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Duration 21 hours
Course Outline
Foundations: Threat Modeling for Agentic AI
- Classifying agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities
- Defining adversary profiles and attacker capabilities specific to autonomous agents
- Identifying assets, trust boundaries, and critical control points within agent architectures
Governance, Policy, and Risk Management
- Structuring governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Formulating policies for acceptable use, escalation protocols, data handling, and auditability
- Addressing compliance requirements and collecting evidence for audits
Non-Human Identity and Authentication for Agents
- Creating distinct identities for agents using service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing
- Managing the identity lifecycle, including rotation, delegation, and revocation strategies
Access Controls, Secrets, and Data Protection
- Applying fine-grained access control models and capability-based patterns for agents
- Managing secrets, ensuring encryption in transit and at rest, and practicing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry for agent behavior, including intent tracing, command logs, and provenance
- Integrating with SIEM, defining alerting thresholds, and preparing for forensics
- Developing runbooks and playbooks for managing agent-related incidents and containment
Red-Teaming Agentic Systems
- Planning red-team exercises, defining scope, rules of engagement, and safe failover mechanisms
- Employing adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to assess exposure and measure impact
Hardening and Mitigations
- Implementing engineering controls like response throttles, capability gating, and sandboxing
- Enforcing policy and orchestration controls through approval flows, human-in-the-loop checks, and governance hooks
- Applying model and prompt-level defenses, including input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Utilizing deployment patterns such as staging, canary releases, and progressive rollouts for agents
- Maintaining change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance with coordinated playbooks for security, legal, product, and ops teams
Capstone: Red-Team / Blue-Team Exercise
- Executing a simulated red-team attack against a sandboxed agent environment
- Defending, detecting, and remediating as the blue team using established controls and telemetry
- Presenting findings, remediation plans, and necessary policy updates
Summary and Next Steps
Requirements
- A robust foundation in security engineering, system administration, or cloud operations
- Proficiency in AI/ML concepts and an understanding of large language model (LLM) behavior
- Practical experience with Identity and Access Management (IAM) and secure system architecture
Target Audience
- Security engineers and professional red-teamers
- AI operations and platform engineers
- Compliance officers and risk management specialists
- Engineering leads overseeing the deployment of agents
Testimonials (1)
inventory and identifying the different risk exposures within AI
