MITRE ATT&CK to SIEM Rules: A Practical Look at SIOR-Helper

Going from MITRE ATT&CK to SIEM rules is the part of detection engineering nobody enjoys. You read about a technique, decide you should detect it, and then spend an evening stitching together half-working examples from a dozen blogs until the rule stops alerting on notepad.exe. SIOR-helper.com is a free platform that tries to compress the research half of that workflow. I spent some time poking at it, and this is an honest account of what it does, where it helps, and where it still leaves you doing the real work.

Key Takeaways

• SIOR-helper is a crowdsourced repository that maps MITRE ATT&CK techniques to detection rules, response procedures, and tests, with built-in Sigma rule conversion for several SIEM platforms.
• It shortens the research phase of detection engineering, but it does not remove the steps that actually matter: testing and tuning the rule against your own environment.
• The Sigma-to-SIEM conversion targets Splunk, Elasticsearch, QRadar, and LogPoint, which saves real time when you maintain rules across more than one platform.
• The heat-map view over the ATT&CK matrix is a useful, if humbling, way to see how much of the matrix you actually have detection coverage for.
• It is still in active development with rough edges, so treat any rule it hands you as a starting draft, not a finished detection.

Environment

• SIOR-helper.com, used through the free web interface with a registered account.
• A live demo is available without registration, so you can evaluate it before signing up.
• Detection output expressed in Sigma, convertible to Splunk SPL, Elasticsearch, IBM QRadar AQL, and LogPoint query languages.
• Reference framework: MITRE ATT&CK Enterprise matrix.

The Problem

The honest version of the detection-engineering workflow looks like this. You spot a technique worth catching, then you read the ATT&CK page, search for detection ideas across a pile of blogs of varying quality, find a few examples written for SIEMs you do not run, adapt them to your fields and data model, and finally test and tune until the thing only fires on something interesting. Every step is necessary and most of them are tedious.

The research portion is the part that feels most wasteful, because someone, somewhere, has almost certainly written a detection for the technique you are looking at. You are rarely the first person to want to catch LSASS memory access or an encoded PowerShell stager. The problem is discovery: that prior work is scattered, written for the wrong platform, or buried in a GitHub repository you have never heard of.

SIOR-helper's pitch is to collapse that discovery step. Instead of starting from a blank page, you start from a technique, see what detections already exist for it, and convert them to the query language you actually use. That is a reasonable thing to want. Whether it delivers depends on the quality of the underlying content, which is the part worth examining.

The Solution

Generating an analysis from keywords or threat groups

The starting point is an analysis run. You search by keyword or by threat group, and SIOR returns the relevant ATT&CK techniques. Searching for ransomware in the example I worked through returned 73 techniques. That is faster than scrolling the matrix by hand, though browsing the matrix was never the slow part of the job. The value is less the speed and more that it gives you a scoped list to work down rather than the entire enterprise matrix at once.

Browsing the detection repository per technique

For most techniques the platform surfaces a bundle of associated content: detection rules, response procedures, tests, and related vulnerability information. The Credential Access example around LSASS memory listed 77 detection rules for a single technique. That is a lot of raw material, and the obvious caveat applies: quantity is not quality. Seventy-seven rules are useful only if you can quickly tell the precise ones from the noisy ones, so plan to read before you deploy.

Converting Sigma rules to your SIEM platform

This is the feature that earns its place. SIOR integrates Sigma rule conversion, so a detection written once in the vendor-neutral Sigma format can be exported to Splunk, Elasticsearch, QRadar, or LogPoint query syntax. Rewriting the same logic by hand across platforms is exactly as tedious as it sounds, and getting field mappings wrong is an easy way to ship a rule that silently never matches. Letting the Sigma toolchain do the translation removes a genuine source of error. It is the same engine behind the official SigmaHQ project, so the output is what you would expect from the standard converter rather than something bespoke.

A realistic example: detecting LSASS memory access (T1003.001)

Take T1003.001 (OS Credential Dumping: LSASS Memory). Here is the workflow with and without the platform, written out so the difference is clear:

# Traditional approach
1. Research the technique
2. Find detection strategies across various sources
3. Write platform-specific queries by hand
4. Test and tune for your environment
5. Hope it works

# SIOR approach
1. Search for the technique
2. Browse the available detection rules
3. Convert the chosen rule to your SIEM platform
4. Test and tune for your environment
5. Hope it works

The last two steps are identical, and that is the point. No tool can know your asset inventory, your normal admin behaviour, or which service accounts legitimately touch LSASS in your environment. SIOR removes some of steps one through three; it cannot remove four and five. If a vendor ever claims otherwise, that is the line to be skeptical of.

The advanced search builder for narrowing scope

Beyond keyword search there is an advanced builder that lets you compose more specific queries against the technique catalogue. It is the difference between "show me everything tagged ransomware" and "show me Credential Access techniques with detections that target Windows process access". For building a focused detection backlog rather than an open-ended reading list, the narrower query is the more useful one.

Visualizing detection coverage gaps on the ATT&CK heat map

The feature I found genuinely worthwhile is the heat-map view of an analysis run. After building a run for ransomware techniques, I got a colour-coded rendering of the ATT&CK matrix showing where I had coverage and where I did not. In that run I had touched 88 of 1,076 techniques, which works out to roughly 8 percent, leaving 988 untouched. Seeing that laid out is a useful corrective to the comfortable assumption that you have "the important stuff" covered.

The map breaks coverage into levels rather than a flat covered/not-covered split:

• Complete coverage — techniques where the run found detections, responses, and tests together.
• Partial coverage — techniques with some controls but not the full set.
• Detection only — techniques with a detection rule but no documented response procedure.
• No data — techniques the run has not looked at yet.

You can filter to show only techniques with data, which keeps the view focused on what you have actually worked rather than the vast field of grey. As a way to prioritise the next sprint of detection work, this is the most practically useful part of the platform. It is essentially a coverage-gap map, and gaps in the map are exactly where to invest next.

Where it fits in a detection workflow

SIOR positions itself as a research assistant, not a replacement for judgement, and that framing is right. In practice it slots in like this: use it to find relevant techniques and existing detection content, use the Sigma conversion to get platform-specific queries, then do your own testing, tuning, and validation before anything goes live. If you find an improvement, the community model means you can contribute it back. The platform is crowdsourced, so its long-term value depends entirely on adoption and content quality, both of which are open questions at this stage.

Frequently Asked Questions

Does SIOR-helper write SIEM rules I can deploy without editing?

No. It gives you a draft detection and converts it to your SIEM's query language, but you still have to test and tune it against your own data and normal activity. Treat every output as a starting point, not a finished rule.

Which SIEM platforms does the Sigma conversion support?

The conversion targets Splunk, Elasticsearch, QRadar, and LogPoint. Because it builds on the standard Sigma toolchain, the output matches what the official SigmaHQ converter produces for those backends.

Is SIOR-helper free, and do I need an account?

The platform is free to use. There is a live example available without registration so you can evaluate it first, but creating an account unlocks features like saving analysis runs and the coverage heat map.

How accurate is the detection coverage heat map?

It reflects which techniques your analysis run has associated content for, not whether those detections are deployed and working in your SIEM. It is a planning and prioritisation aid, not a measurement of live detection efficacy.

Is the tool production-ready?

It is still in active development and has rough edges, so treat it as a beta. It is useful for research and planning, but do not build a critical workflow around it expecting enterprise-grade stability.

Conclusion

SIOR-helper solves a real and slightly boring problem: the gap between knowing a technique exists and having a detection for it. It does so with a reasonable approach, crowdsourced content plus Sigma conversion, and it will not revolutionise anything. The research shortcut is genuine, and the coverage heat map is a better prioritisation tool than most teams build for themselves.

What it does not do, and honestly cannot do, is the part that takes the most skill: understanding your environment well enough to tune a rule so it catches attacks without burying analysts in noise. That work stays with you. If you go in expecting a research assistant rather than an autopilot, it earns its place. Given that there is a no-registration demo and the whole thing is free, the cost of finding out whether it fits your workflow is an afternoon, which is a fair trade.

Related Posts

• From Logs to Threats: SIEM Correlation Rules for Real Attacks — what to do once you have a draft rule and need to turn it into a multi-event detection.
• Getting Started with MITRE ATT&CK: Fetching and Processing Data — how to work with the ATT&CK data set programmatically rather than through a web UI.
• Mapping with MITRE ATT&CK for Full Potential — building your own technique mappings as an alternative to a crowdsourced repository.

Editorial note: posts on this blog are drafted with AI assistance and then reviewed, edited, and tested against a real environment before publishing. Commands, output, and screenshots come from systems I actually ran the work on.