AI-Driven Cybersecurity Analyst
Threat Detection, Cloud Security & Incident Response with AI
Provider
CloudSpace Academy
Duration
20-24 Weeks (Cohort-Based)
Format
Instructor-Led, Live Online + Labs
Level
Intermediate (Career Switchers & Professionals)
Capstone
End-to-end incident investigation and response project
Prerequisites
Basic IT, networking, or systems knowledge recommended
Course Overview
The AI-Driven Cybersecurity Analyst program prepares learners to detect, analyze, and respond to security threats in modern, cloud-based environments using AI-assisted workflows.
Rather than training students to manually chase alerts, this program teaches how security analysts work with AI tools to triage incidents, analyze logs, investigate threats, and respond effectively, just like real SOC teams.
Graduates leave with hands-on security experience, AI-enabled investigation skills, and portfolio-ready incident response projects.
Who This Program Is For
- IT professionals moving into cybersecurity
- SOC analysts seeking modern, AI-enabled skills
- Network or system administrators transitioning to security
- Cloud professionals expanding into security
- Veterans and transitioning service members
Program Outcomes
- Detect and investigate security incidents using AI-assisted analysis
- Analyze logs, alerts, and telemetry efficiently
- Respond to threats using structured incident response workflows
- Secure cloud environments and identities
- Apply threat intelligence and risk context
- Communicate findings clearly to technical and non-technical stakeholders
- Operate confidently in a modern SOC environment
Detailed Syllabus
Phase 1
Cybersecurity & AI Foundations (Weeks 1-3)
Topics Covered
- Modern cybersecurity roles (pre-AI vs AI-driven SOC)
- Threat landscape overview
- Security principles: CIA triad, risk, attack surfaces
- Introduction to AI in cybersecurity
- Prompting fundamentals for security analysis
Outcome
Students understand how AI supports modern security operations and investigations.
Phase 2
Networking, Logs & Telemetry (Weeks 4-6)
Topics Covered
- Network fundamentals for security analysts
- Common attack vectors and indicators
- Log sources: network, system, application, cloud
- SIEM fundamentals
- AI-assisted log correlation and anomaly detection
Outcome
Students can interpret logs and use AI to surface meaningful security signals.
Phase 3
Threat Detection & Alert Triage (Weeks 7-9)
Topics Covered
- SOC workflows and alert pipelines
- False positives vs true incidents
- Threat modeling basics
- AI-assisted alert prioritization
- Case management and escalation
Outcome
Students can triage alerts efficiently and focus on high-risk incidents.
Phase 4
Incident Response & Investigation (Weeks 10-13)
Topics Covered
- Incident response lifecycle
- Investigation techniques
- Root cause analysis
- Evidence collection and documentation
- AI-assisted investigation workflows
Outcome
Students can investigate incidents and document findings professionally.
Phase 5
Cloud Security & Identity (Weeks 14-17)
Topics Covered
- Cloud security fundamentals (AWS-focused)
- Identity and access management risks
- Cloud logging and monitoring
- Misconfigurations and common cloud threats
- AI-assisted cloud security analysis
Outcome
Students can analyze and secure cloud-based environments.
Phase 6
Threat Intelligence, Risk & Communication (Weeks 18-20)
Topics Covered
- Threat intelligence sources
- Mapping threats to business risk
- Security reporting and communication
- Writing incident summaries and recommendations
- AI-assisted report generation
Outcome
Students can translate technical findings into actionable insights.
Phase 7
Capstone Project (Weeks 21-24)
Capstone Requirements
Simulated security incident scenario, alert triage and investigation, log analysis and threat identification, incident response actions, and a final report with AI-assisted analysis.
Final Deliverables
- Incident timeline
- Investigation notes
- Response documentation
- Portfolio-ready case study
AI-Driven Workflows Taught
Throughout the program, students learn how to:
- Use AI to analyze logs and alerts
- Summarize incidents efficiently
- Identify patterns across large datasets
- Support decision-making, not replace it
- Improve documentation and reporting
- Use AI as a force multiplier
Final Graduation Outcomes
Graduates leave with:
- Hands-on SOC-style experience
- AI-enabled investigation workflows
- Incident response portfolio projects
- Confidence to operate in modern security teams
- Readiness for cybersecurity analyst and SOC roles