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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorization of agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities
- Analysis of adversary profiles and attacker capabilities specific to autonomous agents
- Mapping of assets, trust boundaries, and critical control points for agent operations
Governance, Policy, and Risk Management
- Governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Policy development covering acceptable use, escalation protocols, data handling, and auditability
- Compliance requirements and strategies for evidence collection for audits
Non-Human Identity & Authentication for Agents
- Architecting agent identities: service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing
- Strategies for identity lifecycle management, including rotation, delegation, and revocation
Access Controls, Secrets, and Data Protection
- Granular access control models and capability-based patterns for agents
- Secrets management, encryption in transit and at rest, and data minimization practices
- Protection of 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
- SIEM integration, alerting threshold configuration, and forensic readiness
- Development of runbooks and playbooks for agent-related incident response and containment
Red-Teaming Agentic Systems
- Planning red-team exercises: defining scope, rules of engagement, and safe failover mechanisms
- Adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Execution of controlled attacks to measure exposure and potential impact
Hardening and Mitigations
- Engineering controls: response throttling, capability gating, and sandboxing
- Policy and orchestration controls: approval workflows, human-in-the-loop mechanisms, and governance hooks
- Model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Deployment strategies: staging, canary releases, and progressive rollout for agents
- Change management, testing pipelines, and pre-deployment safety checks
- Cross-functional governance: integrating security, legal, product, and operations playbooks
Capstone: Red-Team / Blue-Team Exercise
- Execution of a simulated red-team attack against a sandboxed agent environment
- Blue-team defense, detection, and remediation using established controls and telemetry
- Presentation of findings, remediation plans, and policy updates
Summary and Next Steps
Requirements
- A strong foundation 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
