and exposed interfaces create vulnerabilities in AI model deployments. Apply OWASP control 6 with deployment-specific templates.
12 chapters in this module
  1. Default credentials in containers
  2. Verbose error messages
  3. Debug mode in production
  4. Unnecessary services exposed
  5. Insecure HTTP headers
  6. CSP misconfigurations
  7. Version disclosure risks
  8. Backup file exposure
  9. Cloud storage permissions
  10. CI/CD pipeline leaks
  11. Insecure base images
  12. Runtime environment leaks
Module 8. Cross-Site Scripting in AI dashboards
Examine how XSS risks emerge in internal tools, reporting interfaces, and model monitoring dashboards. Apply OWASP control 7 to data visualization layers and user inputs.
12 chapters in this module
  1. Reflected XSS in search
  2. Stored XSS in comments
  3. DOM-based XSS in widgets
  4. Chart label injection
  5. User-controlled redirects
  6. SVG payload risks
  7. Template engine escapes
  8. Sandboxed iframe use
  9. Content filtering tools
  10. Input context validation
  11. Output encoding layers
  12. Testing for XSS in UIs
Module 9. Insecure dependencies in AI toolchains
Audit third-party libraries, model hubs, and framework dependencies for vulnerabilities. Apply OWASP control 8 to Python packages, container layers, and MLOps tooling.
12 chapters in this module
  1. Vulnerable model hubs
  2. PyPI package risks
  3. NPM dependencies in tooling
  4. Base image scanning
  5. Transitive dependency chains
  6. License compliance risks
  7. Model card integrity
  8. Pretrained model verification
  9. Supply chain signing
  10. SBOM generation
  11. Automated dependency checks
  12. Patch prioritization logic
Module 10. Vulnerable deployment pipelines
Secure CI/CD systems used for AI model updates and data pipeline changes. Apply OWASP control 9 to prevent unauthorized code promotion and configuration drift.
12 chapters in this module
  1. Unvetted model promotion
  2. Pipeline privilege escalation
  3. Lack of code review gates
  4. Unsigned artifact deployment
  5. Credential leakage in jobs
  6. Environment variable leaks
  7. Rollback mechanism flaws
  8. Parallel deployment risks
  9. Manual override paths
  10. Audit trail gaps
  11. Pipeline input validation
  12. Immutable pipeline design
Module 11. Insufficient logging and monitoring
Improve detection of attacks in AI systems by strengthening logging, alerting, and incident response readiness. Implement OWASP control 10 with actionable monitoring design.
12 chapters in this module
  1. Missing login logs
  2. Inadequate event context
  3. Silenced alerts
  4. Log retention gaps
  5. Attack detection delays
  6. False positive tuning
  7. Incident timeline gaps
  8. Automated response rules
  9. User behavior baselines
  10. Model access patterns
  11. API call anomaly detection
  12. Post-attack recovery steps
Module 12. Building your personal OWASP implementation playbook
Synthesize learning into a custom, living document tailored to your environment. Learn how to evolve it as standards and threats shift.
12 chapters in this module
  1. Mapping OWASP to your stack
  2. Custom control definitions
  3. Team-specific examples
  4. Audit preparation workflow
  5. Stakeholder communication plan
  6. Control review cadence
  7. Update tracking system
  8. Cross-functional alignment
  9. Vendor assessment grid
  10. Risk acceptance criteria
  11. Playbook versioning
  12. Living framework maintenance

How this maps to your situation

  • Designing or reviewing AI system architecture
  • Responding to security audit findings
  • Leading secure deployment of ML models
  • Improving internal security maturity

Before vs. after

Before
OWASP is treated as a compliance requirement with little adaptation to AI and data systems
After
You lead with a tailored, internalized framework for securing AI deployments, grounded in the OWASP Top 10 but evolved for your context

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 3-4 hours per module, designed for on-demand, self-paced learning with immediate applicability to current projects.

If nothing changes
Without structured mastery, security efforts remain reactive , reliant on checklists rather than command of underlying principles, making it harder to influence design or respond to evolving threats confidently.

How this compares to the alternatives

Unlike generic OWASP training, this course focuses exclusively on AI and data engineering contexts, with concrete implementation patterns that apply directly to your work. No theory without implementation.

Frequently asked

Is this course focused on web apps or AI systems?
It’s tailored to AI and data systems, showing how OWASP applies to pipelines, models, and APIs , not just traditional web apps.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get a certification?
No. This is a mastery-building program, not a test-prep course. The outcome is a personal implementation playbook, not a badge.
$199 one-time. Approximately 3-4 hours per module, designed for on-demand, self-paced learning with immediate applicability to current 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
!