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Prompts Data Staging Exfiltration Threat Hunter

analyst security skill risk: medium

Data Staging Exfiltration Threat Hunter

The prompt outlines steps to detect data staging before exfiltration by monitoring archive tool execution, staging directories, file consolidation patterns, and sensitive path acce…

SKILL 4 files · 2 folders

SKILL.md
---
name: hunting-for-data-staging-before-exfiltration
description: "Detect data staging activity before exfiltration by monitoring for archive creation with 7-Zip/RAR, unusual temp"
---
# Hunting for Data Staging Before Exfiltration

## Overview

Before exfiltrating data, adversaries typically stage collected files in a central location (MITRE ATT&CK T1074). This involves creating archives with tools like 7-Zip, RAR, or tar, consolidating files from multiple directories, and using temporary or hidden staging directories. This skill detects staging behavior by analyzing process creation logs for archiver activity, monitoring file system events in common staging paths, and identifying anomalous file consolidation patterns.


## When to Use

- When investigating security incidents that require hunting for data staging before exfiltration
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- EDR or Sysmon telemetry with process creation and file system events
- Windows Event Logs (Event ID 4688) or Sysmon Event ID 1, 11
- Python 3.8+ with standard library
- Access to process creation logs in JSON/CSV format

## Steps

1. **Detect Archive Tool Execution** — Monitor for 7z.exe, rar.exe, tar, zip, and WinRAR process creation with compression arguments
2. **Identify Staging Directories** — Flag file writes to common staging locations (Recycle Bin, %TEMP%, ProgramData, hidden directories)
3. **Detect Large File Consolidation** — Identify patterns of multiple file reads followed by writes to a single directory
4. **Monitor Sensitive Path Access** — Track bulk reads from document directories, database paths, and network shares
5. **Analyze Archive Metadata** — Extract and analyze archive file sizes, creation times, and source paths
6. **Score Staging Risk** — Apply heuristic scoring based on archive size, source diversity, staging path suspicion, and timing
7. **Generate Hunt Report** — Produce a structured report with staging event timeline and MITRE ATT&CK mapping

## Expected Output

- JSON report of detected staging events with risk scores
- Archive creation timeline with source file analysis
- MITRE ATT&CK mapping (T1074.001, T1074.002, T1560)
- Staging directory heat map showing suspicious write activity

REQUIRED CONTEXT

  • EDR or Sysmon telemetry with process creation and file system events
  • Windows Event Logs (Event ID 4688) or Sysmon Event ID 1, 11
  • process creation logs in JSON/CSV format

EXPECTED OUTPUT

Format
structured_report
Schema
json_report_with_sections · JSON report of detected staging events with risk scores, Archive creation timeline with source file analysis, MITRE ATT&CK mapping (T1074.001, T1074.002, T1560), Staging directory heat map showing suspicious write activity
Constraints
  • JSON report of detected staging events with risk scores
  • Archive creation timeline with source file analysis
  • MITRE ATT&CK mapping (T1074.001, T1074.002, T1560)
  • Staging directory heat map showing suspicious write activity

SUCCESS CRITERIA

  • Detect archive tool execution
  • Identify staging directories
  • Score staging risk
  • Generate hunt report

CAVEATS

Dependencies
  • EDR or Sysmon telemetry with process creation and file system events
  • Windows Event Logs (Event ID 4688) or Sysmon Event ID 1, 11
  • Python 3.8+ with standard library
  • Access to process creation logs in JSON/CSV format
Missing context
  • Exact log formats or sample data schemas
  • Thresholds or heuristics for risk scoring
Ambiguities
  • Description field is truncated ('unusual temp')

QUALITY

OVERALL
0.74
CLARITY
0.88
SPECIFICITY
0.62
REUSABILITY
0.72
COMPLETENESS
0.78

IMPROVEMENT SUGGESTIONS

  • Add concrete query or code examples for each detection step
  • Define input data schema and expected fields for the JSON/CSV logs

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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