Query

Federated Search for Security Teams //

[Query @query_ai]

When you see security teams still trying to force more data into the SIEM ($$) instead of adopting a security data mesh approach

[Query @query_ai]

Security data is distributed by default. Your SIEM holds a fraction of it.

The Query Security Data Mesh connects your data wherever it lives: SIEM, cloud storage, data lakes, EDR, identity, network tools, 50+ sources in all.

learn more: https://t.co/BBgxVlZCee

[Query @query_ai]

Security operations beyond your SIEM.

Lower SIEM costs. Expanded coverage. The data foundation AI agents need.

Your data becomes operational wherever it lives: AWS, CrowdStrike, Databricks, Splunk, and 50+ more sources.

https://t.co/aPHK3bHgq3

[Query @query_ai]

Hint: Your problem is in the data.

[Query @query_ai]

Renewing your SIEM or evaluating a security data platform?

Ask every vendor: where does the index live?

It decides your monthly bill, how fast new data is usable, and which questions get expensive after you sign.

Our CTO's blog on the questions to ask: https://t.co/dfLpptdNvn

[Query @query_ai]

An AI agent with 8x more data sources spent 40% more tokens and got less complete.

5 sources: raw MCP 78%, data mesh 63%
40 sources: raw MCP 53%, data mesh 70%

It ran out of attention, not budget. Then answered with full confidence.

Methodology: https://t.co/8Nj1rSd8Qw

[Query @query_ai]

The little jig your "AI SOC" vendor does when you ask what happens when you run their agents against more than 3 or 4 sources

[Query @query_ai]

Full Investigation Gallery: https://t.co/WGwbbf7BFq

[Query @query_ai]

What does an AI agent actually do in a SOC? 12 real cases.

278 queries. 8 risks found with no alert. 140+ analyst hours saved.

Every case shows its work, including one where the agent got it wrong and a reviewer caught it.

https://t.co/D7Nmc69awB

[Query @query_ai]

Every vendor says "federated search" now. Ask one question:

Where does the index live?

Splunk: Splunk Cloud.
Vega: your cloud storage.
Query: you choose.

That decides what you can ask, what you're locked into, and what you pay.

Full breakdown: https://t.co/tCWxC66VwM

[Query @query_ai]

Retained. Paid for. Never searched.

That's most security telemetry in cloud object storage.

Query Accelerated Connectors make it searchable in minutes, with no data engineering project.

Federate when possible. Accelerate when necessary.

https://t.co/GzVigXFMNL

#SecDataOps

[Query @query_ai]

Archived isn't dead. It just got priced out of your SIEM.

Query Accelerated Connectors keep that data on the security data mesh: fast to search and ready for retro hunts across years of history, with results in seconds.

Your archived logs feel happy. They feel happy.

[Query @query_ai]

Federate first. But when you do need to store data, it shouldn't be out of reach.

Query Accelerated Connectors turn high-volume archives into a fast, queryable source.

Full history. Fully searchable. No pipelines, no re-ingestion.

https://t.co/cUdcCDBYwT

[Query @query_ai]

An AI agent searched 40 security data sources, found about half the answer, and reported done with full confidence.

On the broadest question: 20% wired tool by tool, 83% through one normalized layer.

The problem isn't ingest vs. federate. It's reach.

https://t.co/LJjyMKlLf3

[Query @query_ai]

Attackers don't need to beat your detections anymore. They need to persuade your triage agent.

Planting a sentence in a user agent is easy. Faking a consistent story across your IdP, EDR, and firewall is not.

Query Workers test every claim against sources attackers can't write.

[Query @query_ai]

When the federated search capability you just bought into only works against two sources... and still indexes the data.

[Query @query_ai]

Service accounts don't offboard.

This one ran 10 months after its engineer left. Then its prod key minted a second key from a German host. Nothing alerted.

A Query Worker found it in 11 minutes, and said don't disable it. Contain quietly.

https://t.co/CDIESHVw0w

[Query @query_ai]

An AI analyst that guesses at its conclusions is a risk. One that guesses at its evidence is a liability.

Reasoning can be probabilistic. Normalized evidence can't.

Run the same investigation twice. If the evidence changes, the conclusions don't matter.

[Query @query_ai]

Query Workers, running on the Query security data mesh, are built to make a security architect's life easy.

Connect a source, every Worker reaches further that day. Normalization happens at query time, and it's deterministic. Agents resolve fields instead of guessing at them.

[Query @query_ai]

There's always been something to stress about. Used to be Super Mario 3, now it's probably something like "omg it's impossible to work all of our alerts"