Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations: Threat Models for Agentic AI
- Understanding agentic threats: misuse, escalation, data leakage, and supply-chain risks.
- Analyzing adversary profiles and attacker capabilities specific to autonomous agents.
- Mapping assets, trust boundaries, and critical control points for agents.
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates.
- Designing policies: acceptable use, escalation rules, data handling, and auditability.
- Addressing compliance considerations and evidence collection for audits.
Non-Human Identity & Authentication for Agents
- Designing identities for agents: utilizing service accounts, JWTs, and short-lived credentials.
- Implementing least-privilege access patterns and just-in-time credentialing.
- 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 practicing data minimization.
- Protecting sensitive knowledge sources and PII from unauthorized agent access.
Observability, Auditing, and Incident Response
- Designing telemetry for agent behavior: intent tracing, command logs, and provenance.
- Integrating SIEM systems, 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.
- Employing adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse.
- Conducting controlled attacks to measure exposure and impact.
Hardening and Mitigations
- Implementing engineering controls: response throttles, capability gating, and sandboxing.
- Utilizing policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks.
- Applying model and prompt-level defenses: input validation, canonicalization, and output filters.
Operationalizing Safe Agent Deployments
- Exploring deployment patterns: staging, canary releases, and progressive rollout for agents.
- Managing change control, testing pipelines, and pre-deploy safety checks.
- Establishing cross-functional governance: 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 controls and telemetry.
- Presenting findings, remediation plans, and policy updates.
Summary and Next Steps
Requirements
- Strong background in security engineering, system administration, or cloud operations.
- Familiarity with AI/ML concepts and the behavior of large language models (LLMs).
- Experience with identity and access management (IAM) and secure system design principles.
Target Audience
- Security engineers and red-team specialists.
- AI operations and platform engineers.
- Compliance officers and risk managers.
- Engineering leads responsible for agent deployments.
21 Hours
