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.
Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorizing agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities.
- Defining adversary profiles and attacker capabilities unique to autonomous agents.
- Mapping assets, trust boundaries, and critical control points for agent operations.
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 requirements and collecting evidence for audits.
Non-Human Identity & Authentication for Agents
- Creating identities for agents using service accounts, JWTs, and short-lived credentials.
- Implementing least-privilege access patterns and just-in-time credentialing.
- Managing the identity lifecycle, including rotation, delegation, and revocation strategies.
Access Controls, Secrets, and Data Protection
- Applying fine-grained access control models and capability-based patterns for agents.
- Handling secrets management, encryption in transit and at rest, and data minimization.
- Safeguarding 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 with SIEM, setting alerting thresholds, and preparing for forensics.
- Developing runbooks and playbooks for managing and containing agent-related incidents.
Red-Teaming Agentic Systems
- Planning red-team exercises: defining scope, rules of engagement, and safe failover procedures.
- Exploring adversarial techniques: 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: response throttles, capability gating, and sandboxing.
- Applying policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks.
- Utilizing model and prompt-level defenses: input validation, canonicalization, and output filtering.
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary, and progressive rollouts for agents.
- Managing change control, testing pipelines, and pre-deployment safety checks.
- Fostering cross-functional governance: playbooks for security, legal, product, and operations.
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 necessary policy updates.
Summary and Next Steps
Requirements
- A robust foundation in security engineering, system administration, or cloud operations.
- Proficiency with AI/ML concepts and an understanding of large language model (LLM) behavior.
- Practical experience with identity & access management (IAM) and secure system architecture.
Target Audience
- Security engineers and red-team specialists.
- AI operations and platform engineers.
- Compliance officers and risk managers.
- Engineering leads accountable for agent deployments.
Testimonials (1)
inventory and identifying the different risk exposures within AI
