A tailored course, built for your situation
Mastering OWASP for Senior Data Scientists in Gen AI Roles
Build secure generative AI systems with confidence using industry-standard threat prevention frameworks.
$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.
Most data scientists lack a structured framework to address AI-specific vulnerabilities, leading to rework and delayed deployment when security teams flag issues late in the cycle.
The situation this course is for
Even highly technical AI teams face pushback during integration phases because they can’t speak the same risk language as security and compliance. Without a recognized security framework, deployments stall, stakeholders lose confidence, and security debt accumulates.
Who this is for
Senior data scientists in enterprise AI roles who are transitioning from model development to end-to-end AI system ownership, especially where security, compliance, and governance intersect.
Who this is not for
Entry-level developers, non-technical compliance staff, or professionals not actively building or deploying generative AI systems.
What you walk away with
- Apply OWASP Top 10 for LLMs to real-world generative AI design decisions
- Produce audit-ready threat models for AI pipelines
- Anticipate and resolve security team objections before deployment
- Become the internal go-to person for secure AI architecture
- Document controls that survive team changes and leadership shifts
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Security
Introduce the core threat landscape for generative AI with emphasis on OWASP's role in standardizing risk communication across teams.
12 chapters in this module
- Emerging risks in generative AI
- OWASP history and evolution
- AI-specific threat categories
- Threat actor profiles
- Security vs safety distinctions
- Enterprise expectations
- Security team incentives
- Compliance convergence points
- Risk language alignment
- Secure development lifecycle
- Red team perspectives
- AI governance roles
Module 2. OWASP Top 10 for LLMs Overview
Walk through each of the ten critical vulnerabilities with real-world analogs and enterprise impact levels.
12 chapters in this module
- LLM01: Prompt injection
- LLM02: Insecure output handling
- LLM03: Training data poisoning
- LLM04: Denial of service
- LLM05: Supply chain risks
- LLM06: Sensitive data exposure
- LLM07: Weak authentication
- LLM08: Excessive agency
- LLM09: Poor sandboxing
- LLM10: Misalignment
- Risk scoring matrix
- Priority by use case
Module 3. Threat Modeling AI Pipelines
Teach a repeatable method for mapping OWASP risks to specific stages in data ingestion, model inference, and API exposure.
12 chapters in this module
- Pipeline segmentation
- Data source validation
- Prompt flow tracing
- Context window risks
- Output filtering strategies
- API exposure points
- Third-party model risks
- Model fine-tuning controls
- Monitoring blind spots
- Feedback loop threats
- User escalation paths
- Automated risk tagging
Module 4. Prompt Injection Deep Dive
Analyze prompt injection techniques and practical countermeasures using canonical examples and detection logic.
12 chapters in this module
- Direct injection
- Indirect injection
- Homograph attacks
- Unicode smuggling
- Role-jacking
- Chain hijacking
- Instruction reversal
- Context overflow
- Obfuscation techniques
- Detection regex patterns
- Input sanitization
- Model-level defenses
Module 5. Secure Data Flow Design
Guide on structuring AI-enabled workflows to minimize data leakage and meet compliance expectations.
12 chapters in this module
- Data classification schemes
- PII detection in prompts
- Output redaction logic
- Token-level filtering
- Session isolation
- Audit logging
- Retention policies
- Cross-tenant risks
- Encryption boundaries
- Masking strategies
- Data provenance
- Consent-aware outputs
Module 6. Model Integrity and Verification
Establish protocols for validating model behavior against intended design, including drift and sabotage detection.
12 chapters in this module
- Model provenance
- Fine-tune integrity
- Backdoor detection
- Training data audits
- Weight anomaly checks
- Behavioral baselines
- Drift monitoring
- Adversarial testing
- Model rollback triggers
- Version control needs
- Third-party model validation
- Reputation scoring
Module 7. Authentication and Access Control
Design identity-aware AI systems that enforce least privilege and prevent unauthorized escalation.
12 chapters in this module
- User context propagation
- Role-based access
- API key hygiene
- Session binding
- Token lifespan
- Multi-factor enforcement
- Bot detection
- Request rate limits
- IP allow listing
- Identity federation
- Zero-trust integration
- Escalation workflows
Module 8. Sandboxing and Isolation
Implement effective runtime isolation to limit the blast radius of malicious or erroneous AI outputs.
12 chapters in this module
- Execution environment
- Container hardening
- Network segmentation
- Resource limits
- File system access
- Code execution risks
- Memory isolation
- GPU access controls
- Privilege separation
- Logging constraints
- Breakout prevention
- Recovery strategy
Module 9. Monitoring and Detection
Build observability into generative AI systems with tailored alerts and forensic readiness.
12 chapters in this module
- Log schema design
- Anomaly detection
- Prompt pattern alerts
- Output sentiment shifts
- User behavior baselines
- Threat intelligence feeds
- Incident response playbooks
- Forensic data retention
- Automated suppression
- Human-in-the-loop
- Alert fatigue reduction
- Post-incident analysis
Module 10. Compliance Integration
Align OWASP controls with regulatory expectations including FCRA, GDPR, and internal audit requirements.
12 chapters in this module
- Regulatory touchpoints
- FCRA implications
- GDPR Article 22
- Audit trail needs
- Right to explanation
- Bias documentation
- Data subject rights
- Third-party oversight
- Vendor risk
- Certification mapping
- Internal policy alignment
- Global variation
Module 11. Cross-Functional Collaboration
Equip data scientists to lead secure AI initiatives with security, legal, and compliance teams.
12 chapters in this module
- Translating risk
- Security team perspective
- Legal review process
- Compliance checklists
- Documentation standards
- Stakeholder alignment
- Escalation paths
- Risk acceptance
- Security review cycle
- Peer validation
- Conflict resolution
- Joint sign-off
Module 12. Operationalizing Secure AI
Turn principles into practice with templates, playbooks, and deployment checklists.
12 chapters in this module
- Implementation roadmap
- Pre-deployment checklist
- Security sign-off
- Incident playbook
- Stakeholder comms
- Training materials
- Architecture diagrams
- Audit preparation
- Post-mortem process
- Version upgrade path
- Feedback loops
- Continuous improvement
How this maps to your situation
- Onboarding new generative AI projects
- Preparing for internal audit
- Responding to security review feedback
- Leading cross-functional AI initiative
Before vs. after
Before
Spending extra cycles clarifying security gaps late in deployment, relying on ad-hoc fixes rather than a recognized framework.
After
Confidently leading secure AI design with OWASP-aligned artifacts and stakeholder trust from day one.
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 12 hours total, designed for completion over 3-4 weeks with real-world application between modules.
If nothing changes
Without a structured security approach, AI initiatives face delays, rework, or rejection during compliance review, limiting visibility and career growth.
How this compares to the alternatives
Unlike generic AI ethics courses or broad security certifications, this program is tailored to data scientists building production AI systems and provides actionable OWASP-aligned frameworks used by leading organizations.
Frequently asked
Who is this course for?
Senior data scientists and machine learning engineers who are building or deploying generative AI systems in enterprise environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover non-technical compliance topics?
No. The focus is on technical implementation of OWASP standards in AI systems, not legal interpretation or policy drafting.
$199 one-time. Approximately 12 hours total, designed for completion over 3-4 weeks with real-world application between modules..
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