A tailored course, built for your situation
Mastering OWASP for Research Leads in High-Efficiency Tech Environments
Build unshakable command of web application security frameworks from the ground up
The situation this course is for
Security reviews often catch research teams off guard because OWASP isn’t fully internalized. Teams that lack proactive alignment face rework, extended cycles, and weakened influence with engineering partners.
Who this is for
Research Lead at a high-growth tech company driving innovation at the intersection of AI and product development
Who this is not for
This course is not for junior analysts or consultants without direct ownership of research-to-production pipelines. It’s tailored for seasoned practitioners who must speak confidently across technical, security, and executive domains.
What you walk away with
- Apply OWASP principles to early-stage research planning
- Anticipate security review questions before they're asked
- Speak confidently about control mappings in cross-functional meetings
- Reduce rework by aligning research outputs with compliance expectations
- Build a repeatable mental model for threat modeling in AI-integrated systems
The 12 modules (with all 144 chapters)
- How OWASP guides early-phase security thinking
- Why Research Leads are now first-line reviewers
- Mapping OWASP to common AI research outputs
- Where OWASP overlaps with internal Meta policies
- How security teams expect OWASP to be used
- Distinguishing OWASP from ISO 27001 and NIST CSF
- The rise of automated compliance tooling referencing OWASP
- Why OWASP matters even when not formally cited
- Using OWASP to strengthen peer review processes
- How OWASP informs red teaming exercises
- Common misconceptions about OWASP scope
- Building credibility through precise OWASP references
- Why injection remains at the top of the list
- How broken access controls manifest in APIs
- Cryptographic failures in AI model serving layers
- How insecure design differs from implementation flaws
- Real case: Authentication bypass in a test rollout
- How security misconfigurations scale with AI
- Vulnerable components in third-party ML libraries
- How logging failures affect incident response
- Server-side request forgery in proxy systems
- How flawed AI access policies create risk
- Data exposure patterns in feature stores
- Prioritizing Top 0.5 risks beyond the main list
- Understanding the three security levels in ASVS
- Mapping ASVS to research system design phases
- How Level 1 applies to internal tooling
- When Level 2 becomes necessary for deployment
- Standards alignment in pre-production reviews
- ASVS and data handling in model training
- Authentication requirements for research APIs
- Session management in cross-platform tools
- Verifying input validation in AI pipelines
- Ensuring secure error handling in outputs
- Encryption expectations for stored research data
- ASVS checkpoints for vendor-integrated tools
- Starting threat modeling with STRIDE
- How OWASP complements DFD-based analysis
- Identifying trust boundaries in AI systems
- Using OWASP to spot design-level risks
- Threat modeling for real-time inference APIs
- How to classify risk severity using OWASP
- Linking threats to mitigations in ASVS
- Documenting findings for audit readiness
- Facilitating sessions with engineering teams
- Capturing edge cases in model input handling
- Evaluating third-party model providers
- Tracking remediation progress post-session
- Four business functions in OWASP SAMM
- How to score current maturity levels
- Applying SAMM to research-specific workflows
- Benchmarking against internal peer teams
- Roadmap planning for maturity improvement
- Integrating SAMM into quarterly planning
- Measuring progress across development cycles
- Using SAMM to justify security investments
- Leadership reporting with SAMM metrics
- Tailoring SAMM for AI development pipelines
- Common pitfalls in SAMM implementation
- How Meta leverages SAMM-adjacent models
- Installing and configuring OWASP ZAP
- Running automated scans on test APIs
- Interpreting scan results for non-security roles
- Differentiating false positives from real risks
- Using ZAP to validate input sanitization
- Scanning AI-powered web interfaces
- Integrating ZAP into CI/CD pipelines
- Generating reports for security teams
- Customizing rules for unique use cases
- Collaborating with security engineers on findings
- Staying within ethical testing boundaries
- When to escalate findings to specialists
- How OWASP applies to AI system architecture
- Mapping OWASP Top 10 to model endpoints
- Prompt injection as a form of injection flaw
- Authentication bypass in AI chat interfaces
- Data exposure in training datasets
- Model inversion and privacy risks
- Adversarial attacks on image classifiers
- Securing model weights and checkpoints
- Monitoring for anomalous model behavior
- Logging AI interactions for audit trails
- Third-party AI service risk assessment
- Building secure AI development guidelines
- Translating OWASP for engineering audiences
- Using OWASP to justify research timelines
- Facilitating joint review sessions
- Aligning with AppSec team priorities
- Negotiating scope with product managers
- Presenting risks to non-technical leaders
- Documenting decisions with OWASP references
- Building trust through consistent framing
- Avoiding overstatement of security claims
- Handling pushback on security findings
- Creating reusable risk narratives
- Owning the security narrative in reviews
- Using OWASP to screen vendor documentation
- Asking the right questions during SIGs
- Evaluating vendor security posture claims
- Mapping vendor controls to OWASP Top 10
- Assessing AI model providers for compliance
- Reviewing third-party code libraries
- Validating API security implementations
- Identifying red flags in vendor responses
- Documenting assessment findings
- Escalating concerns to procurement teams
- Maintaining independence in vendor reviews
- Using OWASP to strengthen negotiation position
- Common OWASP-related questions in reviews
- How to answer confidently without overcommitting
- Preparing evidence in advance
- Structuring responses around control mapping
- Using ASVS to back up claims
- Handling edge-case scenarios
- When to involve AppSec for clarification
- Balancing transparency and risk disclosure
- Documenting assumptions and limitations
- Responding to follow-up questions
- Avoiding common misrepresentations
- Turning feedback into improvement
- Capturing lessons from past projects
- Organizing OWASP knowledge by use case
- Creating templates for common scenarios
- Building decision trees for risk evaluation
- Integrating OWASP into personal workflow
- Sharing knowledge with team members
- Maintaining relevance amid framework updates
- Curating reliable external resources
- Automating checklist applications
- Tracking changes in OWASP guidance
- Using the playbook in peer reviews
- Updating content based on real-world use
- Starting with a hypothetical research project
- Applying OWASP Top 10 from the beginning
- Conducting a threat modeling session
- Using ASVS to define security requirements
- Integrating ZAP into prototype testing
- Assessing third-party dependencies
- Documenting decisions for audit purposes
- Aligning with engineering teams
- Preparing for AppSec review
- Anticipating follow-up questions
- Delivering a secure system narrative
- Reflecting on the end-to-end process
How this maps to your situation
- Research Leads defining early-stage AI systems
- Cross-functional alignment on security expectations
- Pre-production security reviews
- Third-party tool integration for AI research
Before vs. after
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 total, self-paced, designed for completion over a single weekend morning.
How this compares to the alternatives
Generic compliance courses don’t address the nuances of AI research at Meta-scale. This course is tailored to the specific intersection of innovation, research leadership, and security framework fluency.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.