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.
Insider-Bedrohungen
Detect and respond to anomalous behavior from humans and entities and track suspicious activity from AI agents before insider risk escalates.
Demo anfordernVERSTECKTE BEDROHUNGEN ERKENNEN
Aufspüren von Insider-Bedrohungen, die andere Tools übersehen
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.
ÜBERWACHUNG JEDER IDENTITÄT
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
SICHERE KI-AGENTEN
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.
Auditmanipulationen aufdecken
Aufdeckung von Versuchen zur Verschleierung böswilliger Aktivitäten
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.
DATENZERSTÖRUNG VERHINDERN
Erkennung des anormalen Löschens kritischer Daten
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.
BÖSARTIGE INSIDERN ERKENNEN
Missbrauch von Zugangsdaten aufdecken
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.
DATENLECKS ENTDECKEN
Verbinden Sie Ereignisse, um Datenlecks aufzudecken
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.
ÜBERWACHUNG PRIVATKONTEN
Unbefugten Zugriff erkennen, um Sicherheitslücken zu verhindern
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.
ERKENNEN SIE EINE RECHTSAUSSTEIGERUNG
Privilegieneskalation stoppen
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.
DATENZUGRIFFSMISSBRAUCH VERHINDERN
Hochrisikozugriffe auf Sensible Daten identifizieren
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.
PHYSIKALISCHE ZUGANGSSICHERHEIT
Verdächtigen physischen Zugang erkennen
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.
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KontaktHäufig gestellte Fragen
Warum werden KI-Agenten als Bedrohung von innen betrachtet?
Wie deckt Exabeam Insider-Bedrohungen ab?
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.
Überwacht Exabeam KI-Agenten als Insider?
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.
Was unterscheidet Exabeam von SIEM- oder EDR-Tools zur Erkennung von Insider-Bedrohungen?
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.
„Bei 90 % der tatsächlichen Angriffe werden kompromittierte Anmeldeinformationen verwendet, was sehr schwer zu erkennen und abzuwehren sein kann. Wir haben uns für Exabeam entschieden, weil die Tools diese Art von Angriffen erfolgreich erkennen können, da sie nicht nur Sicherheitswarnungen, sondern auch viele Quellen nutzen. Die Technologie analysiert und ermittelt effektiv die normale Nutzung, um schnell auf kompromittierte Benutzer oder Anmeldeinformationen aufmerksam zu machen.“
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Sebastian Bittig
Leiter des Cyber Defense Centers | r-tec IT Security
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