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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
- Profiling adversaries and assessing attacker capabilities specific to autonomous agents
- Mapping assets, defining trust boundaries, and identifying critical control points for agents
Governance, Policy, and Risk Management
- Applying governance frameworks to agentic systems (defining roles, responsibilities, and approval gates)
- Formulating policies: acceptable use, escalation protocols, data handling, and auditability standards
- Addressing compliance requirements and gathering evidence for audits
Non-Human Identity & Authentication for Agents
- Constructing agent identities: utilizing service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing mechanisms
- Managing identity lifecycles, 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, enforcing 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: intent tracing, command logging, and provenance tracking
- Integrating with SIEM, setting alerting thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for managing agent-related incidents and containment
Red-Teaming Agentic Systems
- Planning red-team exercises: defining scope, establishing rules of engagement, and ensuring safe failover
- Employing adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and assess impact
Hardening and Mitigations
- Implementing engineering controls: response throttling, capability gating, and sandboxing
- Establishing policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks
- Applying model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary releases, and progressive rollouts for agents
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance: aligning security, legal, product, and operations playbooks
Capstone: Red-Team / Blue-Team Exercise
- Executing a simulated red-team attack against a sandboxed agent environment
- Acting as the blue team to defend, detect, and remediate using established controls and telemetry
- Presenting findings, remediation strategies, and policy refinements
Summary and Next Steps
Requirements
- Robust expertise in security engineering, system administration, or cloud operations
- Working knowledge of AI/ML concepts and the behavior of large language models (LLMs)
- Hands-on experience with identity & access management (IAM) and secure system design
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
