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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Key agentic threats, including misuse, escalation, data leakage, and supply-chain risks
- Specific adversary profiles and attacker capabilities relevant to autonomous agents
- Mapping assets, trust boundaries, and critical control points within agent architectures
Governance, Policy, and Risk Management
- Governance frameworks for agentic systems, covering roles, responsibilities, and approval gates
- Policy design encompassing acceptable use, escalation rules, data handling, and auditability
- Compliance considerations and strategies for evidence collection during audits
Non-Human Identity & Authentication for Agents
- Creating agent identities using service accounts, JWTs, and short-lived credentials
- Applying least-privilege access patterns and just-in-time credentialing methods
- Managing the identity lifecycle, including rotation, delegation, and revocation strategies
Access Controls, Secrets, and Data Protection
- Implementing 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 to track agent behavior, including intent tracing, command logs, and provenance
- Integrating with SIEM tools, setting alerting thresholds, and preparing for forensics
- Developing runbooks and playbooks for managing and containing agent-related incidents
Red-Teaming Agentic Systems
- Planning red-team exercises, defining scope, rules of engagement, and safe failover mechanisms
- Applying adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure system exposure and potential impact
Hardening and Mitigations
- Implementing engineering controls like response throttles, capability gating, and sandboxing
- Using policy and orchestration controls, including approval flows, human-in-the-loop processes, and governance hooks
- Deploying model and prompt-level defenses through input validation, canonicalization, and output filters
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary releases, and progressive rollouts for agents
- Managing change control, testing pipelines, and pre-deployment safety checks
- Coordinating cross-functional governance involving 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 by leveraging controls and telemetry
- Presenting findings, remediation plans, and necessary policy updates
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Knowledge of AI/ML concepts and the behavior of large language models (LLMs)
- Experience with identity & access management (IAM) and secure system design principles
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
