A tailored course, built for your situation
Mastering OWASP for Healthcare AI Architecture Leaders
Speed through secure AI implementation with battle-tested web application safeguards
$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.
Spending too long translating AI security goals into working controls?
The situation this course is for
Senior architects in healthcare AI often face pressure to deliver rapidly while maintaining strict security compliance. Without a repeatable method, teams default to slow, patchwork implementations that break down under audit or scaling pressure.
Who this is for
Vice President and Chief Architect in Healthcare AI, responsible for secure, compliant, and scalable AI systems across regulated environments
Who this is not for
Junior developers, general IT staff, or professionals outside healthcare-focused AI or application security
What you walk away with
- Turn AI security policies into working OWASP-aligned controls in under 7 days
- Reduce time spent on rework by 60% using modular implementation templates
- Produce artefacts that pass internal review the first time
- Deploy a standardized OWASP integration playbook across AI project teams
- Shorten audit preparation cycles by having controls mapped and tested ahead of time
The 12 modules (with all 144 chapters)
Module 1. Foundations of OWASP in AI Systems
Establish the baseline for integrating OWASP principles into AI-driven healthcare applications. Understand how traditional web vulnerabilities translate to AI interfaces and APIs.
12 chapters in this module
- AI security landscape
- OWASP Top 10 relevance
- Healthcare-specific threats
- Threat modeling basics
- AI attack surface mapping
- Compliance alignment
- Risk tolerance definition
- Architecture layer mapping
- Data flow tagging
- Security-by-design principles
- Regulatory touchpoints
- Implementation milestones
Module 2. Threat Modeling for AI-Powered Workflows
Build proactive threat models tailored to AI inference pipelines, data ingestion, and real-time decisioning in clinical environments.
12 chapters in this module
- AI workflow decomposition
- Entry point identification
- Trust boundary analysis
- Adversary profile setup
- Likelihood scoring
- Impact categorization
- Data exposure mapping
- Model access paths
- Privilege escalation checks
- Third-party integrations
- Attack tree construction
- Modeling documentation
Module 3. Secure API Design for Healthcare AI
Design APIs that withstand OWASP API Security Top 10 risks while supporting high-throughput AI services in regulated settings.
12 chapters in this module
- API attack patterns
- Authentication schemes
- Token lifecycle management
- Rate limiting strategies
- Input validation rules
- Payload sanitization
- Schema enforcement
- Error handling safely
- Logging without exposure
- OAuth integration
- Zero-trust API gateways
- Audit trail design
Module 4. Input Validation and Injection Defense
Implement robust defenses against injection attacks targeting AI prompts, data pipelines, and backend services.
12 chapters in this module
- Prompt injection anatomy
- SQLi prevention
- NoSQL attack vectors
- Command injection filters
- Context-aware validation
- Whitelist implementation
- Sanitization pipelines
- Model prompt hardening
- Embedding space monitoring
- Log anomaly detection
- Automated testing triggers
- Real-time blocking rules
Module 5. Authentication and Identity Assurance
Strengthen identity workflows in AI systems to prevent privilege escalation and unauthorized access.
12 chapters in this module
- MFA integration
- Role-based access control
- Attribute-based policies
- Session fixation defenses
- Token expiration tuning
- Biometric validation
- Identity proofing
- Federation risks
- SSO integration
- Account recovery safety
- Session lockout logic
- Identity auditing
Module 6. Session Management at Scale
Architect secure, scalable session handling for distributed AI applications serving healthcare providers and patients.
12 chapters in this module
- Stateless session design
- Token binding
- Session timeout policies
- Concurrent login rules
- IP correlation checks
- Device fingerprinting
- Token revocation
- Refresh token rotation
- Cross-origin protection
- Mobile session handling
- Single sign-on risks
- Anomaly detection triggers
Module 7. Access Control and Privilege Modeling
Design granular access controls that align with clinical workflows and AI system roles.
12 chapters in this module
- Role hierarchy design
- Contextual access rules
- Time-bound permissions
- Just-in-time access
- Escalation workflows
- Segregation of duties
- AI model access logging
- Clinical override patterns
- Audit-ready reporting
- Dynamic policy engines
- Policy versioning
- Change approval chains
Module 8. Cryptographic Storage and Key Management
Implement secure encryption practices for AI models, training data, and inference results in transit and at rest.
12 chapters in this module
- Encryption key lifecycle
- HSM integration
- Key rotation policies
- Data classification schema
- Field-level encryption
- Tokenization frameworks
- Secure key storage
- Algorithm selection
- Key access logging
- Quantum readiness
- Compliance alignment
- Audit trail integration
Module 9. Error Handling and Logging Discipline
Create secure, informative logging systems that support operations without exposing sensitive data.
12 chapters in this module
- Secure log formatting
- PII redaction
- Stack trace suppression
- Error code mapping
- Centralized log aggregation
- Log retention rules
- Anomaly detection
- Real-time alerting
- Incident response triggers
- Audit-ready exports
- Retention compliance
- Cross-system correlation
Module 10. Data Protection in AI Pipelines
Embed data protection controls across AI data ingestion, training, and inference workflows.
12 chapters in this module
- Data provenance tracking
- Masking in dev environments
- Labeling for sensitivity
- Consent tracking
- Right-to-be-forgotten workflows
- Data lineage mapping
- Cross-border data rules
- De-identification standards
- Re-identification risk
- Model memorization checks
- Data access audits
- Automated data policy enforcement
Module 11. Secure Configuration Management
Maintain hardened configurations across AI infrastructure, models, and deployment pipelines.
12 chapters in this module
- Baseline configuration
- Automated compliance checks
- Drift detection
- Patch management
- Container hardening
- Cloud security posture
- Model version control
- Pipeline integrity
- Immutable infrastructure
- Change validation
- Rollback safeguards
- Audit trail capture
Module 12. Operationalizing OWASP Compliance
Turn OWASP controls into repeatable, auditable practices that scale across AI projects.
12 chapters in this module
- Control implementation checklist
- Automated testing integration
- Peer review workflows
- Compliance dashboards
- Audit preparation
- Continuous monitoring
- Incident response integration
- Vendor assessment alignment
- Training rollout
- Leadership reporting
- Documented playbook creation
- Scaling across teams
How this maps to your situation
- Initial AI project kickoff
- Mid-cycle security review
- Pre-audit preparation
- Post-incident process refinement
Before vs. after
Before
Spending weeks translating AI security intent into compliant implementations, with recurring rework and audit delays.
After
Delivering OWASP-aligned AI systems in days, with artefacts that stand up to scrutiny and scale across teams.
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 week over 12 weeks, designed to fit around leadership responsibilities.
If nothing changes
Without a structured method, even skilled architects face growing delays, rework, and exposure as AI systems scale and attract greater regulatory attention.
How this compares to the alternatives
Unlike generic cybersecurity courses, this program is tailored specifically for Healthcare AI architects, focusing on speed-to-compliance and real-world OWASP application in clinical AI systems.
Frequently asked
Is this course technical or strategic?
It's both, focused on actionable technical controls with strategic implementation paths for senior architects.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to existing AI projects?
Yes, each module includes templates and examples designed for immediate use in active implementations.
$199 one-time. Approximately 3-4 hours per week over 12 weeks, designed to fit around leadership responsibilities..
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