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 for Cybersecurity Operations: SOC, Threat Detection and Automation 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: Practical AI/ML application mapped to SOC workflows and MITRE ATT&CK.
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
Where AI and ML add value in security operations
AI-assisted alert triage and enrichment
Anomaly detection and behavioural analytics (UEBA)
LLMs for investigation, summarisation and reporting
SOAR and AI-driven response automation
Detection engineering supported by AI
Risks: hallucination, data leakage and adversarial ML
Building a practical AI-in-SOC adoption roadmap
2 Days Agenda
Structured learning flow
Day 1
Day 1 — AI in Detection & Triage
Where AI and ML add value in security operations; AI-assisted alert triage and enrichment; Anomaly detection and behavioural analytics (UEBA); LLMs for investigation, summarisation and reporting.
Day 2
Day 2 — Automation, Risks & Roadmap
SOAR and AI-driven response automation; Detection engineering supported by AI; Risks: hallucination, data leakage and adversarial ML; Building a practical AI-in-SOC adoption roadmap.
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 where ai and ml add value in security operations.
- Confidently work with ai-assisted alert triage and enrichment.
- Confidently work with anomaly detection and behavioural analytics (ueba).
- Confidently work with llms for investigation, summarisation and reporting.
- Confidently work with soar and ai-driven response automation.