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Duration 21 hours
Course Outline
Foundations: Threat Modeling for Agentic AI
- Categorizing agentic threats: misuse, privilege escalation, data leakage, and supply-chain vulnerabilities
- Analyzing adversary profiles and attacker capabilities targeted at autonomous agents
- Mapping assets, trust boundaries, and key control points specific to agents
Governance, Policy, and Risk Management
- Implementing governance frameworks for agentic systems (roles, responsibilities, approval gates)
- Crafting policies for acceptable use, escalation protocols, data handling, and auditability
- Addressing compliance requirements and gathering evidence for audits
Non-Human Identity & Authentication for Agents
- Designing agent identities: service accounts, JWTs, and short-lived credentials
- Applying least-privilege access patterns and just-in-time credentialing
- Managing identity lifecycle, 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 enforcing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry for agent behavior: intent tracing, command logging, and provenance
- Integrating with SIEM, setting alerting thresholds, and preparing for forensics
- 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
- Applying adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and impact
Hardening and Mitigations
- Engineering controls: response throttles, capability gating, and sandboxing
- Policy and orchestration controls: approval workflows, human-in-the-loop mechanisms, and governance hooks
- Model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Deployment strategies: staging, canary releases, and progressive rollouts for agents
- Implementing change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance: playbooks for security, legal, product, and operations
Capstone: Red-Team / Blue-Team Simulation
- 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 strategies, and policy enhancements
Summary and Next Steps
Requirements
- Strong proficiency in security engineering, system administration, or cloud operations
- Working knowledge of AI/ML principles and the operational behavior of large language models (LLMs)
- Hands-on experience with identity & access management (IAM) and secure architecture design
Target Audience
- Security engineers and red-team specialists
- AI operations and platform engineers
- Compliance officers and risk management professionals
- Engineering leads overseeing agent deployment
Testimonials (1)
inventory and identifying the different risk exposures within AI
