Analyzing Windows Prefetch with Python

Overview

Windows Prefetch files (.pf) record application execution data including executable names, run counts, timestamps, loaded DLLs, and accessed directories. This guide covers parsing Prefetch files using the windowsprefetch Python library to reconstruct execution timelines, detect renamed or masquerading binaries by comparing executable names with loaded resources, and identifying suspicious programs that may indicate malware execution or lateral movement.

Prerequisites

• Python 3.9+ with windowsprefetch library (pip install windowsprefetch)
• Windows Prefetch files from C:\Windows\Prefetch\ (versions 17-30 supported)
• Understanding of Windows Prefetch file naming conventions (EXECUTABLE-HASH.pf)

Steps

Step 1: Collect Prefetch Files

Gather .pf files from target system's C:\Windows\Prefetch\ directory.

Step 2: Parse Execution History

Extract executable name, run count, last execution timestamps, and volume information.

Step 3: Detect Suspicious Execution

Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.

Step 4: Build Execution Timeline

Reconstruct chronological execution timeline from all Prefetch files.

Expected Output

JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC7.3 (Incident Identification), CC7.4 (Incident Response)
• ISO 27001: A.16.1 (Security Incident Management), A.12.4 (Logging)
• NIST 800-53: AU-6 (Audit Review), IR-4 (Incident Handling), AU-9 (Audit Protection)
• NIST CSF: RS.AN (Analysis), RS.RP (Response Planning)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add analyzing-windows-prefetch-with-python

# Or load dynamically via MCP
grc.load_skill("analyzing-windows-prefetch-with-python")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.