脅威検知調査および対応（TDIR）

Detect, investigate, and respond faster with an AI-native platform that unifies security operations workflows and enables human-agent teaming.

デモのリクエスト

アナリストの生産性向上

TDIRを単一のワークベンチに統合

Prioritize alerts, automate evidence collection, build timelines, and manage cases from a centralized workbench. See related detections in one view and focus effort on credible threats using context-aware risk scoring for human users and AI agents.

検出精度の向上

誤報ではなく本物の脅威に焦点を当てる

Reduce false positives by grouping related events and entities into one view. New-Scale Fusion correlates behavior for user accounts, service identities, and AI agents so your team can assess risk faster and act using shared cases, escalation, and notes.

Triage high-risk detections versus low-fidelity alerts

自動化された脅威のタイムライン

機械化されたタイムラインによる調査の迅速化

Investigate faster with timelines that collect evidence and correlate alerts. Analysts see how an attack started, progressed, and where to act next, including activity from AI agents. Exabeam Nova builds and summarizes timelines to support human-agent teaming.

反復的なワークフローを自動化

レスポンス・アクションの標準化と自動化

Reduce manual effort with prebuilt playbooks and a no-code editor. Automate triage, case escalation, and context collection to reduce response time. Exabeam Nova works within response workflows to assist with triage and evidence collection for AI-related incidents.

明確な脅威の説明

範囲と影響を理解し、伝える

Quickly interpret the impact of any security event. New-Scale Fusion explains threat behavior in plain language and adds context so analysts can assess risk and share findings. Exabeam Nova automates tasks while analysts validate conclusions and drive decisions.

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お問い合わせ

よくある質問

How does Exabeam use machine learning (ML)?

Exabeam pioneered ML for user and entity behavior analytics (UEBA) and for automating the TDIR workflow. These models help security operations teams reduce noise and focus on credible threats by:

Event correlation: Analyzing raw, stateless events and linking them into a coherent history of user and device activity for faster triage.
Behavioral modeling: Establishing a baseline of normal activity for every user and device using hundreds of behavior-based models.
Peer grouping analysis: Dynamically determining peer groups and host functions to improve anomaly detection.
Threat analytics: Identifying specific threats such as algorithmically generated malicious domains.
Risk-prioritized alerts: Adjusting risk scores to reduce false positives and highlight activity that warrants investigation.

Exabeam、AIエージェントが関与する捜査をどのようにサポートするのか？

AI agents are monitored like any other entity. New-Scale Analytics automatically includes their actions in Threat Timelines, giving analysts visibility into when an AI agent acted, whether the behavior was expected, and how it influenced the sequence of events. This context helps teams investigate faster and avoid missed or misinterpreted activity through effective human-agent teaming.

AIエージェントは捜査のワークフローにどのように参加するのか？

Exabeam Nova agents, part of New-Scale Fusion, assist with triage, evidence collection, timeline summaries, and suggested next steps. They reduce manual effort and help analysts move faster, especially when human and AI identities are involved in the same investigation.

Exabeam、AIエージェントが危険にさらされたり、悪用されたりした場合に、それを検知することができるのだろうか？

Yes. By monitoring AI agent activity and correlating it with other events, New-Scale Analytics helps analysts identify misuse. If an agent accesses sensitive data or performs unexpected actions, that behavior appears in the Threat Timeline and surfaces in Threat Center so analysts can quickly assess and respond.

エクサビームでは、TDIRのタイムラインをどのように可視化しているのですか？

New-Scale Fusion offers multiple timeline views to support different stages of investigation:

Threat Timelines: Available in Threat Center, these timelines visualize alerts and active cases by combining correlation rule triggers and behavioral analytics alerts.
Investigation Timelines: A Search feature that lets analysts build custom timelines for any entity, including users, hosts, applications, and processes with fine-grained filtering.
Smart Timelines: Precomputed investigation views within Investigation Timelines that automatically highlight abnormal behavior against a learned baseline to speed anomaly analysis.

Exabeam TDIRへのアプローチはどう違うのか？

Many security tools rely on signature-based detection, which often misses subtle or novel attacks. New-Scale Fusion uses behavioral analytics to establish a baseline of normal activity for users and entities, making it easier to detect deviations tied to compromised credentials, insider misuse, or suspicious automation. Instead of replacing analysts, Exabeam delivers human-agent teaming that combines machine speed with human judgment to reduce risk and improve outcomes.