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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorization of agentic threats: misuse, privilege escalation, data leakage, and supply-chain vulnerabilities
- Adversary profiles and attacker capabilities relevant to autonomous agents
- Identifying assets, trust boundaries, and critical control points for agent-based systems
Governance, Policy, and Risk Management
- Governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Policy development: acceptable use, escalation rules, data handling, and auditability requirements
- Compliance considerations and strategies for evidence collection during audits
Non-Human Identity & Authentication for Agents
- Designing agent identities: service accounts, JWTs, and short-lived credentials
- Least-privilege access patterns and just-in-time credentialing strategies
- Managing identity lifecycle, 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
- Building telemetry for agent behavior: intent tracing, command logging, and data provenance
- Integration with SIEM tools, setting alerting thresholds, and ensuring forensic readiness
- 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
- Adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks and assessing exposure and impact
Hardening and Mitigations
- Engineering controls: response throttling, capability gating, and sandboxing
- Policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks
- Model and prompt-level defenses: input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Deployment patterns: staging, canary releases, and progressive rollout for agents
- Change management, testing pipelines, and pre-deployment safety checks
- Cross-functional governance: coordination between security, legal, product, and operations teams
Capstone: Red-Team / Blue-Team Exercise
- Conducting a simulated red-team attack against a sandboxed agent environment
- Acting as the blue team to defend, detect, and remediate using controls and telemetry
- Presenting findings, remediation plans, and necessary 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
- Proficiency in identity & access management (IAM) and secure system design
Target 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
