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
- Analyzing adversary profiles and attacker capabilities specific to autonomous agents
- Mapping key assets, trust boundaries, and critical control points associated with agents
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Developing policies that address acceptable use, escalation rules, data handling, and auditability
- Navigating compliance requirements and collecting evidence for audits
Non-Human Identity & Authentication for Agents
- Defining agent identities through service accounts, JWTs, and short-lived credentials
- Applying 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
- Implementing fine-grained access control models and capability-based patterns for agents
- Managing secrets, ensuring encryption-in-transit and at-rest, and enforcing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry for agent behavior, encompassing intent tracing, command logs, and provenance
- Integrating with SIEM, setting alerting thresholds, and maintaining forensic readiness
- Creating 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 mechanisms
- Employing adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and assess impact
Hardening and Mitigations
- Applying 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
- Implementing model and prompt-level defenses through input validation, canonicalization, and output filters
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
- Facilitating cross-functional governance among security, legal, product, and ops teams through dedicated playbooks
Capstone: Red-Team / Blue-Team Exercise
- Conducting a simulated red-team attack against a sandboxed agent environment
- Assuming the blue team role to defend, detect, and remediate using established controls and telemetry
- Presentation of findings, a remediation plan, and necessary policy updates
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Working knowledge of AI/ML concepts and the behavior of large language models (LLMs)
- Practical experience with identity & access management (IAM) and secure system design
Audience
- Security engineers and red-teamers
- 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
