内部脅威

Detect and respond to anomalous behavior from humans and entities and track suspicious activity from AI agents before insider risk escalates.

デモのリクエスト

隠れた脅威を検出する

インサイダーの脅威を発見する

Identify intentional or accidental insider threats by learning normal behavior for human and non-human entities, including AI agents. Automated Threat Timelines link related actions so you can uncover slow-moving risks other tools miss.

すべてのIDを監視する

See Every Action From People and AI Agents

AI agents act on their own, access sensitive data, and can take insider actions. Behavioral analytics track human, machine, and agent identities so you see their access, data movement, and activity. Turn opaque logs into actionable insight with native support for major AI platforms:

Google Gemini
ChatGPT
Microsoft Copilot

セキュアAIエージェント

Extend Insider Threat Detection to AI Agents

AI agents introduce a new insider risk. Agent Behavior Analytics (ABA) applies proven behavioral analytics to monitor agent activity and find risky actions earlier. Your team can detect misuse, prompt issues, and Shadow AI activity sooner.

監査改ざんを暴く

悪意ある活動を隠そうとする試みを暴く

Insiders with system knowledge may alter or delete logs to hide actions. Behavioral analytics adds business context to show intent. Threat Timelines keep log changes visible over long periods, even when human or AI identities try to erase suspicious behavior.

データ破壊を防ぐ

重要データの異常削除を検出する

A malicious insider may delete important information to disrupt operations. Exabeam baselines file activity for humans and human-agent interaction, automatically flagging abnormal deletion patterns so your team can act before damage escalates.

悪意のある内部関係者を検知する

クレデンシャルの不正使用を発見する

Malicious insiders may use their access to reach critical systems. You need a way to track their behavior and understand incident scope. Exabeam correlates behavioral analytics from human users with activity logs from AI agents to show risk and impact.

Spotting credential misuse for personal gain

情報漏えいを発見する

データ漏洩を明らかにするイベントを接続する

Data leakage can resemble normal activity. Exabeam puts DLP alerts in context by correlating them with authentication, access, and other event data. By baselining behavior for users and monitoring agent activity your team can see intent that other tools miss.

Understand user intent quickly and accurately

特権アカウントの監視

不正アクセスを特定して情報漏えいを防ぐ

Attackers often target privileged accounts to evade controls or get to sensitive information. Exabeam analyzes user context and flags abnormal behavior patterns for human, agent, and entity identities so your team can act on unauthorized activity earlier.

特権の昇格を検出する

権限昇格攻撃を阻止せよ

Privilege escalation attempts put critical assets at risk. Exabeam monitors credential activity and highlights anomalies in Threat Timelines. Your security operations team can uncover escalation behavior, even when it unfolds slowly or through automated actions.

データアクセスの不正使用を防ぐ

リスクの高いアクセスを特定する機密データ

Malicious insiders may abuse their privileges to reach sensitive data. Exabeam baselines normal behavior for users and monitors agent activity to flag anomalies. Long correlation windows reveal risk patterns over time, so your analysts see the full picture.

物理的アクセスセキュリティ

不審な物理的アクセスを検知する

Exabeam monitors for physical access anomalies, such as badge misuse or impossible travel. These events can signal credential sharing or other insider activity. By correlating identity, geolocation, and access data, your team can uncover subtle threats.

まずは専門家にご相談ください。

お問い合わせ

よくある質問

なぜAIエージェントは内部脅威と見なされるのか？

AI agents operate with valid credentials, access sensitive data, and take autonomous actions. When misused, compromised, or poorly governed, they behave like insiders and introduce a new category of risk.

Exabeam は内部脅威をどのようにカバーしていますか？

Exabeam provides insider threat coverage for human users and non-human entities like AI agents. Our patented Session Data Model maintains open-ended correlation windows to detect slow-moving threats that unfold over weeks or months. When combined with behavioral analytics for users and monitoring for agents, this visibility helps your team reveal activity most SIEM and EDR tools miss.

ExabeamはAIエージェントをインサイダーとして監視しているのか？

Yes. Exabeam monitors AI agents as insiders because they act with credentials and access sensitive data. We collect and correlate their logs to provide deep visibility into actions, helping your security team investigate suspicious behavior and hunt for threats from machine entities.

Does Exabeam map lateral movement to the MITRE ATT&CK^® framework?

Yes. Exabeam maps detection coverage to the ATT&CK framework. For the Lateral Movement tactic, this includes specific techniques and sub-techniques such as Remote Desktop Protocol (RDP), SMB or Windows Admin Shares, Distributed Component Object Model (DCOM), Secure Shell (SSH), Virtual Network Computing (VNC), and Windows Remote Management (WinRM). New-Scale Fusion uses behavioral analytics to detect these threats, builds cases with correlation rules, automates response through Automation Management, and provides dashboards organized by ATT&CK tactics, techniques, and procedures (TTPs).

Can I keep my current SIEM and add Exabeam behavioral analytics to address insider threats?

Yes. Many customers integrate data from SIEMs such as Splunk, Microsoft Sentinel, IBM QRadar, and others. New-Scale Analytics adds behavioral analytics for users and Agent Behavior Analytics for AI agents, giving your security operations team deeper visibility into insider threats without extensive retraining.

内部脅威を検知するためのSIEMやEDRツールと、Exabeamは何が違うのか？

Most SIEM and EDR tools rely on short correlation windows, which makes it difficult to detect insider threats that evolve slowly. The Exabeam Session Data Model maintains long-term, stateful timelines that track behavior over extended periods. Exabeam also uses behavioral analytics to detect risky behavior (not just rule violations) and offers broad prebuilt detection coverage for the AI workforce. This approach helps your analysts surfaces subtle anomalies and insider activity that competitors often overlook.

「実際の攻撃の90%では、漏洩した認証情報が使用されており、これを検知し防御するのは非常に困難です。私たちがExabeamを選んだ理由は、セキュリティ・アラートだけでなく、多くの情報源を利用することで、この種の攻撃を検知することができるからです。同社のテクノロジーは、通常の使用状況を効果的に分析し、ベースライン化することで、侵害されたユーザーや認証情報に対して迅速に警告を発します。"