“Should we go all-in on Sigma?” is the question that comes up first when a team decides to take DetectionOps seriously. It is a fair question, but it quietly skips an earlier one: compared with what? In this piece we line up three realistic ways to manage detection rules, say what each is good at, and show where each tends to hurt. No tool worship, just trade-offs you can discuss over coffee.
The three contenders
- Console-managed rules. Everyone edits rules directly in the monitoring product.
- Native queries as code. The same rules, stored as files in a repository and deployed by a pipeline, still written in the platform’s own query language.
- Portable rules as code. Rules written in a neutral format such as Sigma and converted to whichever platform you run.
The Ops Atlas entry on DetectionOps treats the second and third as two flavours of the same discipline, and that framing is helpful. The big step is moving from the first to either of the others. Choosing between them is a smaller decision than people expect.
Round one: speed of the first week
Console editing wins on day one. There is nothing to set up, and an analyst can tweak a rule in thirty seconds. That is exactly why teams stay there for years. The cost shows up later, when somebody asks who changed a rule and why, and the console has no good answer.
Native queries as code take a little setup: a repository, a build runner and a deployment script. The Atlas says a small lab needs only Git, two scripts, a runner and one API connection. Portable rules add one more layer, the converter, and with it a new thing to keep upgraded.
Round two: portability
This is where the neutral format shines. If you run two monitoring products, or expect to migrate in the next two years, writing rules once and converting them saves effort. The format’s current specification, version 2.1.0 from August 2025, describes each rule with a title, a log source and a detection condition, plus a unique identifier and case-insensitive value matching.
If you run one platform and do not plan to leave, though, portability is a benefit you may never cash in. The Atlas gives a clear steer for exactly that situation: a team on a single monitoring product, with platform-neutral requirements and tests, can reasonably keep native queries as code. In other words, the format is not mandatory, which comes as a relief to a lot of people.
Round three: how fragile is the toolchain?
Here the portable approach pays a tax. Look at what the Atlas logged for this month. The library that does the heavy lifting inside rule converters shipped a version 2.0.0 in early October, then 2.0.1 a week after, but the conversion CLI and a well-used Splunk backend are still written for the 1.x line. Anyone whose versions floated woke up to red builds they had not caused.
Native queries have fewer moving parts, so fewer such surprises, but they are tied to the vendor’s roadmap. The same Atlas page notes that one monitoring product’s older portal loses support after 31 March 2027 and that the vendor suggests a newer preview feature for new rules. Either way, the conclusion is the same: pin versions, read release notes, and upgrade deliberately.
Round four: testing and review
Here the three contenders separate sharply, but not along the line you might expect. Console-managed rules can technically be tested, yet almost nobody does, because tests need somewhere to live. Both code-based options give tests a home and make peer review natural. A 2026 practitioner survey summarised in the Atlas has roughly three in five respondents keeping rules under version control, a similar share reviewing each other’s work, and well under half pushing through CI/CD. Most teams, in other words, are standing halfway up this ladder.
A tip regardless of option: write tests from the requirement, not from the rule’s query, or they will pass by construction. Aim for at least one triggering case and one harmless lookalike per rule.
Round four and a half: the cost of change when something breaks
Picture a quiet Thursday when a rule starts flooding the queue. In the console world, someone edits it live, relieved, and goes home. In either code-based world, someone opens a change, a reviewer glances at it, the tests run, and the pipeline deploys. That feels slower by about ten minutes, but it leaves a trail, and the fix survives the next release.
The Atlas even gives urgency a defined lane. When an active campaign forces a rushed change, the Atlas lets it bypass the waiting period only; the tests and the reviewer stay, and a second look follows inside five working days. Notice what that means for the comparison: the code-based options do not forbid speed, they just refuse to let speed erase the record. Console editing offers no equivalent safety net, so a console hot-fix is the one most likely to vanish, be repeated, or be quietly contradicted next month.
Round five: starting content
Community libraries of ready-made rules, with over three thousand sitting in the largest open collection, tempt teams to import everything. The Atlas calls those starting points rather than finished work. Whichever approach you choose, import selectively, test what you adopt, and write down why each one stays.
One more filter: a rule keyed to a lone file hash or IP address is the cheapest kind for an attacker to dodge, so lean toward behaviour-based logic where you have the choice.
A simple scoring table in words
If you like decisions made on a napkin, here is the verdict in plain language. For a team with one monitoring platform and no plan to change: native queries as code. For a team with several platforms, a managed-service business serving different clients, or a planned migration: portable rules, with a pinned toolchain. For a team still in the console: move to either, because the leap between the first option and the other two is far bigger than the gap between those two.
What our readers usually ask next
The follow-up question is typically how fast to move. The Atlas suggests a thirty-day goal of exporting rules to Git with a lint check in CI, then tests for the twenty noisiest rules by day sixty, and shadow mode plus a first emulation round by day ninety. If that sounds like a machine-learning delivery path, you are not imagining things; our look at how AIOps platforms speed up IT problem resolution and our overview of an MLOps roadmap for engineering teams describe the same repository, test and measure rhythm in different domains.
Choose according to how many platforms you run now, pin the toolchain, write tests first, and look again in twelve months. None of this is permanent; what counts is the routine, not the file format.