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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorizing agentic threats: misuse, privilege escalation, data leakage, and supply-chain vulnerabilities
- Analyzing adversary profiles and attacker capabilities relevant to autonomous agents
- Mapping critical assets, trust boundaries, and control points for agent operations
Governance, Policy, and Risk Management
- Implementing governance frameworks for agentic systems (roles, responsibilities, and approval gates)
- Designing policies for acceptable use, escalation protocols, data handling, and auditability
- Addressing compliance requirements and collecting evidence for audits
Non-Human Identity & Authentication for Agents
- Defining agent identities: 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
- Deploying 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
- Building telemetry for agent behavior: intent tracing, command logging, and provenance tracking
- Integrating with SIEM, defining alerting thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for managing agent-related incidents and containment
Red-Teaming Agentic Systems
- Structuring red-team exercises: scope definition, rules of engagement, and safe failover mechanisms
- Employing adversarial techniques: 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: response throttling, capability gating, and sandboxing
- Applying policy and orchestration controls: approval workflows, human-in-the-loop mechanisms, and governance hooks
- Deploying model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary releases, and progressive rollouts for agents
- Establishing change control, testing pipelines, and pre-deployment safety checks
- Facilitating cross-functional governance: security, legal, product, and ops playbooks
Capstone: Red-Team / Blue-Team Exercise
- Conducting a simulated red-team attack on a sandboxed agent environment
- Defending, detecting, and remediating as the blue team using established controls and telemetry
- Presenting findings, remediation strategies, and policy adjustments
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Proficiency in AI/ML concepts and an understanding of large language model (LLM) behavior
- Hands-on 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
