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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Classification of agentic threats, including misuse, escalation, data leakage, and supply-chain risks.
- Analysis of adversary profiles and attacker capabilities specific to autonomous agents.
- Mapping of assets, trust boundaries, and critical control points within agent architectures.
Governance, Policy, and Risk Management
- Implementation of governance frameworks for agentic systems, defining roles, responsibilities, and approval gates.
- Policy design covering acceptable use, escalation rules, data handling, and auditability.
- Addressing compliance considerations and managing evidence collection for audits.
Non-Human Identity & Authentication for Agents
- Designing 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
- Implementing fine-grained access control models and capability-based patterns for agents.
- Overseeing secrets management, encryption-in-transit and at-rest, and data minimization practices.
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access.
Observability, Auditing, and Incident Response
- Developing telemetry for agent behavior, including intent tracing, command logs, and provenance tracking.
- Integrating with SIEM tools, setting alerting thresholds, and ensuring forensic readiness.
- Creating 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 protocols.
- Exploring adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse.
- Executing controlled attacks to measure exposure and assess impact.
Hardening and Mitigations
- Applying engineering controls like response throttles, capability gating, and sandboxing.
- Utilizing policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks.
- Implementing model and prompt-level defenses through input validation, canonicalization, and output filters.
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary, and progressive rollout for agents.
- Managing change control, testing pipelines, and pre-deploy safety checks.
- Facilitating cross-functional governance across security, legal, product, and ops playbooks.
Capstone: Red-Team / Blue-Team Exercise
- Executing a simulated red-team attack against a sandboxed agent environment.
- Performing defense, detection, and remediation as the blue team using available controls and telemetry.
- Presenting findings, remediation plans, and proposed policy updates.
Summary and Next Steps
Requirements
- A robust background in security engineering, system administration, or cloud operations.
- Proficiency with AI/ML concepts and an understanding of large language model (LLM) behavior.
- Practical experience in 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
