What platforms does Exabot Detect support?

Exabot Detect is available on Web App.

What are the limitations of Exabot Detect?

No public pricing, no self-serve signup and no free tier: the only route in is a demo request, which slows down evaluation. The product page does not name a connector list, ingestion limits or retention terms, so scoping a deployment requires a sales conversation. Detection quality depends on feedback loops and business context rules being configured, so the promised low tuning is not zero tuning.

Exabot Detect

Name: Exabot Detect
Author: Exabot Detect

An AI detection engineer that watches your cloud, identity and SaaS activity

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What is Exabot Detect?

Exabot Detect is the AI detection component of the Exaforce security platform. It learns normal behavior across IaaS, SaaS, identity, code and collaboration tools, then surfaces a small number of high-confidence alerts with evidence, MITRE technique context and a reconstructed attack chain.

Exabot Detect is the detection layer of the Exaforce platform, positioned as an AI detection engineer for security operations teams. It learns the normal rhythm of an environment and flags deviations across infrastructure as a service, SaaS, identity, code repositories and collaboration tools. Rather than requiring a security team to hand-write and maintain rules, the product builds and sustains detection coverage on its own and adjusts as the stack changes and attacker behavior shifts. In practice the tool runs a tiered pipeline. It ingests low-fidelity signals from users, assets and applications, correlates them, enriches them and filters them down to a small set of high-confidence alerts. Machine learning models sit alongside conventional rule-based logic, using statistical behavioral techniques such as rate anomalies and peer group comparison. Alerts arrive with the evidence attached: a curated data dashboard, a graph showing relationships between actors, actions and resources, and the MITRE technique behind each finding, so analysts and auditors can see why something was flagged. The product is aimed at SOC teams, detection engineers and security leaders who cover modern cloud and SaaS estates and find that a SIEM leaves gaps around tools like Google Workspace, Slack, GitHub, Okta, AWS and GCP. Exaforce positions it as either a replacement or an augmentation for SIEM detections, and distinguishes it from legacy UEBA by noting that older user behavior analytics were built for human users on premises and handle poorly with IAM roles, service principals, federated identities and machine accounts. Coverage can be fully managed, fully custom, or mixed, and organization-specific logic can be added through a visual query builder or in natural language. It fits alongside the vendor's other Exabots for triage, investigation and response, and customers can either operate the platform themselves or have Exaforce run it. Pricing is not published on the product page, and evaluation goes through a demo request rather than self-serve signup.

How do you use Exabot Detect?

1Request a demo and scope your environment
Start on the demo request page and describe the cloud, identity, SaaS and code sources you want covered. This is the entry point for evaluation, since there is no self-serve signup.
2Watch the two minute product tour
The tour walks through a real Okta-based attack scenario, showing how signals across multiple systems become a single high-signal alert. It is the fastest way to judge whether the alert style matches how your SOC works.
3Review a customer case study
Read the Accton case study to see how one security team used AI threat detection across AWS and Azure, including auto-triage of third-party alerts and the time saved.
4Connect sources and let baselines form
Once onboarding begins, connect the accounts and applications in scope and allow the behavioral models to learn what normal looks like for your users, roles and services. Expect a period before baselines are meaningful.
5Add organization-specific detections
Use the visual query builder or natural language input to encode logic for your own environment, such as lab activity, maintenance windows or expected contractor behavior.
6Work the queue and give verdict feedback
Review each finding with its evidence dashboard, threat graph and disposition recommendation, then record your verdict. That feedback is how the system sharpens its accuracy over time.

Pros and cons

Pros

Extends detection into SaaS, collaboration and version control tools such as Google Workspace, Slack and GitHub, which the vendor says many SIEMs missAI
Every alert carries natural language reasoning, supporting data, a threat finding graph and the MITRE technique, which helps with analyst review and audit questionsAI
Behavioral models track multiple dimensions at once (geographic, temporal, volumetric, resource and peer group), which suits cloud identities like IAM roles and service principalsAI
Custom detection logic can be added through a visual query builder or in natural language, so teams are not locked into out-of-the-box rules onlyAI
Coverage can run fully managed, fully custom or a mix, and the platform can replace or augment an existing SIEM depending on the gap being filledAI

Cons

No public pricing, no self-serve signup and no free tier: the only route in is a demo request, which slows down evaluationAI
The product page does not name a connector list, ingestion limits or retention terms, so scoping a deployment requires a sales conversationAI
Detection quality depends on feedback loops and business context rules being configured, so the promised low tuning is not zero tuningAI
It is one module of a wider platform, and the strongest results assume the companion triage and investigation Exabots are also in useAI
Vendor comparisons with UEBA and SIEM are self-reported, and no independent benchmark data is published on the product pageAI

How much does Exabot Detect cost?

Integrations

The product page names Google Workspace, Slack, GitHub, Okta, AWS and GCP as platforms where detection coverage extends, and describes the underlying semantic model as deeply integrated with AWS, GCP, GitHub, Okta and Google Workspace. A full connector list is not published on the page.

Features

Detection spans IaaS, SaaS, identity, code and collaboration sources. A tiered pipeline correlates low-fidelity signals and reduces them to a small set of high-confidence alerts. Machine learning behavioral models (rate anomalies, peer analysis, multi-dimensional tracking) run alongside rule-based detections. Each finding includes a curated evidence dashboard, a threat finding graph and the MITRE technique. Custom detections can be written visually or in natural language.

Frequently asked questions about Exabot Detect

How do you use Exabot Detect?
The walkthrough on this page covers 6 steps: 1. Request a demo and scope your environment 2. Watch the two minute product tour 3. Review a customer case study 4. Connect sources and let baselines form 5. Add organization-specific detections 6. Work the queue and give verdict feedback.
What platforms does Exabot Detect support?
Exabot Detect is available on Web App.
What does Exabot Detect integrate with?
The product page names Google Workspace, Slack, GitHub, Okta, AWS and GCP as platforms where detection coverage extends, and describes the underlying semantic model as deeply integrated with AWS, GCP, GitHub, Okta and Google Workspace. A full connector list is not published on the page.
What are the limitations of Exabot Detect?
No public pricing, no self-serve signup and no free tier: the only route in is a demo request, which slows down evaluation. The product page does not name a connector list, ingestion limits or retention terms, so scoping a deployment requires a sales conversation. Detection quality depends on feedback loops and business context rules being configured, so the promised low tuning is not zero tuning.