Cloud Security & Threat Intelligence (AI-Driven)
Securing Cloud Environments & Interpreting Threats with AI
Provider
CloudSpace Academy
Duration
20-24 Weeks (Cohort-Based)
Format
Instructor-Led, Live Online + Labs
Level
Intermediate to Advanced
Capstone
Cloud security investigation with threat intelligence analysis
Prerequisites
- Basic cloud (AWS) knowledge
- Cybersecurity or IT background recommended
Course Overview
The Cloud Security & Threat Intelligence program prepares learners to secure modern cloud environments and interpret real-world threat activity using AI-assisted security workflows.
This program bridges cloud security engineering and cyber threat intelligence, teaching students how to monitor cloud environments, detect misconfigurations and suspicious activity, correlate security events with global threat intelligence, and communicate risk clearly to technical and business stakeholders.
Graduates gain hands-on cloud security experience, threat analysis skills, and portfolio-ready investigations aligned with real enterprise needs.
Who This Program Is For
- Cybersecurity analysts expanding into cloud security
- Cloud engineers adding security specialization
- SOC analysts moving into cloud-focused roles
- Threat intelligence or risk analysts
- Veterans and transitioning service members
Program Outcomes
- Secure AWS cloud environments using best practices
- Detect cloud misconfigurations and abuse patterns
- Analyze cloud logs and telemetry efficiently
- Apply threat intelligence to real cloud events
- Use AI to correlate signals and reduce noise
- Communicate cloud risk and threat context clearly
- Support security engineering and leadership decisions
Detailed Syllabus
Phase 1
Cloud Security & AI Foundations (Weeks 1-3)
Topics Covered
- Evolution of cloud security
- Shared responsibility model (AWS-focused)
- Cloud attack surfaces and abuse cases
- Introduction to threat intelligence concepts
- Role of AI in cloud security analysis
- Prompting fundamentals for security reasoning
Outcome
Students understand how cloud security and threat intelligence intersect and how AI supports both.
Phase 2
AWS Identity, Access & Misconfigurations (Weeks 4-6)
Topics Covered
- IAM risks and privilege escalation
- Identity abuse in cloud environments
- Common AWS misconfigurations
- Logging and visibility for IAM activity
- AI-assisted misconfiguration analysis
Outcome
Students can identify and assess identity-based cloud risks.
Phase 3
Cloud Logging, Monitoring & Telemetry (Weeks 7-9)
Topics Covered
- AWS logging services (CloudTrail, VPC Flow Logs, CloudWatch)
- Event correlation across cloud services
- Behavioral baselining
- AI-assisted anomaly detection in cloud logs
Outcome
Students can interpret cloud telemetry and surface suspicious behavior.
Phase 4
Threat Intelligence Fundamentals (Weeks 10-13)
Topics Covered
- Types of threat intelligence (strategic, tactical, operational)
- Threat actors and cloud-focused attack patterns
- MITRE ATT&CK (cloud perspective)
- Intelligence sources and reliability
- AI-assisted intelligence summarization
Outcome
Students can contextualize cloud security events using threat intelligence.
Phase 5
Correlation, Risk & Cloud Incident Analysis (Weeks 14-17)
Topics Covered
- Mapping cloud events to threat campaigns
- Risk scoring and prioritization
- Cloud incident investigation workflows
- AI-assisted correlation and reporting
- Communicating findings to engineers and leaders
Outcome
Students can connect cloud activity to real threats and assess business risk.
Phase 6
Cloud Security Strategy & Advisory (Weeks 18-20)
Topics Covered
- Cloud security posture management (concepts)
- Policy, governance, and guardrails
- Security recommendations and remediation planning
- Executive-level risk communication
- AI-assisted briefing creation
Outcome
Students can advise teams and leadership on cloud security posture and threats.
Phase 7
Capstone Project (Weeks 21-24)
Capstone Requirements
Simulated AWS cloud environment, detection of misconfigurations or suspicious activity, cloud log analysis and correlation, threat intelligence mapping, and a final security and risk advisory report.
Final Deliverables
- Investigation documentation
- Threat intelligence summary
- Risk assessment
- Portfolio-ready cloud security case study
AI-Driven Workflows Taught
Throughout the program, students learn how to:
- Correlate cloud events with threat intelligence
- Summarize large datasets efficiently
- Reduce false positives using AI reasoning
- Improve clarity in security reporting
- Support decision-making, not replace it
- Use AI as a decision support tool
Final Graduation Outcomes
Graduates leave with:
- Cloud-native security experience
- Threat intelligence analysis skills
- AI-enabled investigative workflows
- Real-world cloud security case studies
- Readiness for cloud security and intelligence roles