Course Objective
What this programme helps you achieve
A hands-on 2 days programme that builds practical, job-ready capability in this area. Delivered through guided walkthroughs, worked examples and reusable checklists you can apply immediately.
UnderstandBuild a clear, structured understanding of the core concepts, terminology and drivers behind this domain.
ApplyTranslate the framework into practical actions, controls and working methods you can use on real engagements.
AssessEvaluate current-state maturity, identify gaps and prioritise the improvements that reduce the most risk.
DeliverProduce the artefacts, evidence and reporting expected of a competent practitioner in this area.
Market Need
Why this matters now
AI Security for Developers and Secure AI Application Development addresses a capability that organisations increasingly need but often lack in a structured, defensible form. Teams frequently rely on ad-hoc knowledge, scattered documents or vendor claims instead of a repeatable method.
This programme closes that gap. It gives participants a shared vocabulary, a practical method and a set of reusable artefacts so the organisation can operate this domain consistently rather than reactively.
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Framework alignment: OWASP LLM Top 10 and secure development best practices.
What makes this programme useful
01
Practical Focus
Every module is grounded in real scenarios, walkthroughs and reusable checklists rather than theory alone.
02
Career Relevant
Skills map directly to in-demand roles and to how organisations actually operate this domain.
03
Structured Path
A logical progression from fundamentals to applied practice, with a clear takeaway toolkit.
Key Learning Areas
What participants will learn
Secure AI/LLM application architecture
Threat modelling AI-enabled applications
Securing prompts, context and system instructions
Input validation, output encoding and content filtering
Securing model APIs, keys and inference endpoints
RAG data security and access control
Guardrails, rate limiting and abuse prevention
Testing, logging and monitoring AI applications
2 Days Agenda
Structured learning flow
Day 1
Day 1 — Secure AI Architecture & Threat Modelling
Secure AI/LLM application architecture; Threat modelling AI-enabled applications; Securing prompts, context and system instructions; Input validation, output encoding and content filtering.
Day 2
Day 2 — Controls, Guardrails & Testing
Securing model APIs, keys and inference endpoints; RAG data security and access control; Guardrails, rate limiting and abuse prevention; Testing, logging and monitoring AI applications.
Audience & Entry Level
Who should attend?
This programme is designed for professionals who work with, or are moving into, this area of cybersecurity, governance or technology.
AI Governance LeadRisk & ComplianceData Science / ML TeamSecurity ArchitectProduct & EngineeringCISO / CTOIT AuditorLegal & Privacy
Recommended prerequisite
A general awareness of information security or IT concepts is helpful. No specific certification is required to attend.
Learning Outcome
What participants should be able to do after the course
- Confidently work with secure ai/llm application architecture.
- Confidently work with threat modelling ai-enabled applications.
- Confidently work with securing prompts, context and system instructions.
- Confidently work with input validation, output encoding and content filtering.
- Confidently work with securing model apis, keys and inference endpoints.