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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorizing agentic threats: misuse, escalation, data leakage, and supply-chain risks.
- Defining adversary profiles and attacker capabilities specific to autonomous agents.
- Mapping assets, trust boundaries, and critical control points for agents.
Governance, Policy, and Risk Management
- Structuring governance frameworks for agentic systems (roles, responsibilities, and approval gates).
- Policy design covering acceptable use, escalation rules, data handling, and auditability.
- Addressing compliance requirements and collecting evidence for audits.
Non-Human Identity & Authentication for Agents
- Creating agent identities using service accounts, JWTs, and short-lived credentials.
- Implementing least-privilege access patterns and just-in-time credentialing.
- Managing identity lifecycle, 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, ensuring encryption-in-transit and at-rest, and enforcing data minimization.
- Safeguarding 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.
- Integrating with SIEM, defining alerting thresholds, and ensuring forensic readiness.
- Developing 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 mechanisms.
- 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: response throttles, capability gating, and sandboxing.
- Implementing policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks.
- Deploying model and prompt-level defenses: input validation, canonicalization, and output filters.
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary, and progressive rollout for agents.
- Enforcing change control, testing pipelines, and pre-deploy safety checks.
- Establishing cross-functional governance involving security, legal, product, and ops playbooks.
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 using established controls and telemetry.
- Presenting findings, the remediation plan, and proposed policy updates.
Summary and Next Steps
Requirements
- Robust foundation in security engineering, system administration, or cloud operations.
- Working knowledge of AI/ML concepts and large language model (LLM) behavior.
- Hands-on experience with identity & access management (IAM) and secure system design.
Target Audience
- Security engineers and red-teamers.
- 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
