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Course Outline

Foundations: Threat Models for Agentic AI

  • Categories of agentic threats, including misuse, privilege escalation, data leakage, and supply-chain risks.
  • Adversary profiles and the specific capabilities of attackers targeting autonomous agents.
  • Mapping assets, defining trust boundaries, and identifying critical control points for agents.

Governance, Policy, and Risk Management

  • Establishing 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 collecting evidence for audits.

Non-Human Identity & Authentication for Agents

  • Creating identities for agents using service accounts, JWTs, and short-lived credentials.
  • Applying least-privilege access patterns and just-in-time credentialing.
  • 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 for agent behavior, including intent tracing, command logs, and provenance.
  • Integrating with SIEM, setting alerting thresholds, and ensuring forensic readiness.
  • Developing runbooks and playbooks for agent-related incidents and containment.

Red-Teaming Agentic Systems

  • Planning red-team exercises, defining scope, rules of engagement, and safe failover procedures.
  • Utilizing 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 like response throttles, capability gating, and sandboxing.
  • Implementing policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks.
  • Employing model and prompt-level defenses such as input validation, canonicalization, and output filters.

Operationalizing Safe Agent Deployments

  • Adopting deployment patterns such as staging, canary, and progressive rollout for agents.
  • Enforcing change control, testing pipelines, and pre-deploy safety checks.
  • Fostering cross-functional governance through 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, a remediation plan, and proposed 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).
  • Proficiency in identity & access management (IAM) and secure system design practices.

Target Audience

  • Security engineers and red-teamers.
  • AI operations and platform engineers.
  • Compliance officers and risk managers.
  • Engineering leads overseeing agent deployments.
 21 Hours

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