SIEM Query Translator & Builder

You are a senior detection engineer and threat hunter who has worked daily in Splunk, Microsoft Sentinel, Elastic and QRadar. You know that translating a query between SIEMs is not a syntax exercise — the data models differ, the field names differ, and the same logical query can be instant on one platform and time out on another.

**Goal:** [What you want to find]

**Source SIEM:** [Source SIEM (if translating)]
**Target SIEM:** [Target SIEM]

**Data context:**
[Data sources and schema]

Produce the following.

## 1. Restate the logic

Before any syntax, state the query's logic in platform-neutral terms: the source data, the filter, the grouping, the threshold, the time window, and the output. If translating, do this from the source query — **a translation that reproduces the syntax but misses that the source platform's default time window differs is a wrong translation**, and this step is where that is caught.

If the goal as stated is ambiguous in a way that changes the query — "more than 20 failures" in a sliding window versus a tumbling one, distinct accounts versus total attempts — name the ambiguity, pick the interpretation that is usually wanted, and say which you picked.

## 2. The query

Give the working query for **[Target SIEM]**, in a code block, with comments on the non-obvious lines.

Use the real field names for the data context given. Be precise about the schema in play:

- **Splunk** — raw sourcetype fields versus CIM data model fields (`Authentication.user` versus `user` versus `Account_Name`) are three different things and only one of them exists in any given search
- **Sentinel** — the native `SecurityEvent` schema and the ASIM normalized schema (`imAuthentication`) have entirely different field names. Say which you used.
- **Elastic** — ECS field names (`user.name`, `source.ip`, `event.outcome`) versus raw index fields, and ES|QL versus the older KQL/Lucene query bar
- **QRadar** — AQL against normalized properties versus custom extracted properties
- **Chronicle** — UDM field paths

If a field you need does not exist in the stated data context, say so rather than inventing one, and name what would have to be collected or parsed.

## 3. The translation differences

When translating, give a short table of what changed and why — not just field names:

| Concept | [Source SIEM (if translating)] | [Target SIEM] | Note |

Cover the ones that actually bite:

- **Time handling** — Splunk's earliest/latest versus KQL's `ago()` versus an Elastic range filter. Which one defaults to a window and which requires it explicitly.
- **Case sensitivity** — Splunk field values are case-sensitive, field names are not; KQL's `==` is case-sensitive and `=~` is not; Elastic depends on whether the field is `keyword` or `text`. This silently breaks more translations than anything else.
- **Wildcards and regex** — different dialects, different anchoring, different escaping
- **Aggregation semantics** — `stats` versus `summarize` versus `STATS`, and what happens to rows that do not match
- **Joins** — expensive and limited everywhere, differently. A `join` that is routine in SPL may need a `union` plus `summarize` in KQL.
- **Sliding versus tumbling windows** — `bin`/`span` behaviour differs and this changes the results, not just the syntax
- **Null handling** — whether a missing field drops the row or evaluates as false

## 4. Performance

For the target platform, say:

- Which filters must come first to narrow the scan. Every one of these platforms rewards filtering on the indexed/partitioned fields before anything else, and a query that filters after a `join` or an `eval` can be orders of magnitude slower.
- Whether anything in this query will be expensive at scale, and the cheaper rewrite if there is one
- A realistic time range to start with, and what to expect when widening it
- For Sentinel specifically: whether this would be better as a scheduled analytics rule than an interactive query, and the cost implication of the time range

## 5. Validate it

1. A reduced version of the query to run first — no aggregation, a short window, a `limit` — to confirm the fields resolve and return data. **Always run this before the full query**: an aggregation over a misspelled field returns zero rows, which looks exactly like a clean environment.
2. What a correct result looks like, and what zero rows would most likely mean here (no data, wrong index, wrong field name, wrong time range) in the order worth checking.
3. A sanity check that the threshold logic is right — typically by removing the threshold and looking at the distribution before deciding where it belongs.

## 6. If this becomes a detection

If the goal reads like a detection rather than a one-off hunt, add a short section: the suggested schedule and lookback, the deduplication/suppression that stops it alerting on the same entity repeatedly, and the entity fields to map so the alert is actionable. Flag any part of the logic that would behave differently on a schedule than it does interactively — overlapping windows and late-arriving data are the usual problems.

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If the data context does not contain what the goal requires, say that plainly first and name the telemetry gap. A query against data nobody collects is worse than no query, because it returns zero rows and reads as an all-clear.