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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
- Applying governance frameworks for agentic systems (roles, responsibilities, approval gates)
- Designing policies: acceptable use, escalation rules, data handling, and auditability
- Addressing compliance considerations and collecting evidence for audits
Non-Human Identity & Authentication for Agents
- Architecting agent identities: service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing
- Managing identity lifecycle, rotation, delegation, and revocation strategies
Access Controls, Secrets, and Data Protection
- Utilizing fine-grained access control models and capability-based patterns for agents
- Managing secrets, 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
- Creating telemetry for agent behavior: intent tracing, command logs, and provenance
- Integrating with SIEM, defining alerting thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for agent-related incidents and containment
Red-Teaming Agentic Systems
- Structuring red-team exercises: scope, rules of engagement, and safe failover
- 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
- Applying engineering controls: response throttles, capability gating, and sandboxing
- Enforcing policy and orchestration controls: approval flows, human-in-the-loop, and governance hooks
- Implementing 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
- Establishing change control, testing pipelines, and pre-deploy safety checks
- Fostering cross-functional governance: security, legal, product, and ops playbooks
Capstone: Red-Team / Blue-Team Exercise
- Conducting a simulated red-team attack against a sandboxed agent environment
- Defending, detecting, and remediating as the blue team using controls and telemetry
- Presenting findings, remediation plans, and necessary policy updates
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Proficiency with AI/ML concepts and an understanding of large language model (LLM) behavior
- Practical experience in identity & access management (IAM) and secure system design
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
