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, privilege escalation, data leakage, and supply-chain risks
- Identifying adversary profiles and attacker capabilities specific to autonomous agents
- Mapping assets, trust boundaries, and critical control points within agent architectures
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Designing policies for acceptable use, escalation rules, data handling, and auditability
- Addressing compliance requirements and collecting evidence for audit purposes
Non-Human Identity & Authentication for Agents
- Creating agent identities 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
- Managing secrets, ensuring encryption in transit and at rest, and practicing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry for agent behavior, including intent tracing, command logs, and provenance
- Integrating with SIEMs, setting alerting thresholds, and preparing for forensics
- Developing runbooks and playbooks for managing 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 such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and potential impact
Hardening and Mitigations
- Implementing engineering controls like response throttles, capability gating, and sandboxing
- Utilizing policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks
- Applying model and prompt-level defenses such as input validation, canonicalization, and output filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns such as staging, canary releases, and progressive rollouts for agents
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Establishing cross-functional governance involving security, legal, product, and operations teams
Capstone: Red-Team / Blue-Team Exercise
- Executing a simulated red-team attack against a sandboxed agent environment
- Defending, detecting, and remediating threats as the blue team using established controls and telemetry
- Presenting findings, remediation plans, and necessary policy updates
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Proficiency in AI/ML concepts and an understanding of large language model (LLM) behavior
- Hands-on experience with 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 management professionals
- Engineering leads overseeing agent deployments
Testimonials (1)
inventory and identifying the different risk exposures within AI
