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Duration 21 hours
Course Outline
Foundations: Threat Models for Agentic AI
- Categorising agentic threats: misuse, privilege escalation, data leakage, and supply-chain risks.
- Understanding adversary profiles and attacker capabilities specific to autonomous agents.
- Mapping assets, trust boundaries, and critical control points for agent operations.
Governance, Policy, and Risk Management
- Developing governance frameworks for agentic systems, including roles, responsibilities, and approval gates.
- Designing policies covering 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 using service accounts, JWTs, and short-lived credentials.
- Applying least-privilege access patterns and just-in-time credentialing.
- Managing identity lifecycle, 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, encryption in transit and at rest, and 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 SIEMs, defining alerting thresholds, and ensuring forensic readiness.
- Creating runbooks and playbooks for 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.
- Conducting controlled attacks to measure exposure and impact.
Hardening and Mitigations
- Engineering controls: response throttles, capability gating, and sandboxing.
- Policy and orchestration controls: approval flows, human-in-the-loop mechanisms, and governance hooks.
- Model and prompt-level defences: input validation, canonicalisation, and output filters.
Operationalizing Safe Agent Deployments
- Deployment patterns: staging, canary releases, and progressive rollouts for agents.
- Managing change control, testing pipelines, and pre-deploy safety checks.
- Cross-functional governance: integrating 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 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.
- Familiarity with AI/ML concepts and the behaviour of large language models (LLMs).
- Experience with identity and 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.
Testimonials (1)
inventory and identifying the different risk exposures within AI
