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Duration 21 hours
Course Outline
Foundations: Threat Modeling for Agentic AI
- Categorizing agentic threats: misuse, privilege escalation, data leakage, and supply-chain vulnerabilities
- Defining adversary profiles and capabilities specific to autonomous agents
- Identifying assets, trust boundaries, and critical control points associated with agents
Governance, Policy, and Risk Management
- Implementing governance frameworks for agentic systems, including roles, responsibilities, and approval mechanisms
- Developing policies for acceptable use, escalation protocols, data handling, and audit requirements
- Addressing compliance requirements and gathering evidence for audits
Non-Human Identity & Authentication for Agents
- Architecting agent identities using service accounts, JWTs, and short-lived credentials
- Applying least-privilege access patterns and just-in-time credentialing strategies
- Managing the identity lifecycle, including rotation, delegation, and revocation
Access Controls, Secrets, and Data Protection
- Utilizing fine-grained access control models and capability-based patterns for agents
- Managing secrets, ensuring encryption during transit and at rest, and practicing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Building telemetry for agent behavior, including intent tracing, command logging, and provenance tracking
- Integrating with SIEM, setting alerting thresholds, and preparing for forensics
- Creating runbooks and playbooks for handling agent-related incidents and containment
Red-Teaming Agentic Systems
- Planning red-team exercises: defining scope, rules of engagement, and safe failover procedures
- Employing adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and impact
Hardening and Mitigations
- Applying engineering controls like response throttles, capability gating, and sandboxing
- Implementing policy and orchestration controls, including approval flows, human-in-the-loop checks, and governance hooks
- Deploying model and prompt-level defenses via input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary releases, and progressive rollouts
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance among security, legal, product, and operations teams
Capstone: Red-Team / Blue-Team Simulation
- Carrying out a simulated red-team attack on a sandboxed agent environment
- Acting as the blue team to defend, detect, and remediate using established controls and telemetry
- Presenting findings, remediation strategies, and proposed policy updates
Summary and Next Steps
Requirements
- A strong 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 & access management (IAM) and secure system architecture
Target Audience
- Security engineers and red-team specialists
- AI operations and platform engineers
- Compliance officers and risk managers
- Engineering leads overseeing agent deployments
Testimonials (1)
inventory and identifying the different risk exposures within AI
