Natural-language threat hunting

Several major security platforms now let analysts perform threat hunting using plain-English questions instead of manually writing complex query languages such as KQL, XQL, SPL, or proprietary search syntax. The key difference is whether the AI merely generates a query or supports a complete, conversational investigation.

Leading platforms for natural-language threat hunting

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1. SentinelOne Purple AI

(Strong natural-language fit)

Analysts ask questions in plain English, and Purple AI translates them into PowerQueries, searches security telemetry, summarizes findings, and suggests follow-up questions. It can also investigate third-party telemetry.www.sentinelone.com+1

Best for: Teams seeking conversational hunting across endpoint and connected security data.

Official product page

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2. Microsoft Security Copilot — Defender XDR / Microsoft Sentinel

The Threat Hunting Assistant accepts natural-language questions, generates and runs KQL, interprets results, and maintains conversational context for follow-up investigations. It can inspect available table schemas rather than relying solely on assumed table names.www.sentinelone.com+6

Best for: Microsoft-centric environments using Defender XDR and Sentinel.

Official documentation

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3. Google Security Operations — Gemini

Analysts can search security data, refine queries conversationally, drill into results, and create detections using natural language. Gemini generates the underlying query syntax and displays it for review.cloud.google.com+2

Best for: Cloud-scale SIEM hunting and organizations consolidating security telemetry.

Official product page

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4. Elastic Security — AI Assistant / Agent Builder

Its AI chat interfaces assist with alert investigations, security-data questions, incident response, and ES|QL query generation. This reduces the need to author queries from scratch, although query generation and full conversational hunting are not identical capabilities.www.elastic.co+1

Best for: Teams using Elastic for SIEM, endpoint security, and custom telemetry analytics.

Official documentation

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5. Palo Alto Networks Cortex XSIAM — Agentic Assistant

Supports natural-language prompts to create and run XQL queries, visualize data, and investigate threats. The Agentic Assistant can also plan and execute more complex investigation workflows.docs-cortex.paloaltonetworks.com+2

Best for: Enterprises using Palo Alto Networks security products and seeking AI-assisted SOC workflows.

Official product page

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6. CrowdStrike Falcon — Charlotte AI

Provides natural-language conversations and AI-assisted investigations across supported Falcon modules. Its capabilities include query-result interpretation, investigation assistance, and translating legacy queries into Falcon-native syntax. Confirm the specific natural-language hunting functionality available in your licensed modules.www.crowdstrike.com+1

Best for: Organizations already using Falcon for endpoint, identity, cloud, or SIEM security.

Official product page

Quick comparison

Sources: vendor documentation linked above. Feature availability, licensing, and deployment requirements vary.

Which should you shortlist?

• For the most direct plain-English hunting experience: start with SentinelOne Purple AI and Microsoft Security Copilot.
• For SIEM-focused investigations across large volumes of telemetry: evaluate Google SecOps and Cortex XSIAM.
• For an existing security stack: Elastic Security and CrowdStrike Falcon are worth evaluating if you already use their data and detection ecosystem.

One important distinction: natural-language interfaces don't necessarily eliminate query languages. Most of these products translate your question into an underlying query, which you can inspect and validate. For serious threat hunting, prioritize platforms that actually execute searches, explain the results, and support iterative follow-up questions—not just generate query text.

To narrow this to the best three options: are you primarily looking for endpoint/EDR hunting, SIEM and log hunting, or cross-platform hunting across endpoint, identity, cloud, and network data?

If you want to hunt for security threats by typing plain-English questions instead of learning a specialized query language, look for security platforms with natural-language threat hunting or AI-powered SIEM search.

Several established platforms now offer this capability. The key difference is whether they simply translate your sentence into a technical query or let you conduct a full investigation conversationally.

Platforms worth evaluating

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1. Microsoft Security Copilot + Microsoft Sentinel

(Strongest fit for conversational hunting)

Ask questions such as “Show me failed sign-ins for admin accounts this week” or “Which devices contacted suspicious domains today?” The Threat Hunting Assistant can generate and run KQL queries, explain results, and handle follow-up questions during an investigation.

Best for: Organizations using Microsoft Defender, Sentinel, or Microsoft security data.

Source:learn.microsoft.com+6

Official documentation

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2. Splunk AI Assistant in Security

Translate natural-language questions into SPL searches, summarize investigation findings, and suggest next steps. It is designed to help analysts investigate without manually composing every query.

Best for: Teams already using Splunk Enterprise Security or Splunk Cloud.

Source:www.splunk.com+1

Official product page

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3. Elastic Security AI Assistant

Provides a chat interface for investigating alerts, generating queries, analyzing security data, and assisting with incident response. Elastic also offers AI agent capabilities for iterative threat hunting.

Best for: Teams using the Elastic Stack that want flexible log analytics and AI-assisted investigations.

Consider: AI features require appropriate configuration and licensing.

Source:www.elastic.co+2

Official documentation

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4. CrowdStrike Falcon LogScale

A high-performance log search and threat-hunting platform with free-text search, live event analysis, and a purpose-built query language.

Best for: Teams handling large volumes of security logs that prioritize fast searching and investigation.

Important distinction: Its core search capabilities are not the same as a full conversational, plain-English query assistant. Verify the natural-language workflow you need before choosing it.

Source:www.crowdstrike.com

Official product page

Three more platforms to consider

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5. Google Security Operations (Gemini)

Natural-language searches across security telemetry, conversational refinement of results, and AI-assisted detection-rule creation. A particularly strong option if you want to investigate threats without writing YARA-L manually.

Official documentation cloud.google.comdocs.cloud.google.com

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6. Securonix Unified Defense SIEM (Search Agent)

A natural-language search agent designed to understand analyst intent, use context about entities and relationships, and accelerate threat hunting without manual query construction.

Official Search Agent overview www.securonix.comwww.securonix.com

How I would narrow the shortlist

What to test in a demo

Use the same questions against each platform:

• “Show me users with unusual login activity outside business hours.”
• “Find devices that contacted known malicious IP addresses.”
• “Identify possible lateral movement following a compromised account.”
• “Explain why these events are suspicious, and show me the supporting logs.”

Check whether the tool actually executes searches against your data, explains its findings, supports follow-up questions, and lets you inspect the generated query. Also verify licensing, supported log sources, and whether the natural-language features are generally available or still in preview.

One question that would help me recommend the best fit: Are you looking for a standalone platform to ingest and search logs from many different systems, or do you already use a security platform such as Microsoft Defender, Splunk, or CrowdStrike?

The security industry has shifted heavily toward generative and agentic AI, meaning you no longer need to memorize complex syntaxes like KQL, SPL, or Lucene to build queries. Several major platforms feature native natural-language assistants designed specifically to translate plain English into executable threat hunts, interpret the data, and surface actionable insights.sentinelone.com+2

The leading options on the market that specialize in plain-English threat hunting include:

• Microsoft Security Copilot (Threat Hunting Assistant) : Built directly into Microsoft Defender XDR and Microsoft Sentinel, this assistant allows you to type queries in plain English. It is schema-aware, meaning it automatically discovers the right tables (including custom workspace tables), writes the underlying KQL, executes it, and translates the output into natural-language summaries, timelines, or charts.blog.7ai.com+1
• SentinelOne Purple AI : Integrated into the SentinelOne Singularity Platform, Purple AI is an interactive security analyst designed around natural language. You can ask it to hunt for specific adversary behaviors or IOCs in plain English, and it translates your intent into structured queries. It also features collaborative notebooks, pre-populated quick starts, and proactive suggestions for follow-up investigative questions.sentinelone.com+1
• CrowdStrike Charlotte AI (Hunt Agent) : Part of CrowdStrike’s agentic Falcon platform, Charlotte AI features specialized security agents—including a dedicated Hunt Agent. It lets analysts of all skill levels execute cross-domain parallel investigations, query massive telemetry datasets using conversational language, and generate custom scripts without needing to write or sift through complex documentation.crowdstrike.com+1
• Prophet Security : A dedicated AI-driven threat hunting platform built to integrate out-of-the-box with third-party tools like existing SIEMs, EDRs, and cloud logs. It features a conversational natural-language chat interface that completely removes query language dependencies, converts your prompt into a live hunt, and turns successful findings into permanent detections.prophetsecurity.ai
• Crogl : An emerging AI-native threat hunting platform that continuously ingests threat intelligence feeds (such as vendor bulletins and ISAC advisories) and uses automated agents to execute plain-English hypotheses across your full data lake or SIEM environment without requiring schema mastery.crogl.com

If you want to narrow this down, let me know:

• Are you looking for a standalone platform that connects to your existing tools (like a SIEM/data lake), or an all-in-one ecosystem (like CrowdStrike or Microsoft)?
• What core data sources (EDR, cloud logs, identity providers) do you need the platform to search through?

Yes. The market has moved well beyond “AI that merely writes a query.” If your goal is “let an analyst describe what they’re looking for in English and investigate the results without knowing a query language,” these are the strongest options I’d shortlist:

1. Microsoft Security Copilot — probably the closest match

Microsoft now has a dedicated Threat Hunting Assistant in Defender Advanced Hunting. You can ask questions such as “Which devices communicated with suspicious domains today?” in natural language.

Crucially, it doesn't just translate English → KQL. The assistant can generate the query, execute it, interpret the results, surface observations, and continue a multi-step investigation conversationally. It also examines the schemas available in your environment rather than blindly assuming table names.learn.microsoft.com

That's a significant distinction if your requirement is “I don't want analysts writing KQL.”

Microsoft Security Copilot threat-hunting documentation

Best if: you're already using Microsoft Defender XDR and/or Sentinel.

Watch-out: the experience is tightly connected to the Microsoft security ecosystem, and some capabilities are still described by Microsoft as preview/prerelease.learn.microsoft.com

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2. Google Cloud Google Security Operations + Gemini

Google SecOps is probably the other option I'd put at the top of the evaluation list.

Gemini can take natural-language questions and turn them into searches against your security telemetry. You can then refine the search conversationally—for example, start with failed logins, then ask which users were involved, then drill into their associated IPs.docs.cloud.google.com

Google also goes further with its Threat Hunt Agent, which is designed to automate the research, query construction, and noise filtering needed to validate a threat-hunting hypothesis.docs.cloud.google.com+1

Google describes the broader SecOps platform as combining SIEM, SOAR and threat intelligence, with Gemini providing natural-language investigation, summaries, recommended actions, and detection/playbook creation.cloud.google.com

Google Security Operations

Best if: you want an AI-native SIEM and aren't committed to Microsoft.

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3. CrowdStrike Falcon + Charlotte AI

If your telemetry is primarily coming from endpoint/XDR infrastructure, I'd also evaluate CrowdStrike's AI experience.

The attraction here is less “buy a new SIEM” and more ask questions of the security data you're already collecting in Falcon. This can be particularly compelling for an endpoint-focused SOC where analysts want to investigate hosts, users, processes, detections and attack activity without becoming experts in the underlying query syntax.

I'd put it in the same evaluation bucket as Microsoft if you're already heavily invested in CrowdStrike.

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4. Splunk

Splunk remains worth considering if you already have a substantial Splunk deployment.

Its advantage isn't necessarily that it's the simplest greenfield natural-language hunting product; rather, AI assistance can sit on top of a mature SIEM/data platform so you don't have to throw away your existing searches, detections and telemetry.

For a company starting from scratch specifically because it wants plain-English hunting, though, I'd test Microsoft and Google first.

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What I'd choose

If your requirement is specifically:

“A junior/intermediate analyst should be able to type a question in English, investigate the results conversationally, and not need to know KQL/SPL/YARA-L.”

I'd rank the evaluation like this:

1. Microsoft Security Copilot — strongest fit if you're Microsoft-heavy
2. Google Security Operations + Gemini — strongest alternative / very compelling AI-native approach
3. CrowdStrike Charlotte AI — especially compelling if Falcon is your primary security platform
4. Splunk — excellent if you already have substantial Splunk investment
5. SentinelOne Purple AI — worth evaluating if SentinelOne is already central to your endpoint stack

One important distinction when you run demos: don't accept “our AI generates queries from natural language” as equivalent to conversational threat hunting. Ask vendors to demonstrate an end-to-end scenario: “Find evidence of credential theft → identify affected users → determine initial access → find lateral movement → tell me what I should investigate next.” Microsoft's current Threat Hunting Assistant and Google's newer hunt-agent capabilities are particularly interesting because they are moving toward that multi-step model.docs.cloud.google.com+1

If you tell me your current stack (Microsoft, CrowdStrike, Splunk, SentinelOne, Elastic, etc.) and roughly how many analysts/endpoints you have, I can narrow this to the 2–3 platforms I'd actually put on an RFP, including likely pricing model, deployment effort, data requirements, and where each one still requires query-language expertise.