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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
Testimonials (1)
inventory and identifying the different risk exposures within AI