A tailored course, built for your situation
Mastering OWASP for Agentic AI Systems in Enterprise Environments
Build defensible, auditable AI security practices with framework-backed reasoning and real-world precedent
$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.
Technically sound AI security decisions getting challenged due to lack of cited precedent or structured justification
The situation this course is for
Even robust AI security implementations are being second-guessed in cross-functional reviews. Practitioners who can’t cite specific framework clauses, attack vectors, or documented examples lose influence, regardless of technical accuracy. The gap isn’t in implementation quality, but in justificatory depth.
Who this is for
Senior ML Engineer or AI Security Practitioner working on autonomous or agent-based AI systems in regulated or large-scale enterprise environments
Who this is not for
Engineers focused only on classical ML model security or practitioners not involved in architecture-level AI security decisions
What you walk away with
- Articulate the reasoning behind AI security controls using direct OWASP framework citations
- Reference documented attack patterns and mitigations from high-profile AI incidents
- Defend system design choices with lineage to NIST and ISO-aligned practices
- Produce auditable justifications that survive senior technical review
- Anticipate peer challenges with pre-mapped counterpoints grounded in real-world breaches
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Security in Autonomous Systems
Establish core principles of AI security with emphasis on agent autonomy, goal misalignment, and emergent behavior risks. Ground concepts in OWASP Top 10 for AI and map to enterprise accountability frameworks.
12 chapters in this module
- Defining agentic behavior in modern AI deployments
- OWASP LLM Top 10 overview with enterprise relevance
- Differences between traditional app security and AI security
- Key risks in autonomous decision-making loops
- Case study: Autonomous agent privilege escalation
- Framework alignment with ISO 27001 and NIST CSF
- Mapping OWASP categories to IBM deployment patterns
- Security assumptions in chain-of-thought architectures
- Data provenance and its role in attack surface expansion
- Evaluating trust boundaries in multi-agent systems
- Real-world example: Self-modifying AI in customer service
- Common misconfigurations in prompt chaining
Module 2. Threat Modeling for Agentic Workflows
Apply OWASP threat modeling techniques to agent-driven workflows. Use STRIDE and attack trees to identify vulnerabilities in planning, memory, and tool-use subsystems.
12 chapters in this module
- Introducing STRIDE to agent-based threat analysis
- Identifying spoofing risks in agent identity systems
- Tampering risks in long-term memory stores
- Elevation of privilege in autonomous tool access
- Threat mapping for multi-step agent planning
- Denial of service vectors in agent orchestration
- Information disclosure in agent memory retrieval
- Abuse cases in agent-to-agent communication
- Case study: Compromised agent in supply chain bot
- Using DFDs for agent architecture visualization
- Mapping OWASP AI01 to agent injection attacks
- Validating threat models with red team inputs
Module 3. OWASP AI01: Prompt Injection Deep Dive
Master AI01 by analyzing real prompt injection attacks, defense strategies, and architectural safeguards in agent systems that maintain autonomy while resisting manipulation.
12 chapters in this module
- Understanding prompt injection mechanics in agents
- Direct vs indirect prompt injection techniques
- Case study: Agent bypassing approval workflows
- Role-based prompt boundaries in agent systems
- Input sanitization for natural language interfaces
- Context-aware filtering for agent memory
- Static analysis of prompt flow in agent code
- Runtime monitoring for prompt deviation
- Sandboxing agent actions with guardrails
- Logging and alerting for injection attempts
- Evaluating model-level vs system-level defenses
- Benchmarking defenses using known exploit patterns
Module 4. AI02: Data Poisoning and Training Integrity
Secure agent learning pipelines against data manipulation, including fine-tuning risks, feedback loop poisoning, and adversarial input during reinforcement phases.
12 chapters in this module
- Understanding data poisoning in agent learning
- Risks in human feedback loops for agent training
- Securing reward models from manipulation
- Detecting bias injection in fine-tuning sets
- Validation strategies for third-party training data
- Version control for agent learning artifacts
- Monitoring for distribution drift in inputs
- Case study: Agent optimizing for hidden reward
- Integrity checks for autonomous data collection
- Access controls for model update pipelines
- Cryptographic signing of training datasets
- Audit trails for agent learning decisions
Module 5. AI03: Model Theft and Intellectual Property
Protect proprietary agent logic from extraction and misuse, using OWASP guidance to harden inference interfaces and deployment configurations.
12 chapters in this module
- Model inversion risks in agent APIs
- Membership inference in autonomous systems
- Protecting internal logic from probing attacks
- Rate limiting and query fingerprinting defenses
- Obfuscation techniques for agent decision paths
- Watermarking agent-generated outputs
- Case study: Competitor cloning agent behavior
- Secure model serving in hybrid environments
- Differential privacy in agent output generation
- Legal considerations for AI IP protection
- Monitoring for bulk extraction attempts
- Designing agents with controlled knowledge exposure
Module 6. AI04: Privacy Threats in Autonomous Agents
Address PII leakage, inference risks, and unauthorized data handling in agents with persistent memory and long-term planning capabilities.
12 chapters in this module
- PII detection in agent memory streams
- Anonymization techniques for agent context storage
- Consent tracking in multi-turn agent dialogs
- Case study: Agent leaking data via tool use
- Data minimization in agent planning steps
- Right to be forgotten in persistent agents
- Jurisdictional compliance in agent memory
- Encryption strategies for agent state
- Auditing data access in agent workflows
- Handling biometric and sensitive personal data
- Agent behavior under GDPR and CCPA
- Privacy impact assessment for long-running agents
Module 7. AI05: Model Denial of Service
Prevent resource exhaustion and performance degradation in agent systems through input validation, load management, and architectural resilience.
12 chapters in this module
- Resource exhaustion via agent planning loops
- Input flooding in natural language interfaces
- Case study: Agent stuck in infinite recursion
- Rate limiting for agent-to-agent communication
- Budgeting compute for autonomous workflows
- Timeout strategies for long-running agent tasks
- Monitoring agent memory and CPU utilization
- Fail-safe modes for overloaded agents
- Architectural patterns for scalable agents
- Detecting adversarial input bursts
- Cost controls for autonomous API usage
- Load testing agent systems under stress
Module 8. AI06: Supply Chain Integrity
Secure agent dependencies including tools, plugins, and external APIs using OWASP supply chain guidelines and provenance verification.
12 chapters in this module
- Verifying integrity of prebuilt agent tools
- SBOM use in agent deployment pipelines
- Case study: Malicious plugin in agent toolkit
- Authenticating external API connections
- Secure update mechanisms for agent modules
- Dependency scanning for agent codebases
- Zero-trust access to agent tools
- Monitoring for anomalous tool behavior
- Vendor risk assessment for third-party agents
- Isolating untrusted agent components
- Cryptographic signing of agent extensions
- Audit trails for tool installation and removal
Module 9. AI07: Security Misconfigurations
Eliminate configuration drift and default-risk settings in agent deployment, memory, and access layers through standardized, auditable setups.
12 chapters in this module
- Default settings risks in agent frameworks
- Overprivileged agent role configurations
- Case study: Agent with unnecessary admin access
- Hardening agent memory access controls
- Secure default prompts for autonomous agents
- Configuration management for agent fleets
- Automated scanning for misconfigurations
- Environment-specific agent settings
- RBAC alignment for agent permissions
- Audit logging for configuration changes
- Drift detection in agent deployment states
- Policy-as-code for agent configuration
Module 10. AI08: Unauthorized Data Access
Prevent agent overreach in data access through granular permissions, context-aware policies, and runtime enforcement mechanisms.
12 chapters in this module
- Principle of least privilege in agent design
- Dynamic data access based on agent context
- Case study: Agent accessing restricted systems
- Attribute-based access control for agents
- Contextual authorization in multi-step tasks
- Data access logging at agent decision points
- Real-time policy evaluation for agent queries
- Masking sensitive data in agent outputs
- Time-bound access for agent operations
- Escalation workflows for data access
- Monitoring for anomalous data retrieval
- Zero-trust architecture for agent data flows
Module 11. AI09: Unsafe Output Generation
Ensure agent outputs remain safe, factual, and aligned with organizational values using content moderation, fact-checking, and alignment validation.
12 chapters in this module
- Detecting hallucinated facts in agent responses
- Content moderation for agent-generated text
- Case study: Agent spreading misinformation
- Fact-checking integration in agent workflows
- Alignment with organizational values
- Red teaming for agent output safety
- Output filtering and rewriting strategies
- Human-in-the-loop review triggers
- Reputation risk in agent communications
- Bias detection in agent-generated content
- Domain-specific safety rules for agents
- Versioning safety policies across agent updates
Module 12. AI10: Excessive Agency and Goal Misalignment
Maintain human oversight and system boundaries in autonomous agents through goal validation, kill switches, and alignment monitoring.
12 chapters in this module
- Defining acceptable autonomy boundaries
- Goal decomposition with safety checks
- Case study: Agent optimizing for wrong metric
- Human oversight integration patterns
- Kill switch design for autonomous agents
- Runtime monitoring for goal drift
- Reward function auditing techniques
- Alignment testing for new agent behaviors
- Fail-safe modes for goal misalignment
- Escalation to human review triggers
- Version control for agent goals
- Organizational governance of agent autonomy
How this maps to your situation
- Agentic AI deployment in regulated enterprise environments
- Security review of autonomous decision-making systems
- Architecture-level justification for AI controls
- Cross-functional technical leadership in AI security
Before vs. after
Before
Making technically sound AI security decisions but lacking structured, source-backed reasoning when challenged by peers or reviewers
After
Consistently defending architectural choices with specific examples, framework citations, and attack-path analysis that preempts objections
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 per week for 12 weeks, or self-paced equivalent
If nothing changes
Without defensible justification practices, even correct security decisions may be overridden or deprioritized in cross-functional settings, eroding influence and slowing adoption of robust AI governance.
How this compares to the alternatives
Unlike generic AI security courses, this program focuses exclusively on OWASP’s AI security framework with direct application to agentic systems, providing specific examples, attack patterns, and justification templates used by leading enterprise teams.
Frequently asked
Is this course focused on OWASP for web apps or AI systems?
This course focuses exclusively on OWASP's AI security framework (OWASP Top 10 for LLMs), not traditional web application security.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover IBM-specific tools or platforms?
No. The course avoids proprietary systems and focuses on defensible, framework-backed practices applicable across enterprise AI deployments.
$199 one-time. 90 minutes per week for 12 weeks, or self-paced equivalent.
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