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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorising agentic threats: misuse, escalation, data leakage, and supply-chain vulnerabilities
- Defining adversary profiles and attacker capabilities relevant to autonomous agents
- Mapping assets, trust boundaries, and critical control points specific to agents
Governance, Policy, and Risk Management
- Establishing governance frameworks for agentic systems (roles, responsibilities, and approval gates)
- Designing policies covering acceptable use, escalation rules, data handling, and auditability
- Addressing compliance requirements and gathering evidence for audits
Non-Human Identity and Authentication for Agents
- Designing agent identities: service accounts, JWTs, and short-lived credentials
- Implementing least-privilege access patterns and just-in-time credentialing
- Managing 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 practising data minimisation
- Safeguarding sensitive knowledge sources and PII from unauthorised agent access
Observability, Auditing, and Incident Response
- Designing telemetry for agent behaviour, including intent tracing, command logs, and provenance
- Integrating with SIEM, setting alerting thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for responding to and containing agent-related incidents
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 impact
Hardening and Mitigations
- Implementing engineering controls such as response throttling, capability gating, and sandboxing
- Applying policy and orchestration controls, including approval flows, human-in-the-loop mechanisms, and governance hooks
- Deploying model and prompt-level defences like input validation, canonicalisation, and output filtering
Operationalising 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
- Fostering cross-functional governance through playbooks for 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 as part of the blue team using established controls and telemetry
- Presenting findings, a remediation plan, and proposed policy updates
Summary and Next Steps
Requirements
- A robust foundation in security engineering, system administration, or cloud operations
- Working knowledge of AI/ML concepts and the behaviour of large language models (LLMs)
- Experience with identity and 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 responsible for deploying agents
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised 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*
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