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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 risks
- Defining adversary profiles and attacker capabilities unique to autonomous agents
- Mapping critical assets, trust boundaries, and control points for agent environments
Governance, Policy, and Risk Management
- Implementing governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Crafting policies for acceptable use, escalation rules, data handling, and auditability
- Addressing compliance requirements and gathering evidence for audits
Non-Human Identity & Authentication for Agents
- Creating agent identities using service accounts, JWTs, and short-lived credentials
- Applying least-privilege access patterns and just-in-time credentialing
- Managing the identity lifecycle, including rotation, delegation, and revocation
Access Controls, Secrets, and Data Protection
- Utilizing 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
- Building telemetry for agent behavior, covering intent tracing, command logs, and provenance
- Integrating with SIEM, setting alerting thresholds, and maintaining forensic readiness
- Developing runbooks and playbooks for agent-related incidents and containment strategies
Red-Teaming Agentic Systems
- Planning red-team exercises, including scope, rules of engagement, and safe failover procedures
- Applying adversarial techniques such as 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 like response throttles, capability gating, and sandboxing
- Establishing policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks
- Deploying model and prompt-level defenses such as input validation, canonicalization, and output filters
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary releases, and progressive rollout for agents
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance through playbooks involving security, legal, product, and ops teams
Capstone: Red-Team / Blue-Team Exercise
- Executing a simulated red-team attack against a sandboxed agent environment
- Acting as the blue team to defend, detect, and remediate using established controls and telemetry
- Presenting findings, outlining a remediation plan, and proposing policy updates
Summary and Next Steps
Requirements
- Proficiency in security engineering, system administration, or cloud operations
- Understanding of AI/ML concepts and the behavioral characteristics of large language models (LLMs)
- Hands-on experience with 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
