Recently I completed a deep digital forensics and data-correlation investigation involving a large dataset of emails, file metadata, CRM exports, and PST archives. Due to confidentiality and legal sensitivity, I cannot share real names, companies, or actual data — but I want to share the technical approach, methodology, and lessons learned from this project.
This case required combining multiple forensic sources into one consolidated analysis to identify inconsistencies, automation patterns, and timeline anomalies.
🧩 Scope of the Investigation
The project involved forensic analysis of:
- Email PST archives (thousands of messages)
- Autopsy extraction reports
- File metadata & EXIF data
- CRM exports (leads, deals, payouts, assignments)
- Document attachments (PDF, images, spreadsheets)
- Timestamp correlation across systems
- User activity logs & call logs
The goal was to determine:
- Whether records were created in real time
- Whether documents were reused or backfilled
- Whether multiple users were operating under one account
- Whether timeline events matched across systems
⚙️ Methodology Used
1. Email Forensic Analysis
- Parsed headers, subject lines, bodies, and attachments
- Checked repeated subjects and batch patterns
- Compared send/receive timestamps
- Identified automated vs manual email behavior
2. Metadata & Document Forensics
- Extracted PDF author / owner metadata
- Checked file creation vs modification times
- Identified template reuse patterns
- Verified scanner / device / software tags
3. EXIF & Image Analysis
- Checked device models
- Verified capture timestamps
- Looked for GPS / location mismatches
- Grouped files by hardware fingerprint
4. Timeline Correlation
Compared across:
- Email time
- File creation time
- CRM record time
- Funding / approval time
- User login time
This revealed cases where:
- Documents appeared after deals were completed
- Files were created in batches
- Emails did not match CRM events
5. Personnel & Access Pattern Analysis
- Checked overlap between users
- Found near-identical deal assignments
- Detected possible shared accounts / delegation
- Identified workflow anomalies
6. Excel + Autopsy + Custom Scripts
Used combination of:
- Autopsy forensic output
- Excel pivot tables & formulas
- CSV / XML exports
- Python scripts
- Manual validation
This allowed building a full forensic timeline model.
📊 Key Lessons from This Investigation
✔ Data from different systems rarely matches perfectly
✔ Metadata often reveals more than visible data
✔ Batch timestamps are strong forensic indicators
✔ Shared accounts leave detectable patterns
✔ Pivot tables are extremely powerful in forensic work
✔ Autopsy exports can be correlated with CRM data
✔ Timeline comparison is the most important step
🛠 Skills Used in This Project
- Digital Forensics
- Email PST Analysis
- Autopsy Forensic Tool
- Metadata / EXIF Analysis
- Excel Advanced Analysis
- Data Correlation
- Timeline Reconstruction
- Fraud / anomaly detection
- Python data scripts
- CRM data analysis
🔐 Note on Confidentiality
This post intentionally avoids:
❌ Client names ❌ Company names ❌ Real data ❌ Screenshots ❌ Case details
The purpose is only to share technical experience, not the case itself.
🚀 If you need similar work
I work on:
- Digital forensics
- Email analysis
- PST / Autopsy analysis
- Data anomaly detection
- CRM / log correlation
- Investigation support
- Expert technical reports
Available for consulting & forensic projects.