Detect
Multi-layer detection, autonomously written and tuned for your environment.Multi-layer detection, autonomously written and tuned for your environment.
Artemis builds a model of your environment and runs detection against it: adaptive detectors, threat intel, anomaly detection, MITRE ATT&CK rules, and your own. Each detector is tested on your data before it goes live and tunes itself as the environment changes.
Detect
Multi-layer detection, autonomously written and tuned for your environment.Multi-layer detection, autonomously written and tuned for your environment.
Artemis builds a model of your environment and runs detection against it: adaptive detectors, threat intel, anomaly detection, MITRE ATT&CK rules, and your own. Each detector is tested on your data before it goes live and tunes itself as the environment changes.
Detect
Multi-layer detection, autonomously written and tuned for your environment.Multi-layer detection, autonomously written and tuned for your environment.
Artemis builds a model of your environment and runs detection against it: adaptive detectors, threat intel, anomaly detection, MITRE ATT&CK rules, and your own. Each detector is tested on your data before it goes live and tunes itself as the environment changes.
Trusted by SOCs from AI-native startups to global enterprises.
“Now if we want to detect something, we just ask the agent in plain English: help us write a detector to find this.”
Zehuan Li
Security Software Engineer, Cursor
Trusted by SOCs from AI-native startups to global enterprises.
“Now if we want to detect something, we just ask the agent in plain English: help us write a detector to find this.”
Zehuan Li
Security Software Engineer, Cursor
Multi-layer detection wherever your data lives
Detectors autonomously built and tuned from attack paths, threat intel, and baselines, alongside managed rules and your own.
Multi-layer detection wherever your data lives
Detectors autonomously built and tuned from attack paths, threat intel, and baselines, alongside managed rules and your own.
Multi-layer detection wherever your data lives
Detectors autonomously built and tuned from attack paths, threat intel, and baselines, alongside managed rules and your own.
Adaptive detectors
Artemis finds gaps from attack paths, insights, and patterns in your data, then automatically builds, tests, deploys and tunes them as the environment changes.
Adaptive detectors
Artemis finds gaps from attack paths, insights, and patterns in your data, then automatically builds, tests, deploys and tunes them as the environment changes.
Detectors from threat intel
Artemis hunts across your environment for every new threat and writes a detector for your telemetry, so you are covered as soon as new intel drops.
Detectors from threat intel
Artemis hunts across your environment for every new threat and writes a detector for your telemetry, so you are covered as soon as new intel drops.
Anomaly detection
Behavioral detectors compare each user, account, and workload with its own baseline. A transfer that is routine for one service account and unprecedented for another is flagged on the second.
Anomaly detection
Behavioral detectors compare each user, account, and workload with its own baseline. A transfer that is routine for one service account and unprecedented for another is flagged on the second.
MITRE ATT&CK rules
A managed library of known-bad detections mapped to MITRE ATT&CK, maintained by the Artemis research team and live on day one.
MITRE ATT&CK rules
A managed library of known-bad detections mapped to MITRE ATT&CK, maintained by the Artemis research team and live on day one.
Streamed + federated detection
Detectors run on events as they stream in, or inside the tools that already hold your data, in that tool's own query language.
Streamed + federated detection
Detectors run on events as they stream in, or inside the tools that already hold your data, in that tool's own query language.
Bring your own detectors
Write detectors in plain English, SQL, or Sigma, import them from your current SIEM, or manage them as code in Git.
Bring your own detectors
Write detectors in plain English, SQL, or Sigma, import them from your current SIEM, or manage them as code in Git.
Artemis is a force multiplier for detection engineering
Artemis is a force multiplier for detection engineering
Artemis is a force multiplier for detection engineering
01
Artemis learns your environment
Artemis builds a model of your organization consisting of every user, account, and workload, how each usually behaves, and which attack paths lead to the assets that matter most.
The MITRE ATT&CK library is live from the first day
Baselines keep updating as behavior shifts
01
Artemis learns your environment
Artemis builds a model of your organization consisting of every user, account, and workload, how each usually behaves, and which attack paths lead to the assets that matter most.
The MITRE ATT&CK library is live from the first day
Baselines keep updating as behavior shifts
01
Artemis learns your environment
Artemis builds a model of your organization consisting of every user, account, and workload, how each usually behaves, and which attack paths lead to the assets that matter most.
The MITRE ATT&CK library is live from the first day
Baselines keep updating as behavior shifts
02
Detectors are autonomously written and tested on your data
Artemis writes detectors for the gaps it finds and runs each one over your history first. Each is graded on volume, overlap, accuracy, and efficacy, with the reasoning shown.
Validate any detector over a past window and see the hits it would have produced
02
Detectors are autonomously written and tested on your data
Artemis writes detectors for the gaps it finds and runs each one over your history first. Each is graded on volume, overlap, accuracy, and efficacy, with the reasoning shown.
Validate any detector over a past window and see the hits it would have produced
02
Detectors are autonomously written and tested on your data
Artemis writes detectors for the gaps it finds and runs each one over your history first. Each is graded on volume, overlap, accuracy, and efficacy, with the reasoning shown.
Validate any detector over a past window and see the hits it would have produced
03
You approve detectors, or let them deploy automatically
New detectors can wait for your approval or deploy on their own. Every detector records who created it, when, and how, with full version history and one-click rollback.
Detection as code: changes go through pull requests in your repository
Approval rights are set per role
03
You approve detectors, or let them deploy automatically
New detectors can wait for your approval or deploy on their own. Every detector records who created it, when, and how, with full version history and one-click rollback.
Detection as code: changes go through pull requests in your repository
Approval rights are set per role
03
You approve detectors, or let them deploy automatically
New detectors can wait for your approval or deploy on their own. Every detector records who created it, when, and how, with full version history and one-click rollback.
Detection as code: changes go through pull requests in your repository
Approval rights are set per role
04
Detectors continuously tune themselves
When a detector gets noisy, your environment changes, or an upstream source changes its format, Artemis tunes it and shows the diff. Tuning runs on its own by default, or as proposals you approve.
Every signal is investigated, so a broad detector can run without flooding the queue
Detectors learn from the verdicts your team sets on the cases they raise
04
Detectors continuously tune themselves
When a detector gets noisy, your environment changes, or an upstream source changes its format, Artemis tunes it and shows the diff. Tuning runs on its own by default, or as proposals you approve.
Every signal is investigated, so a broad detector can run without flooding the queue
Detectors learn from the verdicts your team sets on the cases they raise
04
Detectors continuously tune themselves
When a detector gets noisy, your environment changes, or an upstream source changes its format, Artemis tunes it and shows the diff. Tuning runs on its own by default, or as proposals you approve.
Every signal is investigated, so a broad detector can run without flooding the queue
Detectors learn from the verdicts your team sets on the cases they raise
Know what you cover, and what it would take to cover more.
Artemis scores your incident readiness out of 100 and tracks it week over week. It also builds detectors to close the gaps it finds, so the score rises on its own. Recommended connectors show which detectors each new source would turn on.
Know what you cover, and what it would take to cover more.
Artemis scores your incident readiness out of 100 and tracks it week over week. It also builds detectors to close the gaps it finds, so the score rises on its own. Recommended connectors show which detectors each new source would turn on.
Know what you cover, and what it would take to cover more.
Artemis scores your incident readiness out of 100 and tracks it week over week. It also builds detectors to close the gaps it finds, so the score rises on its own. Recommended connectors show which detectors each new source would turn on.
Coverage by threat scenario
Coverage in the terms a board uses, such as account takeover, insider threat, and mailbox takeover.
Coverage by threat scenario
Coverage in the terms a board uses, such as account takeover, insider threat, and mailbox takeover.
Coverage by MITRE ATT&CK
Each tactic shows how many techniques your deployed detectors cover, across ATT&CK and ATLAS.
Coverage by MITRE ATT&CK
Each tactic shows how many techniques your deployed detectors cover, across ATT&CK and ATLAS.
Recommended connectors
Hover a suggested source to see the detectors it would light up in your environment.
Recommended connectors
Hover a suggested source to see the detectors it would light up in your environment.
FAQs
What security teams ask us most
FAQs
What security teams ask us most
FAQs
What security teams ask us most
Can we bring our own detection logic?
Yes. Import Sigma or rules from your current SIEM, write detectors in plain English or SQL, or manage them as code in your Git repository. Imported rules are translated by what they detect, so field names that differ between schemas are handled.
How do we know a detector is any good before it fires?
Every detector is validated against your historical data and graded on volume, overlap, accuracy, and efficacy before deployment, with the reasoning behind each grade.
What happens when our environment changes?
Detectors tune themselves and show the diff between versions. Baselines update as behavior shifts, and you can review every change or roll it back.
Won't adaptive detectors flood our queue?
Every signal is investigated before a case reaches your team. Detectors broad enough to bury a traditional SOC can run here, because Artemis does the first pass.
What if an attacker is already inside when baselines are built?
Artemis scores events for suspiciousness and leaves high-scoring events out of the baselines. It also runs several kinds of baseline side by side, so one contaminated baseline does not hide an attacker.
Does the coverage score include our other security tools?
Not yet. Coverage counts Artemis detectors only, so it can understate what your other tools already catch. Alerts from those tools are still ingested and investigated.
Can detectors run where our data already lives?
Yes. Federated detectors run inside supported platforms on their telemetry, in that platform's query language, and their findings are investigated like any other.
