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
- Identifying agentic threats: misuse, privilege escalation, data leakage, and supply-chain vulnerabilities
- Defining adversary profiles and attacker capabilities unique to autonomous agents
- Mapping critical assets, trust boundaries, and control points for agent operations
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems, including roles, responsibilities, and approval gates
- Designing comprehensive policies covering acceptable use, escalation rules, data handling, and auditability
- Addressing compliance requirements and strategies for evidence collection during 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 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 enforcing data minimization
- Safeguarding sensitive knowledge sources and PII from unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry to track agent behavior: intent tracing, command logging, and provenance
- Integrating SIEM tools, setting alerting thresholds, and ensuring forensic readiness
- 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 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
- Deploying engineering controls: response throttles, capability gating, and sandboxing
- Implementing 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 filtering
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary releases, and progressive rollouts for agents
- Enforcing change control, testing pipelines, and pre-deployment safety checks
- Fostering cross-functional governance: aligning 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 utilizing established controls and telemetry
- Presenting findings, outlining the remediation plan, and proposing policy updates
Summary and Next Steps
Requirements
- A strong foundation in security engineering, system administration, or cloud operations
- Proficiency with 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-teamers
- AI operations and platform engineers
- Compliance officers and risk managers
- Engineering leads overseeing agent deployments
Testimonials (1)
inventory and identifying the different risk exposures within AI
