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Duration 21 hours
Course Outline
Foundations: Threat Modelling for Agentic AI
- Categorizing agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities.
- Identifying adversary profiles and attacker capabilities specifically targeting autonomous agents.
- Mapping critical assets, trust boundaries, and key control points within agent systems.
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates.
- Designing policies for acceptable use, escalation protocols, data handling, and auditability.
- Navigating compliance requirements and collecting evidence for regulatory audits.
Non-Human Identity & Authentication for Agents
- Crafting agent identities using service accounts, JWTs, and short-lived credentials.
- Implementing least-privilege access patterns and just-in-time credential issuance.
- Managing identity lifecycle, rotation, delegation, and revocation strategies.
Access Controls, Secrets, and Data Protection
- Applying fine-grained access control models and capability-based patterns for agents.
- Managing secrets, ensuring encryption in transit and at rest, and practising data minimization.
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access.
Observability, Auditing, and Incident Response
- Building telemetry for agent behaviour, including intent tracing, command logging, and provenance tracking.
- Integrating with SIEM tools, defining alerting thresholds, and maintaining forensic readiness.
- Developing runbooks and playbooks for responding to and containing agent-related incidents.
Red-Teaming Agentic Systems
- Planning red-team exercises, defining scope, rules of engagement, and safe failover mechanisms.
- Utilizing adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse.
- Executing controlled attacks to measure system exposure and impact.
Hardening and Mitigations
- Applying engineering controls like response throttles, capability gating, and sandboxing.
- Implementing policy and orchestration controls, including approval flows, human-in-the-loop checks, and governance hooks.
- Deploying model and prompt-level defenses through input validation, canonicalization, and output filtering.
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary, and progressive rollouts for agents.
- Enforcing change control, testing pipelines, and pre-deployment safety checks.
- Fostering cross-functional governance through coordinated security, legal, product, and ops playbooks.
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 recommended policy updates.
Summary and Next Steps
Requirements
- Robust expertise in security engineering, system administration, or cloud operations.
- Proficiency in AI/ML concepts and an understanding of large language model (LLM) behaviors.
- Hands-on experience with identity & access management (IAM) and secure system architecture.
Target Audience
- Security engineers and professional red-teamers.
- AI operations specialists and platform engineers.
- Compliance officers and risk management professionals.
- Engineering leads overseeing agent deployments.
Testimonials (1)
inventory and identifying the different risk exposures within AI
