/analyzing-outlook-pst-for-email-forensics

name: analyzing-outlook-pst-for-email-forensics
description: Analyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments,
  deleted items, and metadata using libpff, pst-utils, and forensic email analysis tools for legal investigations and incident
  response.
domain: cybersecurity
subdomain: digital-forensics
tags:
- email-forensics
- pst
- ost
- outlook
- mapi
- email-headers
- attachments
- deleted-emails
- libpff
- eml-extraction
version: '1.0'
author: mahipal
license: Apache-2.0
nist_ai_rmf:
- MANAGE-2.4
- MANAGE-3.1
- MEASURE-3.1
nist_csf:
- RS.AN-01
- RS.AN-03
- DE.AE-02
- RS.MA-01

Analyzing Outlook PST for Email Forensics

Overview

Microsoft Outlook PST (Personal Storage Table) and OST (Offline Storage Table) files are critical evidence sources in digital forensics investigations. PST files store email messages, calendar events, contacts, tasks, and notes in a proprietary binary format based on the MAPI (Messaging Application Programming Interface) property system. Forensic analysis of these files enables recovery of deleted emails (from the Recoverable Items folder), extraction of email headers for tracing message routes, analysis of attachments for malware or exfiltrated data, and reconstruction of communication patterns. Modern PST files use Unicode format with 4KB pages and can grow up to 50GB, while legacy ANSI format is limited to 2GB.

When to Use

• When investigating security incidents that require analyzing outlook pst for email forensics
• 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

• libpff/pffexport (open-source PST parser)
• Python 3.8+ with pypff or libratom libraries
• MailXaminer, Forensic Email Collector, or SysTools PST Forensics (commercial)
• Microsoft Outlook (optional, for native PST access)
• Sufficient disk space for extracted content

PST File Locations

| Source | Path | |--------|------| | Outlook 2016+ Default | %USERPROFILE%\Documents\Outlook Files\*.pst | | Outlook Legacy | %LOCALAPPDATA%\Microsoft\Outlook\*.pst | | OST Cache | %LOCALAPPDATA%\Microsoft\Outlook\*.ost | | Archive | %USERPROFILE%\Documents\Outlook Files\archive.pst |

Analysis with Open-Source Tools

libpff / pffexport

# Export all items from PST file
pffexport -m all evidence.pst -t exported_pst

# Export only email messages
pffexport -m items evidence.pst -t exported_emails

# Export recovered/deleted items
pffexport -m recovered evidence.pst -t recovered_items

# Get PST file information
pffinfo evidence.pst

Python PST Analysis

import pypff
import os
import json
import hashlib
import email
import sys
from datetime import datetime
from collections import defaultdict


class PSTForensicAnalyzer:
    """Forensic analysis of Outlook PST/OST files."""

    def __init__(self, pst_path: str, output_dir: str):
        self.pst_path = pst_path
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        self.pst = pypff.file()
        self.pst.open(pst_path)
        self.messages = []
        self.attachments = []
        self.stats = defaultdict(int)

    def process_folder(self, folder, folder_path: str = ""):
        """Recursively process PST folders and extract messages."""
        folder_name = folder.name or "Root"
        current_path = f"{folder_path}/{folder_name}" if folder_path else folder_name

        for i in range(folder.number_of_sub_messages):
            try:
                message = folder.get_sub_message(i)
                msg_data = self.extract_message(message, current_path)
                if msg_data:
                    self.messages.append(msg_data)
                    self.stats["total_messages"] += 1
            except Exception as e:
                self.stats["parse_errors"] += 1

        for i in range(folder.number_of_sub_folders):
            try:
                subfolder = folder.get_sub_folder(i)
                self.process_folder(subfolder, current_path)
            except Exception:
                continue

    def extract_message(self, message, folder_path: str) -> dict:
        """Extract forensic metadata from a single email message."""
        msg_data = {
            "folder": folder_path,
            "subject": message.subject or "",
            "sender": message.sender_name or "",
            "sender_email": "",
            "creation_time": str(message.creation_time) if message.creation_time else None,
            "delivery_time": str(message.delivery_time) if message.delivery_time else None,
            "modification_time": str(message.modification_time) if message.modification_time else None,
            "has_attachments": message.number_of_attachments > 0,
            "attachment_count": message.number_of_attachments,
            "body_size": len(message.plain_text_body or b""),
            "html_size": len(message.html_body or b""),
        }

        # Extract transport headers for routing analysis
        headers = message.transport_headers
        if headers:
            msg_data["headers_present"] = True
            msg_data["headers_size"] = len(headers)
            # Parse key headers
            parsed = email.message_from_string(headers)
            msg_data["from_header"] = parsed.get("From", "")
            msg_data["to_header"] = parsed.get("To", "")
            msg_data["date_header"] = parsed.get("Date", "")
            msg_data["message_id"] = parsed.get("Message-ID", "")
            msg_data["x_originating_ip"] = parsed.get("X-Originating-IP", "")
            msg_data["received_headers"] = parsed.get_all("Received", [])

        # Process attachments
        for j in range(message.number_of_attachments):
            try:
                attachment = message.get_attachment(j)