SIEMslator by Lateos · Available on AWS Marketplace
Your analyst just spent three hours converting a Sigma rule to KQL for a client who switched SIEMs. SIEMslator does it in three seconds via REST API.
The problem every MSSP knows
01 · MIGRATION
Your client moved from Splunk to Sentinel. You have 847 detection rules. Each needs rewriting in KQL. Your analyst estimates three weeks. The client wants it in five days.
02 · ZERO-DAY
A new Sigma rule drops covering a critical vulnerability. You run Splunk. Your top client runs Elastic. Another runs Chronicle. Three rewrites. Three hours. The window has passed.
03 · ONBOARDING
A junior analyst joins. They know Splunk. Your biggest client runs QRadar. You spend two weeks teaching KQL instead of hunting threats. This is a tooling problem.
Live example
Input — Sigma YAML
title: Mimikatz Detection detection: selection: EventID: 4688 CommandLine|contains: mimikatz condition: selection
Output — Splunk SPL
index=windows sourcetype="WinEventLog:Security" EventCode=4688 CommandLine="*mimikatz*"
Output — Elastic EQL
process where event.code == "1" and process.command_line like~ "*mimikatz*"
Output — Sentinel KQL
SecurityEvent | where EventID == 4688 | where CommandLine contains "mimikatz"
Four strategies
Convert Sigma rules to any supported SIEM platform syntax. Field mapping, operator translation, syntax adaptation — one API call.
Describe a threat scenario in plain language. Receive a ready-to-deploy detection rule for your target SIEM platform.
Submit any detection rule. Receive a plain-English breakdown of what it detects, why it matters, and what false positives to expect.
Generate platform-specific threat hunting queries from ATT&CK technique descriptions for proactive detection engineering.
Our mission
We build security and compliance AI tools that give MSSP and SOC teams back the hours lost to manual rule translation, dataset overhead, and cross-jurisdiction compliance gaps — so analysts can focus on what matters: stopping threats faster.
Every Lateos model is purpose-built, continuously trained, and benchmarked against real-world detection engineering workflows. We don't ship general-purpose LLMs with a security skin. We build narrow, deep, and fast.
Detection rule translation that used to take a senior analyst three hours now takes three seconds via REST API — measurable ROI from the first call.
Threat landscapes evolve. So do our models. SIEMslator and POLYGLOT are retrained on verified new Sigma rules and SIEM syntax updates on a rolling cycle — not frozen at a training cutoff.
Every record in our training pipelines carries a legal source attribution. No grey-zone scraping. No synthetic laundering. Every fine-tune can be audited end-to-end.
Core values
Every record in our fine-tuning datasets carries a traceable legal source: open-licensed Sigma repositories, permissive vendor documentation, and curated MITRE ATT&CK content. We publish our data lineage. No hidden scrapes. No laundered synthetic data presented as human-authored.
General models hallucinate on SIEM syntax. Ours don't — because we retrain continuously on verified detection engineering content. Our mission is to build the world's most accurate, domain-specific security AI models, not the biggest ones.
Compliance isn't retrofitted. Our data pipelines are architected from day one for EU AI Act (Art. 9/13/15) traceability requirements and US NIST AI RMF governance standards — with data residency boundaries enforced at the infrastructure layer.
We publish accuracy metrics, known failure modes, and dataset provenance as part of the product — not as afterthoughts. If our model can't reliably handle a query type, we say so, with data, before you pay for it.
Every API endpoint, training artifact, and deployment pipeline ships with the assumption it operates in a high-stakes security environment. HITL controls, prompt injection detection, and cryptographic audit trails are core features — not enterprise add-ons.
We measure success in analyst-hours recovered and compliance gaps closed — not model parameters or benchmark leaderboard positions. Every product decision at Lateos traces back to a concrete, measurable customer outcome.
Pricing
Small teams
$299
per month · via AWS Marketplace
MSSP teams
$999
per month · via AWS Marketplace
Unlimited usage
$2,499
per month · via AWS Marketplace