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 risks
- Defining adversary profiles and attacker capabilities specific to autonomous agents
- Mapping assets, trust boundaries, and critical control points for agents
Governance, Policy, and Risk Management
- Applying governance frameworks for agentic systems (roles, responsibilities, approval gates)
- Designing policies: acceptable use, escalation rules, data handling, and auditability
- Addressing compliance considerations and collecting evidence for audits
Non-Human Identity & Authentication for Agents
- Creating identities for agents: 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
- Utilizing fine-grained access control models and capability-based patterns for agents
- Managing secrets, encryption-in-transit and at-rest, and data minimization
- Securing 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 ensuring forensic readiness
- Developing runbooks and playbooks for agent-related incidents and containment
Red-Teaming Agentic Systems
- Planning red-team exercises: scope, rules of engagement, and safe failover
- Employing adversarial techniques: prompt injection, tool misuse, chain-of-thought manipulation, and API abuse
- Executing controlled attacks to measure exposure and impact
Hardening and Mitigations
- Applying engineering controls: response throttles, capability gating, and sandboxing
- Implementing policy and orchestration controls: approval flows, human-in-the-loop, and governance hooks
- Deploying model and prompt-level defenses: input validation, canonicalization, and output filters
Operationalizing Safe Agent Deployments
- Adopting deployment patterns: staging, canary, and progressive rollout for agents
- Enforcing change control, testing pipelines, and pre-deploy safety checks
- Fostering 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, the remediation plan, and policy updates
Summary and Next Steps
Requirements
- A solid foundation in security engineering, system administration, or cloud operations
- Proficiency with AI/ML concepts and the behavior of large language models (LLMs)
- 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 responsible for agent deployments
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (1)
inventory and identifying the different risk exposures within AI
