IT Security
AI for SOC Analysts
This practical course helps professionals master AI for SOC analysts, alert enrichment, investigation support, detection tuning, reporting, and safe use controls. The program connects key concepts, real use cases, risks, tools, and operational decisions so participants can apply the learning in their work environment. It can be tailored to the organization’s sector, internal systems, participant maturity, and performance objectives.
Objectives
- Understand the concepts, challenges, and use cases related to AI for SOC analysts, alert enrichment, investigation support, detection tuning, reporting, and safe use controls.
- Identify the data, systems, processes, and stakeholders required for effective implementation.
- Assess risks, limitations, governance requirements, and practical control points.
- Use methods, tools, and templates to structure analysis and decision-making.
- Translate learning into action plans, recommendations, and measurable improvement opportunities.
- Adapt the approach to the operating context, team maturity, and business objectives.
Target audience
- Cybersecurity and IT security professionals
- SOC analysts, infrastructure teams, and operations teams
- Risk, audit, compliance, and governance professionals
- System, network, cloud, and OT administrators
- IT managers responsible for security posture
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: AI Use Cases for SOC Analysts and Detection Teams
Foundation for AI Use Cases for SOC Analysts and Detection Teams: application, analysis, and review points linked to the module
Terminology and decisions in AI Use Cases for SOC Analysts and Detection Teams: application, analysis, and review points linked to the module
Inputs required for AI Use Cases for SOC Analysts and Detection Teams: application, analysis, and review points linked to the module
Typical mistakes around AI Use Cases for SOC Analysts and Detection Teams: applied exercise and practical decision from a realistic scenario
Module 2: Alert Summarization, Entity Enrichment, and Investigation Support
Current-state mapping for Alert Summarization, Entity Enrichment, and Investigation Support: application, analysis, and review points linked to the module
Examples and scenarios involving Alert Summarization, Entity Enrichment, and Investigation Support: application, analysis, and review points linked to the module
Diagnostic questions about Alert Summarization, Entity Enrichment, and Investigation Support: application, analysis, and review points linked to the module
Evidence produced through Alert Summarization, Entity Enrichment, and Investigation Support: applied exercise and practical decision from a realistic scenario
Module 3: Prompting for Triage, Timelines, Hypotheses, and Reports
Design considerations for Prompting for Triage, Timelines, Hypotheses, and Reports: application, analysis, and review points linked to the module
Roles and responsibilities in Prompting for Triage, Timelines, Hypotheses, and Reports: application, analysis, and review points linked to the module
Exceptions and constraints affecting Prompting for Triage, Timelines, Hypotheses, and Reports: application, analysis, and review points linked to the module
Quality checks for Prompting for Triage, Timelines, Hypotheses, and Reports: applied exercise and practical decision from a realistic scenario
Module 4: AI Risks: Hallucination, Data Leakage, Prompt Injection, and Bias
Operating model for AI Risks: Hallucination, Data Leakage, Prompt Injection, and Bias: application, analysis, and review points linked to the module
Tools and workflow steps in AI Risks: Hallucination, Data Leakage, Prompt Injection, and Bias: application, analysis, and review points linked to the module
Handoffs and approvals around AI Risks: Hallucination, Data Leakage, Prompt Injection, and Bias: application, analysis, and review points linked to the module
Escalation points in AI Risks: Hallucination, Data Leakage, Prompt Injection, and Bias: applied exercise and practical decision from a realistic scenario
Module 5: Detection Engineering with AI Assistance
Performance measures for Detection Engineering with AI Assistance: application, analysis, and review points linked to the module
Review routines after Detection Engineering with AI Assistance: application, analysis, and review points linked to the module
Improvement actions for Detection Engineering with AI Assistance: application, analysis, and review points linked to the module
Sustaining discipline around Detection Engineering with AI Assistance: applied exercise and practical decision from a realistic scenario
Module 6: Human Review, Approval, Evidence Quality, and Governance
Advanced scenarios in Human Review, Approval, Evidence Quality, and Governance: application, analysis, and review points linked to the module
Failure patterns seen in Human Review, Approval, Evidence Quality, and Governance: application, analysis, and review points linked to the module
Coordination challenges during Human Review, Approval, Evidence Quality, and Governance: application, analysis, and review points linked to the module
Recovery actions for Human Review, Approval, Evidence Quality, and Governance: applied exercise and practical decision from a realistic scenario
Module 7: SOC Productivity Metrics and Safe AI Adoption
Governance requirements for SOC Productivity Metrics and Safe AI Adoption: application, analysis, and review points linked to the module
Data quality checks in SOC Productivity Metrics and Safe AI Adoption: application, analysis, and review points linked to the module
Risk controls related to SOC Productivity Metrics and Safe AI Adoption: application, analysis, and review points linked to the module
Value measures for SOC Productivity Metrics and Safe AI Adoption: applied exercise and practical decision from a realistic scenario
Module 8: AI-Assisted SOC Investigation Lab
Implementation planning for AI-Assisted SOC Investigation Lab: application, analysis, and review points linked to the module
Readiness questions before AI-Assisted SOC Investigation Lab: application, analysis, and review points linked to the module
Pilot design for AI-Assisted SOC Investigation Lab: application, analysis, and review points linked to the module
Lessons learned after AI-Assisted SOC Investigation Lab: applied exercise and practical decision from a realistic scenario
Materials provided
- ○ Slides used during the sessions
- ○ Group activities and practical exercises
- ○ Worksheets, checklists, and templates
- ○ Case studies relevant to the course
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for technical queries and guidance
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy designs technical and professional programs around the client’s operating reality. The course can be adapted to sector requirements, internal systems, team capability, practical use cases, and the level of depth required by the audience.
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