Intel to hunt, on data in place
Tools that read new threat reports, turn them into hunts and run them across your existing sources.
Field guide · Updated 2026-10-18
Threat hunting tools compared: the 2026 field guide to detection engineering platforms
7 threat hunting and detection engineering tools, scored on seven published criteria. Every fact comes from the vendor's own public pages, with a source line.
Editorial assessment · From vendor product pages, docs and announcements · Updated 2026-10-18
Does the tool copy your data or query it in place?
Some tools query each source where it sits, often called federated search. Others copy some of the data into their own platform first, which is ingestion. Copying data in adds a second pipeline and a second store to run and pay for, next to a SIEM that already collects, stores and correlates the same events. Querying in place leaves the SIEM as the system of record. We score this under 'Works across existing SIEM and data lakes' (20%). This site does not rank or compare SIEM products; they appear only as data sources.
Which threat hunting tools rank highest in 2026?
We scored seven tools on seven weighted criteria. Hunt automation and working across existing data carry the most weight, because that is the job these tools are bought for. The table shows every criterion, so you can re-rank by the one that matters to you.
| Rank | Tool | Weighted score | Designation |
|---|---|---|---|
| #1= | Mars Security | 7.45 | Joint top pick: intel-driven hunting across your existing stack |
| #1= | Nebulock | 7.45 | Joint top pick: hypothesis-driven hunting with open frameworks |
| #3 | Anvilogic | 7.30 | Best for multi-platform detection engineering |
| #4 | Cotool | 7.10 | Best for building custom blue-team agents |
| #5 | Vega | 7.00 | Best for hand-off hypothesis hunts with source-controlled detections |
| #6 | Artemis Security | 6.75 | Best for autonomous investigation |
| #7 | CardinalOps | 5.60 | Best for measuring ATT&CK coverage |
Weigh the criteria yourself in the calculator, or compare any two to five tools side by side.
Hunt fit score is an editorial assessment against seven weighted criteria. Highest score per criterion is marked on the full table.
Which data sources does each tool hunt across?
Every tool here says it works on the data you already run. This grid shows which source types each vendor's public pages actually name.
- Named
- Not named
| Tool | SIEM | EDR | Identity provider | Cloud logs | Data lake or warehouse | SaaS apps | Named | How it reaches the data |
|---|---|---|---|---|---|---|---|---|
| Mars Security | NamedSplunk | NamedCrowdStrike Falcon | Namedidentity providers | NamedAWS telemetry, Wiz | NamedSnowflake, Databricks | Not named | 5 of 6 | Queries in place, no ingestion For cloud logs: "No new data copy. No second retention clock." "The SIEM keeps its compliance and retention jobs." Source: marssec.ai/guides/cloud-threat-hunting · Read 2026-10-18 · marssec.ai/guides/ai-threat-hunting · Read 2026-10-18 · Source dates shown above |
| Nebulock | NamedMicrosoft Sentinel | NamedCrowdStrike, Microsoft Defender, SentinelOne | NamedOkta, Microsoft Entra, Duo | NamedAWS CloudTrail | Not named | NamedJamf, GitHub | 5 of 6 | Federated search Helix federated search "to get data just-in-time, not just-in-case". Public pages do not say what data is retained in the platform. Source: nebulock.io/platform · Read 2026-10-18 · Source dates shown above |
| Anvilogic | NamedSplunk, Microsoft Sentinel, CrowdStrike NG-SIEM, Elastic | Not named | Not named | NamedS3, Azure Blob, GCS object storage | NamedSnowflake, Databricks, Azure Data Explorer, Amazon Security Lake, Microsoft Fabric | Not named | 3 of 6 | Queries in place; standalone mode also offered "One query across Splunk, Snowflake, Sentinel, S3, and more, without moving the data"; augment, standalone or combined deployment. Source: www.anvilogic.com · Read 2026-10-18 · Source dates shown above |
| Cotool | NamedSplunk, Panther | NamedCrowdStrike, Microsoft Defender, SentinelOne | NamedOkta | Not named | NamedSnowflake, Databricks | NamedCode42, Kandji, Jira | 5 of 6 | Stores some logs, queries the rest "Logs you store in Cotool are optimized for agents" and "a detection can run against logs in Cotool or directly against the tools where your data already lives." Source: www.cotool.ai · Read 2026-10-18 · www.cotool.ai/detect · Read 2026-10-18 · Source dates shown above |
| Vega | Not named | Not named | Not named | Not named | Not named | Not named | 0 of 6 | Queries in place "No migration, ingestion, or egress." Source: vega.io · Read 2026-10-18 · Source dates shown above |
| Artemis Security | NamedSplunk, Microsoft Sentinel, Google SecOps and others | NamedCrowdStrike, Defender XDR, SentinelOne | NamedEntra ID, Okta | NamedAWS Security Lake, AWS Athena | NamedSnowflake, BigQuery | Namedthe homepage names SaaS among the sources it correlates | 6 of 6 | Ingests hot-path data, queries the rest "Hot-path data ingested, high-volume data queried where it lives." Its connectors page lists Splunk, Microsoft Sentinel, Exabeam, LogRhythm and CrowdStrike NG-SIEM among ingested sources. Source: artemissecurity.com/connectors · Read 2026-10-19 · Source dates shown above |
| CardinalOps | NamedSIEM via native APIs (platforms not named) | NamedEDR via native APIs; CrowdStrike Marketplace | Not named | Not named | Not named | Not named | 2 of 6 | Works inside the SIEM and EDR Connects through native APIs and delivers rules in their native syntax; a separate data store is not described. Source: cardinalops.com · Read 2026-10-18 · Source dates shown above |
Notes and sources
- Mars Security
Source: securityboulevard.com · marssec.ai/guides/ai-threat-hunting · Reviewed Sep 2026
- Nebulock
Source: docs.nebulock.io · Reviewed Sep 2026
- Anvilogic: EDR and identity sources are not named on the pages we reviewed.
Source: anvilogic.com · Reviewed Sep 2026
- Cotool
Source: cotool.ai/hunt · Reviewed Sep 2026
- Vega: Vega's pages describe querying sources in place but do not name connectors.
Source: vega.io/platform/connectors · Reviewed Sep 2026
- Artemis Security: Splunk and Microsoft Sentinel are listed as ingested; other sources are queried where they live.
Source: artemissecurity.com/connectors · Read 2026-10-19 · Source dates shown above
- CardinalOps: Works inside the SIEM and EDR a team runs; individual platforms are not named on the pages reviewed.
Source: cardinalops.com/platform · Reviewed Sep 2026
Where does each tool fit?
Hunt and investigate end to end
Platforms that hunt continuously and also investigate cases autonomously.
Detection engineering at scale
Platforms built around writing, mapping and maintaining detections across many data platforms.
How do these tools relate to the SIEM you already run?
They sit on top of it. Every tool in this guide reads from, or deploys detections into, the SIEM, EDR and data lakes a security team already operates. Splunk, Microsoft Sentinel, Google SecOps, Elastic and cloud data lakes such as Snowflake and Databricks appear throughout this guide as data platforms, not as alternatives.
Some tools query each source where it sits, often called federated search. Others copy some of the data into their own platform first, which is ingestion. Copying data in adds a second pipeline and a second store to run and pay for, next to a SIEM that already collects, stores and correlates the same events. Querying in place leaves the SIEM as the system of record. We score this under 'Works across existing SIEM and data lakes' (20%). This site does not rank or compare SIEM products; they appear only as data sources.
What should you read next?
Mars Security vs Anvilogic
Continuous intel-driven hunting against a detection engineering platform with ATT&CK scoring.
Mars Security vs Nebulock
The two joint leaders, criterion by criterion.
What is threat hunting
A working definition, and how hunting differs from alert triage.
Threat hunting without adding headcount
What automation can take off a small team, and what it cannot.
What is new in threat hunting?
CardinalOps
CardinalOps website now redirects to Cribl
cardinalops.com now redirects to cribl.io; on 14 Jul 2026 Cribl announced it is acquiring CardinalOps.
Source: Cribl press release
Vega
Vega brings Wiz cloud risk data into its detections
Vega now pulls Wiz attributes on exposure, privileges and sensitive data into detection and investigation, so an alert on a cloud resource arrives with its risk context.
Source: Vega blog
Mars Security
Mars Security adds hypothesis playbooks
A Mars blog post introduced MARS playbooks for hypothesis-driven hunting, built around attacks that show up as a failed hypothesis rather than a fired alert.
Source: Mars Security blog
Frequently asked questions
Which threat hunting tools ingest data and which query it in place?
Mars Security: Queries in place, no ingestion. Nebulock: Federated search. Anvilogic: Queries in place; standalone mode also offered. Cotool: Stores some logs, queries the rest. Vega: Queries in place. Artemis Security: Ingests hot-path data, queries the rest. CardinalOps: Works inside the SIEM and EDR.
What is the best threat hunting tool in 2026?
On our rubric, Mars Security and Nebulock tie for first at 7.45 out of 10. Mars leads on turning new intelligence into hunts across data in place and on deployment; Nebulock leads Mars on detection-as-code and ATT&CK evidence. Anvilogic (7.30) is the strongest choice if detection engineering across many platforms is the main job.
Do threat hunting tools replace a SIEM?
None of the tools in this guide is scored as a SIEM replacement. They read from, or deploy detections into, the SIEM, EDR and data lakes a team already runs.
What is the difference between threat hunting and detection engineering?
Threat hunting looks for attacker activity that existing detections missed. Detection engineering turns what hunters and threat intelligence find into rules that run continuously. Most tools here do both and hand results from one to the other.
How were the scores produced?
By desk research from each vendor's public pages, reviewed in September 2026, scored against seven published criteria with fixed weights. We did not test the products. See the scoring method.
Scores follow the scoring method. Hunt fit score (editorial assessment, 0-10). Measures fit for running continuous threat hunting and detection engineering on top of the security data you already have. It is not a measure of overall product quality.