A tailored course, built for your situation
Mastering OWASP for Senior Research Engineers in AI Infrastructure
Build secure, scalable multi-node GPU clusters with authoritative control over security architecture
$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.
Engineers building large-scale AI infrastructure often lack formal authority over security decisions despite owning the systems.
The situation this course is for
Even highly capable teams face delays when security reviews come late or from external teams unfamiliar with cluster-specific risks. The result is rework, bottlenecks, and diluted ownership.
Who this is for
Senior research engineers leading AI infrastructure design who want formal recognition and decision rights over security implementation within their current role
Who this is not for
Junior engineers learning basics, compliance auditors, or professionals outside AI systems development
What you walk away with
- Own the threat model for multi-node GPU clusters end to end
- Define secure configuration baselines aligned with OWASP Top 10 for AI systems
- Lead security validations without escalation to external teams
- Document and justify architectural trade-offs using OWASP controls
- Integrate proactive security checks into cluster deployment pipelines
The 12 modules (with all 144 chapters)
Module 1. OWASP Fundamentals in AI Infrastructure
Ground your cluster security in core OWASP principles tailored to distributed computing environments.
12 chapters in this module
- Threat landscape for AI clusters
- Mapping OWASP Top 10 to GPU nodes
- Security vs performance trade-offs
- Asset classification in multi-node systems
- Attack vectors on inter-node communication
- Privilege escalation paths in containers
- Data exposure risks during training
- Insecure API patterns in ML workflows
- Dependency risks in AI frameworks
- Configuration drift in cluster nodes
- Session management in distributed jobs
- Error handling in fault-tolerant systems
Module 2. Secure Cluster Architecture Patterns
Apply OWASP design patterns to multi-GPU, multi-node environments.
12 chapters in this module
- Zero-trust between compute nodes
- Network segmentation for GPU fabrics
- Secure boot in AI hardware
- Role-based access for research teams
- Isolation of sensitive training jobs
- Trusted execution environments
- Hardware root of trust integration
- Secure firmware update processes
- Air-gapped cluster configurations
- Encrypted inter-node messaging
- Secure cluster orchestration
- Policy enforcement in Kubernetes AI workloads
Module 3. Threat Modeling for Distributed Training
Lead threat modeling sessions specific to large-scale model training.
12 chapters in this module
- Data flow mapping across GPUs
- Identifying high-risk model parameters
- Attacker objectives in AI systems
- Threat actor profiles for research infra
- Model poisoning pathways
- Gradient leakage risks
- Membership inference scenarios
- Adversarial input vectors
- Model checkpoint exfiltration
- Training data provenance
- Model version vulnerabilities
- Federated learning attack surfaces
Module 4. Configuration Security at Scale
Enforce secure baselines across hundreds of nodes.
12 chapters in this module
- Hardening CUDA-enabled hosts
- Secure SSH access for GPU nodes
- Container image scanning pipeline
- Minimal OS footprint for training
- GPU driver vulnerability management
- Automated config drift detection
- Secure logging in high-throughput clusters
- Node health attestation
- Immutable node configurations
- Secure time synchronization
- Certificate lifecycle in distributed systems
- Audit logging for cluster operations
Module 5. Secure Development Lifecycle Integration
Embed OWASP practices into AI research workflows.
12 chapters in this module
- Pre-commit security hooks
- Automated dependency scanning
- Model signing and integrity
- Secure data pipelines
- Code review checklists for AI
- Static analysis for PyTorch scripts
- Dynamic testing in staging clusters
- Model card security sections
- Artifact provenance tracking
- Secure notebook environments
- Pipeline integrity checks
- End-to-end encryption in data loaders
Module 6. API Security in AI Orchestration
Protect the control plane of distributed AI systems.
12 chapters in this module
- Authentication for cluster APIs
- Rate limiting for job submission
- Input validation for model hyperparameters
- RBAC for distributed jobs
- Secure job scheduling
- API key lifecycle management
- Job output exfiltration prevention
- Model API exposure risks
- Cross-cluster API calls
- Secure callback endpoints
- Webhook security in training loops
- Audit trails for API actions
Module 7. Data Protection in Model Training
Apply OWASP data security to sensitive training data.
12 chapters in this module
- Data encryption at rest on GPU nodes
- In-memory data protection
- Secure data shuffling
- Differential privacy implementation
- Data anonymization pipelines
- Cross-node data flow tracking
- Data retention in checkpoints
- Secure data augmentation
- PII detection in training sets
- Secure data versioning
- Data poisoning detection
- Encrypted data loading
Module 8. Vulnerability Management in AI Systems
Prioritize and remediate issues in complex environments.
12 chapters in this module
- GPU-specific CVE tracking
- Dependency tree mapping for AI libs
- Vulnerability scoring for model components
- Patch impact on training stability
- Zero-day response for AI frameworks
- Automated vulnerability reporting
- Cluster-wide patch coordination
- Rollback strategies for security updates
- Third-party model risk
- Open-source library audits
- Container vulnerability scanning
- Secure update distribution to nodes
Module 9. Incident Response for AI Infrastructure
Lead breach response specific to research environments.
12 chapters in this module
- Detection of model theft
- Anomaly detection in GPU usage
- Forensics in distributed jobs
- Model rollback procedures
- Secure incident logging
- Containment of compromised nodes
- Root cause analysis for AI systems
- Notification protocols for research teams
- Evidence preservation in clusters
- Post-mortem of training breaches
- Recovery of secure checkpoints
- Lessons learned documentation
Module 10. Security Documentation and Reporting
Produce authoritative security artifacts.
12 chapters in this module
- Security architecture diagrams
- Threat model documentation
- Risk register maintenance
- Security decision logs
- Audit trail preparation
- Compliance mapping to OWASP
- Executive summary writing
- Technical deep dives for peers
- Vendor security questionnaires
- Internal certification packages
- Security playbook authoring
- Runbook creation for on-call
Module 11. Stakeholder Communication and Influence
Lead security discussions with confidence.
12 chapters in this module
- Explaining cluster risks to leadership
- Negotiating security trade-offs
- Presenting threat models to peers
- Building consensus on security budgets
- Documenting rationale for decisions
- Escalating risks effectively
- Mentoring junior engineers
- Collaborating with external auditors
- Training researchers on security
- Advocating for security tooling
- Measuring security program maturity
- Reporting security metrics
Module 12. Continuous Security Improvement
Establish feedback loops for long-term resilience.
12 chapters in this module
- Security metric selection
- Automated compliance checks
- Red team planning
- Penetration testing scope definition
- Security debt tracking
- Lessons from incident data
- Benchmarking against OWASP ASVS
- Security culture development
- Toolchain evolution
- Knowledge sharing across teams
- Security innovation in AI
- Future-proofing cluster design
How this maps to your situation
- When designing a new multi-node cluster
- During architecture review of AI training pipeline
- Prior to external security assessment
- When onboarding new researchers to secure practices
Before vs. after
Before
Security decisions routed through external teams, creating delays and reducing ownership.
After
You lead security architecture, with clear documentation and authority over controls in your AI infrastructure.
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 hours per module, designed for integration with active cluster development cycles.
If nothing changes
Without clear authority, security remains a bottleneck, slowing innovation and diluting your leadership in AI systems.
How this compares to the alternatives
Unlike generic security courses, this program focuses exclusively on OWASP application in AI infrastructure, no theory, no abstractions, just actionable steps for engineers building at scale.
Frequently asked
Is this course relevant for non-web application security?
Yes. It adapts OWASP principles to distributed AI systems, focusing on infrastructure, data, and model security rather than traditional web apps.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get a promotion?
It expands your mandate in your current role, giving you formal control over security decisions in the systems you lead.
$199 one-time. Approximately 3 hours per module, designed for integration with active cluster development cycles..
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