I've been reviewing the default detection rule content provided by several major SIEM vendors for a new deployment. After mapping them to our actual telemetry schema and evaluating their logic, I'm left with a strong conclusion: these pre-packaged detection rules offer minimal operational value for a mature security team.
The core issue is a fundamental mismatch in abstraction. These rules are written for a generic, often idealized, data model. Our environment uses OpenTelemetry for logging, with custom attributes for service names, tenant IDs, and business context. A vendor rule looking for `eventName="UserLogin"` is useless when our event is a structured log with attributes `rpc.method="CheckAuth"` and `tenant.id=*`. The rules lack the necessary context about *our* application topology and data flow.
Furthermore, they often generate excessive cardinality in alerting systems. A rule triggered on a generic "failed login" event, without scoping to specific user roles, services, or failure modes relevant to our threat model, creates alert fatigue. It's the metric cardinality problem, but in the SIEM. We end up with thousands of low-fidelity alerts that obscure genuine incidents.
Consider this pseudo-rule example, reminiscent of what vendors ship:
```sql
SELECT * FROM events
WHERE event_id = '4688'
AND command_line LIKE '%powershell%'
AND command_line LIKE '%encodedcommand%'
```
This lacks critical context. Is this from a developer workstation, a CI/CD runner, or a production server? Is the process lineage expected? Without embedding our service and resource metadata, the signal-to-noise ratio is far too low.
The only utility I see is as a very basic starting point for a junior analyst learning the query language. For actual detection engineering, we must build rules from the ground up, rooted in our own distributed tracing data, service meshes, and known behavioral baselines. Does anyone else find themselves discarding these packs entirely and treating the SIEM purely as a data lake and query engine for custom detections?
you can't fix what you don't measure
You've nailed the core abstraction mismatch. It's the same fundamental problem we see with out-of-the-box dashboards for Prometheus; they assume a specific metric naming convention and label schema that rarely survives contact with a real, evolving application. Your point about alert cardinality is critical. These generic rules often fail to incorporate the concept of dimensional reduction, which is Observability 101. They alert on the raw event stream without grouping by meaningful scopes like `tenant.id` or `service.name`, creating a flood of identical tickets.
I'd push your point further: even if you could map their `eventName` to your `rpc.method`, the logic is usually too simplistic. A mature detection needs to correlate across services and understand failure modes specific to your implementation. A vendor rule doesn't know that a `CheckAuth` failure from your edge proxy has a completely different security implication than one from your legacy internal service.
The only value I've found in these packs is as a sparse, high-level taxonomy of things you *might* want to detect. You use them as a checklist, then immediately write your own detection logic in your own query language against your actual telemetry schema. Treating them as operational content is a path to alert fatigue and a false sense of coverage.
metrics over vibes
Yeah, the cardinality explosion is a silent killer. I've inherited alert rules that were basically `count by source_ip` and wondered why the previous SRE burned out. You can't just throw a generic rule at a multi-tenant system and expect anything but noise.
You mentioned using them as a checklist - that's exactly it. We treat them like a weak signal for a threat model brainstorming session, then toss the actual query. The real value, if any, is spotting the detection *category* we hadn't considered for our own stack.
Ironically, the best prepackaged "rules" I've seen are just well-commented, templated Kusto or PromQL snippets that show *how* to do dimensional grouping, not what to detect.
Pager duty is not a hobby
Oh man, this hits home from the marketing ops side. It's the same with out-of-the-box "anomaly detection" for web traffic or email engagement in platforms like Marketo or HubSpot. They alert on a generic spike in form submissions, but without any context of *which* form, *which* campaign, or the expected baseline for that specific asset. Total noise.
You're spot on about the abstraction mismatch. Our lead scoring rules would be useless if they just looked for "page_view" without mapping to the actual form IDs and UTM parameters we've built. I guess vendors have to build for a lowest common denominator data model, but it makes the packs feel more like a starting template you'll completely rewrite.
I wonder if the real value, like you said, is just as a checklist to make sure you've *considered* certain threat types, even if you have to rebuild the logic from the ground up with your own schema.
stay automated
Oh absolutely. But the problem's even more basic than that mismatch.
It's not just that they use generic event names. It's that the entire *philosophy* of a pre-built rule pack assumes security events are a universal constant, like the speed of light. They're not. My "failed login" isn't your "CheckAuth error" isn't the finance team's "invalid SAML assertion."
These packs are a vendor's security theater. They're a checkbox for the sales deck - "Look, 500+ detections out of the box!" - and a trap for teams who think they can skip building their own threat model.
I treat them like a very overpriced, very vague book of recipes. You glance at the chapter titles ("Suspicious Authentication"), then close it and cook your own meal with ingredients you actually have.
You're right about the mismatch, but you're missing the bigger trap: the renewal penalty.
Those "useless" rule packs are never free. They're a line item with annual price increases tied to "content updates." Try removing them at contract renewal to save costs. You'll hit a 20% price floor clause or lose your "enterprise" support tier.
So you're paying for shelfware you can't even use. That's the real vendor lock-in.
trust but verify
That makes a lot of sense. The abstraction mismatch you describe is exactly what happens when we try to use out-of-the-box email performance alerts. They look for a generic "spike in bounce rate," but don't know our specific list hygiene rules or seasonal campaign patterns.
You mentioned alert cardinality being a problem for your SIEM. Does tuning these generic rules down to something useful ever feel like more work than just writing your own from scratch?
It's way more work, honestly. You spend days trying to reverse-engineer their logic to fit your schema, and you're left with this Frankenstein's monster of a rule that's impossible to maintain.
I've started to think of it as a kind of tax. If you just build your own from scratch, at least you understand the whole thing and can tune it as your data changes. But if you inherit this half-tuned vendor rule, you're always afraid to touch it because you don't know what assumptions are baked in.
Do you find the same thing with your email alerts? Like, is it easier to just build a custom alert for "bounce rate for Campaign X exceeding threshold Y" than to bend the generic one?