内部脅威

Abused credentials from a trusted insider, a compromised account, or an AI agent look like legitimate activity. Signatures and rules can’t spot the threat. This means security teams have no visibility into attacks from within.

デモのリクエスト

INSIDER THREAT DETECTION

Understanding Normal Behavior to Find the Abnormal

Credential abuse plays a critical role in breaches, giving attackers a fast path to lateral movement and privilege escalation. Behavioral analytics is the most effective way to stop it. By using AI to baseline normal activity for every user and device, Exabeam detects anomalies, scores risk in real time, and helps analysts prioritize and respond to credible threats.

インサイダー脅威の行動

While indicators of compromise (IoCs) change with every attack, attacker behaviors (TTPs) remain consistent. Relying on rules, statistics, and signatures alone isn’t enough.

Organizations need a reliable method to detect, investigate, and respond to insider threats. Exabeam uses AI to baseline normal behavior across all users and entities. This enables security teams to detect lateral movement, privilege escalation, tampering, account manipulation, data destruction, data exfiltration, and more.

異常な認証とアクセス

Exabeam detects abnormal authentication and access patterns from users and monitors the activity of automated agents to provide full context for investigations.

横の動き

Detect attacker techniques like Pass-the-Hash and Pass-the-Ticket. Exabeam applies behavioral analytics to contextualize anomalous activity—such as first-time access to a critical server—and distinguishes attacker TTPs from normal user and entity activity.

権限昇格

Attackers escalate privileges to gain access to critical assets. Exabeam detects techniques like credential enumeration and BloodHound execution by identifying abnormal behavior from users and providing visibility into the actions of automated processes.

特権アカウントの監視

Attackers target privileged accounts to bypass security controls and exfiltrate data. Exabeam detects this activity by applying behavioral analytics to human identities and providing deep monitoring of non-human accounts to detect suspicious activity.

アカウント操作

Exabeam detects unauthorized changes in Active Directory, including account creation, deletion, or modification. It also surfaces attempts to conceal actions using misused service and agent identities.

データ流出

A standalone data loss prevention (DLP) alert lacks context. Exabeam adds behavioral context to DLP alerts to identify compromised and malicious insiders. By monitoring activity from multiple sources, Exabeam helps detect suspicious data exfiltration through DNS, email, or web uploads, or from AI agent data transfers.

攻撃回避

Exabeam detects evasion techniques such as audit log tampering and file destruction. Behavioral analytics reveal malicious intent from users, while centralized logging provides the visibility needed to investigate when autonomous AI agents attempt to conceal their activity.

データ漏洩

Data leakage often resembles normal behavior, making it difficult to detect. Exabeam combines DLP alerts with authentication, access, and contextual data into a complete timeline. This helps analysts determine if a user, entity, or agent is acting maliciously.

データ・アクセスの悪用

Malicious insiders may abuse privileges to access sensitive data. Exabeam identifies this abuse by baselining normal user activity to detect meaningful deviations and by providing detailed monitoring of agent activity to help analysts spot potential misuse.

監査の改ざん

Attackers often tamper with or clear logs to cover their tracks. Exabeam adds business and identity context to user anomalies and all non-human activity, helping analysts accurately identify tampering regardless of the identity used.

データ破棄

A malicious insider may destroy critical data to disrupt operations. Exabeam baselines file and data activity to flag abnormal deletion patterns across all users.

物理的セキュリティ

Exabeam detects suspicious physical access, such as an employee badge being used in two locations in an impossible timeframe. This can indicate a shared or stolen badge being used for unauthorized access.

リスクのある従業員

Exabeam identifies at-risk users by correlating HR data with activity patterns, such as communication with competitors or unusual data access, that may indicate an employee is preparing to leave the organization.

その他のユースケース・ソリューション

Exabeam provides prebuilt content and automated workflows that map to your most critical security use cases.

使用例

コンプライアンス

Manual processes and disparate tools make it difficult to meet regulatory requirements like GDPR, PCI DSS, and SOX. Exabeam automates compliance monitoring and reporting to reduce risk and simplify audits.

コンプライアンス

使用例

外部脅威

External attackers use phishing, malware, and other techniques to breach your organization for financial gain, espionage, or sabotage. Exabeam detects and responds to the entire attack chain, from initial compromise to final exfiltration.

外部の脅威を探る