Cloud forensics Tuning and Optimization: Operator Edition
RCCE students will learn how to collect, preserve, and analyze digital evidence in cloud environments across AWS, Azure, and GCP. RCCE students will learn to acquire cloud-native logs including CloudTrail, Activity Log, and Audit Logs, preserve volatile cloud resources before termination, reconstruct attacker activity across cloud services, analyze IAM permission changes, investigate unauthorized resource provisioning, and build forensic timelines from distributed cloud telemetry. The course covers legal considerations for cloud evidence, chain of custody in shared responsibility models, and forensic imaging of cloud-based virtual machines. This optimization course focuses on maximizing effectiveness and efficiency in production security operations. At an expert level, RCCE students will learn to reduce noise, improve signal quality, tune configurations for optimal performance, and measure operational improvements. Students gain the operational maturity to transform good security programs into exceptional ones.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing Cloud forensics Tuning and Optimization: Operator Edition
- Execute hands-on tasks for cloud forensics
- Explain Course Overview & Learning Objectives fundamentals
- Execute hands-on tasks for cloud evidence collection — covering Collect and preserve digital, Forensic Analysis, Reconstruct attacker activity paths.
- Execute hands-on tasks for forensic analysis — covering Reconstruct attacker activity paths.
- Execute hands-on tasks for tuning & optimization — covering Reduce noise, improve signal.
- Execute hands-on tasks for cloud forensics fundamentals
- Execute hands-on tasks for traditional forensics — covering Physical disk imaging.
- Design a scalable privilege management architecture with policy and enforcement, including Customer: OS logs, app data,, PaaS (Lambda/Functions), and Customer: function logs, configs.
- Execute hands-on tasks for iaas (ec2/vm) — covering Customer: OS logs, app data,.
- Execute hands-on tasks for paas (lambda/functions) — covering Customer: function logs, configs.
- Execute hands-on tasks for saas (o365/workspace) — covering Customer: audit logs, user activity.
- Execute hands-on tasks for cloud evidence types & digital artifacts
| Module 01 | Cloud Forensics |
| Module 02 | Course Overview & Learning Objectives |
| Module 03 | Cloud Evidence Collection |
| Module 04 | Forensic Analysis |
| Module 05 | Tuning & Optimization |
| Module 06 | Cloud Forensics Fundamentals |
| Module 07 | Traditional Forensics |
| Module 08 | Shared Responsibility Model for Forensics |
| Module 09 | IaaS (EC2/VM) |
| Module 10 | PaaS (Lambda/Functions) |
| Module 11 | SaaS (O365/Workspace) |
| Module 12 | Cloud Evidence Types & Digital Artifacts |
| Module 13 | Control Plane Logs |
| Module 14 | Data Plane Logs |
All hands-on labs run on Rocheston Rose X OS. Students practice cloud forensics tuning and optimization: operator edition by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Execute hands-on tasks for cloud forensics
- Lab 2: Explain Course Overview & Learning Objectives fundamentals
- Lab 3: Execute hands-on tasks for cloud evidence collection
- Lab 4: Execute hands-on tasks for forensic analysis
- Lab 5: Execute hands-on tasks for tuning & optimization
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Cloud forensics Tuning and Optimization: Operator Edition, verifiable through the Rocheston certification portal.
- Full access to all course materials and slide decks
- Hands-on lab access on Rocheston Rose X OS environment
- Access to Rocheston CyberNotes
- Access to Rocheston Zelfire — EDR/XDR SIEM platform
- Access to Rocheston Raven — online cyber range exercise platform
- Access to Rocheston Vulnerability Vines AI