AI agents operate with valid credentials, access sensitive data, invoke tools, and take autonomous actions. When misused, compromised, or poorly governed, they create insider risk similar to trusted human or machine identities.
内部脅威
Detect insider threats across identities, systems, and AI-driven activity. Exabeam connects behavior over time so your team can identify risk early and act before it 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 malicious, compromised, or negligent insiders by learning normal behavior for human and non-human identities. Stateful timelines connect related activity over time to reveal slow-moving risk that point-in-time tools often miss.
すべてのIDを監視する
See Activity Across Every Identity
Monitor user activity, service accounts, machines, and AI agents in a single behavioral view. Native integrations and open agent telemetry extend visibility across major AI platforms, custom agents, and autonomous workflows.
- ChatGPT
- Claude
- Microsoft Copilot
- Google Gemini
セキュアAIエージェント
Extend Insider Threat Detection to AI Agents
Agent Behavior Analytics (ABA) expands user and entity behavior analytics (UEBA) to autonomous activity. It baselines agent behavior and detects misuse, drift, abnormal tool use, risky access, and actions outside an agent’s defined role.
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 may alter or delete logs to conceal activity. Exabeam correlates retained and late-arriving evidence across long-running timelines to reveal gaps, inconsistencies, and behavior patterns that indicate possible tampering.
データ破壊を防ぐ
重要データの異常削除を検出する
Exabeam baselines file and data activity for user behavior and automated processes. It flags unusual deletion patterns and connects them to surrounding behavior, so teams can intervene before operational impact grows.
悪意のある内部関係者を検知する
Expose Credential Misuse
Exabeam detects when valid credentials are used in unusual ways. Behavioral baselines, identity context, and long-running timelines reveal account misuse regardless of whether activity originates from people, systems, or AI agents.
情報漏えいを発見する
Connect Events to Detect Data Leaks
Data leakage often appears as normal activity. Exabeam adds behavioral context to DLP, authentication, access, and application events, so analysts can distinguish routine behavior from suspicious movement or exfiltration.
特権アカウントの監視
Identify High-Risk Privileged Access
Exabeam baselines privileged activity and applies identity, asset, and business context to unusual access. Analysts can detect misuse across administrators, service accounts, and AI agents before it leads to a breach.
特権の昇格を検出する
権限昇格攻撃を阻止せよ
Exabeam monitors credential and permission activity for abnormal escalation. Stateful timelines connect changes over long time periods, exposing gradual or automated escalation attempts that static rules often miss.
データアクセスの不正使用を防ぐ
Identify High-Risk Access to Sensitive Data
Exabeam baselines access to sensitive data across human and non-human identities. Long correlation windows connect small anomalies over time, helping analysts recognize patterns that indicate insider risk.
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 correlates badge, identity, geolocation, and system activity to detect anomalies such as badge misuse or impossible travel. This helps teams connect physical events to broader insider risk.
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？
Exabeam は内部脅威をどのようにカバーしていますか？
Exabeam combines UEBA, ABA, and dynamic risk scoring to detect insider threats across users, service accounts, machines, and AI agents. Long-running timelines connect subtle behaviors that may unfold over weeks or months.
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 analyzes AI agents as trusted identities with access and autonomy. Native platform support and open agent telemetry provide behavioral visibility across major AI platforms and custom agents, helping teams investigate misuse, drift, and suspicious activity.
Does Exabeam map lateral movement to the MITRE ATT&CK® framework?
Yes. Outcomes Navigator maps Exabeam detection coverage to ATT&CK tactics and techniques, including lateral movement methods such as RDP, SMB, SSH, VNC, DCOM, and WinRM. New-Scale Fusion combines behavioral analytics, correlation, cases, and automation to detect and respond.
Can I keep my current SIEM and add Exabeam behavioral analytics to address insider threats?
Yes. New-Scale Analytics can augment SIEMs such as Splunk, Microsoft Sentinel, and IBM QRadar. It adds advanced UEBA and ABA without requiring you to replace your existing SIEM.
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 focus on isolated events, indicators, or short correlation windows. Exabeam uses behavioral baselines, business context, and the stateful Session Data Model to connect activity over time across users, entities, service accounts, and AI agents. This helps analysts detect subtle insider risk that event-based tools miss.
「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。"
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セバスチャン・ビティグ
Head of the Cyber
Defense Center | r-tec IT Security
Exabeamのデモを見る
デモをリクエストして、Exabeamがセキュリティ・オペレーション・チームのエージェント型エンタープライズ・セキュリティ確保にどのように役立つかをご覧ください。
以下の事を学びます：
- 人間とエージェントの行動を監視・分析し、リスクを洗い出す
- 機械が構築したタイムラインで脅威を調査
- マルチエージェントAIを使用して、検知、調査、対応ワークフローを改善する。
- プレイブックを適用して意思決定を導く
- コンプライアンス要件のサポート
受賞歴のあるセキュリティのリーダー
