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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categories of agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities
- Adversary profiles and attacker capabilities unique to autonomous agents
- Mapping assets, trust boundaries, and critical control points for agents
Governance, Policy, and Risk Management
- Governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Policy design covering acceptable use, escalation rules, data handling, and auditability
- Compliance requirements and methods for evidence collection 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
- Strategies for identity lifecycle management, rotation, delegation, and revocation
Access Controls, Secrets, and Data Protection
- Fine-grained access control models and capability-based patterns for agents
- Secrets management, encryption in transit and at rest, and data minimization practices
- Safeguarding 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
- SIEM integration, setting alerting thresholds, and maintaining forensic readiness
- Creating runbooks and playbooks for managing agent-related incidents and containment
Red-Teaming Agentic Systems
- Planning red-team exercises, defining scope, rules of engagement, and safe failover procedures
- Adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks and assessing exposure and impact
Hardening and Mitigations
- Engineering controls including response throttles, capability gating, and sandboxing
- Policy and orchestration controls such as approval flows, human-in-the-loop mechanisms, and governance hooks
- Model and prompt-level defenses, covering input validation, canonicalization, and output filters
Operationalizing Safe Agent Deployments
- Deployment patterns like staging, canary releases, and progressive rollouts for agents
- Change control, testing pipelines, and pre-deployment safety checks
- Cross-functional governance involving security, legal, product, and operations playbooks
Capstone: Red-Team / Blue-Team Exercise
- Conduct a simulated red-team attack against a sandboxed agent environment
- Defend, detect, and remediate as the blue team by leveraging controls and telemetry
- Present findings, the remediation plan, 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 the behavioral nuances of large language models (LLMs)
- Hands-on experience with Identity & Access Management (IAM) and secure system design
Target Audience
- Security engineers and red-teamers
- 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
