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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 vulnerabilities.
- Understanding adversary profiles and attacker capabilities tailored to autonomous agents.
- Mapping assets, trust boundaries, and critical control points specific to agents.
Governance, Policy, and Risk Management
- Implementing governance frameworks for agentic systems, including roles, responsibilities, and approval gates.
- Designing policies for acceptable use, escalation rules, data handling, and auditability.
- Addressing compliance considerations and gathering evidence for 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 strategies.
- Managing identity lifecycle, rotation, delegation, and revocation.
Access Controls, Secrets, and Data Protection
- Employing fine-grained access control models and capability-based patterns for agents.
- Handling secrets management, encryption in transit and at rest, and data minimization.
- Shielding 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 with defined scope, rules of engagement, and safe failover protocols.
- Applying 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
- Implementing engineering controls like response throttles, capability gating, and sandboxing.
- Using policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks.
- Applying model and prompt-level defenses via input validation, canonicalization, and output filters.
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary, and progressive rollouts for agents.
- Enforcing change control, testing pipelines, and pre-deployment safety checks.
- Establishing cross-functional governance playbooks for security, legal, product, and operations 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 using established controls and telemetry.
- Presenting findings, remediation plans, and updated policies.
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).
- Practical experience with 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
