Cheap alternative to Dmarcian DMARC analysis

As an engineer, you're likely familiar with the drill: a problem arises, and the first instinct is often to solve it yourself. When it comes to DMARC, that problem is often wrangling the DMARC aggregate reports (RUA records) that land in your inbox. These XML files are crucial for understanding your email ecosystem, identifying unauthorized senders, and ultimately achieving DMARC enforcement. But let's be honest: they're a pain to read, let alone interpret at scale.

Many organizations turn to commercial DMARC analysis services like Dmarcian, which provide excellent dashboards, alerting, and guidance. However, these services come with a price tag that might not always fit your budget, especially if you're working for a startup, a small team, or just prefer a more hands-on approach. The question then becomes: can you get similar insights without the enterprise-level expense? The short answer is yes, but it's crucial to understand the trade-offs.

This article will explore the "cheap" alternatives to Dmarcian, diving into the DIY options and open-source tools available. We'll look at what it takes to implement them, what you gain, and more importantly, what hidden costs and pitfalls you might encounter.

The Core Problem: DMARC Reports are XML

Before we dig into solutions, let's reiterate why DMARC analysis is even a "problem" to begin with. DMARC aggregate reports are sent as gzipped XML files attached to emails. These files contain a wealth of information: which IP addresses sent mail purporting to be from your domain, how SPF and DKIM authentication fared, and what DMARC policy was applied (none, quarantine, or reject).

Here's a tiny snippet of what a raw DMARC aggregate report XML might look like (after decompression):

<record>
  <row>
    <source_ip>192.0.2.1</source_ip>
    <count>15</count>
    <policy_evaluated>
      <disposition>none</disposition>
      <dkim>fail</dkim>
      <spf>pass</spf>
    </policy_evaluated>
  </row>
  <identifiers>
    <header_from>yourdomain.com</header_from>
  </identifiers>
  <auth_results>
    <dkim>
      <domain>yourdomain.com</domain>
      <result>fail</result>
      <selector>s1</selector>
    </dkim>
    <spf>
      <domain>yourdomain.com</domain>
      <result>pass</result>
    </spf>
  </auth_results>
</record>

Imagine hundreds, or even thousands, of these records across multiple reports from different receivers, arriving daily. Manually sifting through this is impossible. You need automation.

DIY Solutions: The Engineer's First Instinct

Your first thought, like many engineers, might be: "I can write a script for that." And you'd be right, to an extent. Building your own DMARC analysis pipeline is entirely feasible, given enough time and expertise.

Option 1: Manual Parsing with Custom Scripts

The most basic DIY approach involves setting up an email address to receive DMARC reports, downloading the gzipped XML attachments, and then writing a script to parse them.

Process: 1. Dedicated Email: Create an email address (e.g., dmarc@yourdomain.com) and configure your DMARC RUA record to send reports there. 2. Collection: Implement a script that connects to this mailbox (e.g., via IMAP), downloads new emails, extracts the gzipped XML attachments, and decompresses them. 3. Parsing: Write code to parse the XML files, extracting relevant data points like source_ip, count, policy_evaluated results (DKIM, SPF, disposition), header_from domain, and auth_results (DKIM and SPF domains/results). 4. Storage: Store the parsed data in a structured format. This could be a simple CSV file, a SQLite database, or even a more robust PostgreSQL/MySQL instance if you anticipate a lot of data. 5. Analysis/Visualization: Develop further scripts to query your stored data, aggregate it, and ideally, visualize it. This might involve generating charts in Python with Matplotlib/Seaborn, or exporting to a spreadsheet for manual analysis.

Real-world Example: Python Script for Parsing

Here's a simplified Python example demonstrating how you might parse a single DMARC XML file to extract key information. This focuses on the parsing logic; email collection and storage would be separate modules.

```python import xml.etree.ElementTree as ET import gzip import os

def parse_dmarc_xml(xml_file_path): """ Parses a DMARC aggregate report XML file and yields relevant data. Assumes a decompressed XML file path. """ try: tree = ET.parse(xml_file_path) root = tree.getroot()

    report_metadata = root.find('report_metadata')
    org_name = report_metadata.find('org_name').text
    date_range_begin = report_metadata.find('date_range').find('begin').text
    date_range_end = report_metadata.find('date_range').find('end').text

    for record in root.findall('record'):
        row = record.find('row')
        source_ip = row.find('source_ip').text
        count = int(row.find('count').text)

        policy_evaluated = row.find('policy_evaluated')
        disposition = policy_evaluated.find('disposition').text
        dkim_result_pe = policy_evaluated.find('dkim').text
        spf_result_pe = policy_evaluated.find('spf').text

        identifiers = record.find('identifiers')
        header_from = identifiers.find('header_from').text

        auth_results = record.find('auth_results')
        dkim_auth =