Switching SIEMs used to feel like changing the engine of a car while driving it at 100 mph on a mountain road in the dark. The biggest roadblock has always been the detection rules. Organizations often have hundreds or thousands of custom Splunk SPL correlation searches, many written years ago by people who have since left the company. Re-writing them manually for a new platform can take months (or longer) and usually becomes the #1 reason migrations stall.
In April 2025 Elastic changed the game with Automatic Elastic Migrations an AI-driven feature that can map and translate Splunk detection rules into native Elastic Security rules, often in minutes. Six months later, as we hit Elastic Security 8.19+, the capability has matured, the success rates have climbed, and the roadmap hints at something much bigger: the end of “SIEM lock-in” as we know it.
Why Rule Migration Has Been So Painful
A typical enterprise Splunk deployment in 2024–2025 has:
- 300–3 000+ correlation searches
- Heavy use of macros, lookups, and custom search commands
- Rules written in wildly different styles over a decade
- Many rules that are “good enough” but poorly documented
Translating that corpus manually means:
- Understanding intent (often lost in time)
- Learning the target query language (ES|QL, KQL, etc.)
- Re-implementing lookups and enrichments
- Testing against production data without creating alert storms
No wonder Gartner kept citing “content migration effort” as the top inhibitor to SIEM replacement for years.
Automatic Migration: How It Actually Works Today
Elastic’s Automatic Migration (GA since 8.18/9.0) follows a surprisingly straightforward workflow:
- Export your Splunk savedsearches.conf (or use the REST API bundle export)
- Upload the JSON bundle in Kibana → Security → Manage → Automatic Migration
- The system immediately:
- Parses every rule, macro, and lookup
- Uses ELSER semantic search to see if an existing Elastic prebuilt rule (1 300+ and growing) already covers the same MITRE technique or behavior
- If no prebuilt match → hands the rule to a grounded generative AI model that translates the SPL into ES|QL or Threshold/EDR rules
- Prompts you to upload any referenced macros/lookups so the translation stays functionally identical
- You get a side-by-side review pane (original SPL ↔ new Elastic rule) with confidence score.
Real-world numbers shared by early adopters at ElasticON 2025:
- 60–85 % of rules map directly to Elastic prebuilts (semantic match, not text match)
- Another 10–25 % translate automatically with >95 % functional equivalence
- The remaining <10 % need minor human review (usually exotic lookup joins or legacy commands)
That turns a 6–18 month migration into weeks.
Under the Hood: Two AI Engines Working Together
Elastic didn’t just slap ChatGPT on the problem. They combined two purpose-built models:
- ELSER v2 (Elastic Learned Sparse Encoder for Retrieval) – outbound-trained on millions of detection rules, MITRE ATT&CK descriptions, and threat reports. It understands that “powershell.exe -enc” and “process where process.name : ‘powershell.exe’ and process.command_line : ‘* -EncodedCommand *’” mean the same thing, even if the syntax is completely different.
- Grounded LLM (currently Anthropic Claude 3.5 Sonnet + Elastic proprietary fine-tuning and RAG) – when no prebuilt rule matches, the LLM receives the original SPL, any associated macros/lookups, plus Elastic’s rule schema and ES|QL documentation as context. The prompt is heavily engineered to force functional equivalence and to never hallucinate fields that don’t exist.
The combination is key: semantic search catches the “known knowns,” gen-AI handles the long-tail custom logic safely because it is grounded.
What “Beyond” Actually Looks Like in 2026–2027
Elastic has been unusually transparent about the roadmap. Here’s what is either already in private preview or publicly committed:
| Capability | Status (Nov 2025) | Expected GA | Impact |
|---|---|---|---|
| Splunk → Elastic dashboards | Public preview | Q1 2026 | Full content parity (visualizations, panels, drilldowns) |
| Other legacy SIEMs (QRadar, ArcSight, Sentinel) | Labs / selected customers | 2026 | Ends the “we can’t leave Splunk” conversation forever |
| Bidirectional translation | Internal prototype | 2026–2027 | Write once in Sigma/ES |
| AI rule optimization & tuning | Attack Discovery GA, expanding | Ongoing | Auto-tune thresholds, suppress duplicates, suggest gaps |
| “Write once, run anywhere” Sigma++ | Community discussion | 2027? | Elastic + Splunk + Microsoft jointly maintaining a universal rule format? |
The holy grail is bidirectional, multi-vendor translation. Imagine writing a detection in plain English or Sigma, and having every major SIEM vendor’s platform instantly generate a native rule with correct field mappings. The technology already exists today inside Elastic’s Automatic Migration engine — the only remaining barriers are commercial, not technical.
The Bigger Picture: Death of the Proprietary Query Language
Every legacy SIEM vendor built a moat out of query language lock-in:
- Splunk → SPL
- Microsoft → KQL (Sentinel/Defender)
- QRadar → AQL
- Elastic → originally Lucene, now ES|QL
When AI can translate between them with 95 %+ fidelity in seconds, that moat collapses. The conversation shifts from “How do we rewrite 2 000 rules?” to “Which platform has the best analyst experience, best TCO, and best AI?”
Elastic is betting everything on the last three and Automatic Migration is the battering ram.
Final Thought
Six months ago if you told a Splunk shop they could migrate their entire detection rule portfolio over a long weekend they would have laughed. Today that is a supported, documented workflow with thousands of rules already migrated in production.
