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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 assets, trust boundaries, and critical control points for agent operations
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Crafting policies for acceptable use, escalation protocols, data handling, and auditability
- Addressing compliance requirements and collecting evidence for audits
Non-Human Identity & Authentication for Agents
- Designing agent identities using service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing
- Managing identity lifecycles, rotation, delegation, and revocation
Access Controls, Secrets, and Data Protection
- Applying fine-grained access control models and capability-based patterns for agents
- Managing secrets, enforcing 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, including intent tracing, command logs, and provenance
- Integrating with SIEM, setting alerting thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for responding to and containing agent-related incidents
Red-Teaming Agentic Systems
- Structuring red-team exercises: defining scope, rules of engagement, and safe failover mechanisms
- Exploring adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to assess exposure and impact
Hardening and Mitigations
- Implementing engineering controls such as response throttling, capability gating, and sandboxing
- Utilizing policy and orchestration controls, including approval flows, human-in-the-loop checks, and governance hooks
- Applying model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns like staging, canary releases, and progressive rollouts for agents
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Establishing cross-functional governance across security, legal, product, and ops 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 proposed policy updates
Summary and Next Steps
Requirements
- Strong foundation in security engineering, system administration, or cloud operations
- Understanding of AI/ML concepts and large language model (LLM) behavior
- Experience with identity and access management (IAM) and secure system design
Target Audience
- Security engineers and red-team practitioners
- AI operations and platform engineers
- Compliance officers and risk managers
- Engineering leaders overseeing agent deployments
Testimonials (1)
inventory and identifying the different risk exposures within AI
