インサイダーの脅威｜エクサビーム

内部脅威検知

脅威を察知するために通常の行動を理解する

Credential abuse enables lateral movement and privilege misuse. Exabeam uses behavioral analytics to model normal activity for human and AI identities, detect meaningful change over time, and prioritize risk before it becomes an incident.

How Insider Risk Develops

攻撃者のテクニックは一貫している

Insider threats emerge as sequences of behavior tied to human and AI identities. Exabeam establishes normal activity, tracks deviations over time, and surfaces high-risk patterns such as lateral movement, privilege misuse, and sensitive data access.

異常な認証とアクセス

Exabeam identifies deviations in authentication and access patterns for human and AI identities. Behavioral analytics correlates activity over time, giving analysts context to understand intent when behavior changes.

ラテラルムーブメント

Exabeam detects lateral movement by correlating behavior sequences over time. It highlights unusual access patterns, including first-time interactions with sensitive systems, so analysts can distinguish attacker activity from normal operations.

権限昇格

Exabeam identifies privilege escalation by detecting shifts in behavior by all identities. Behavioral analytics reveals gradual risk accumulation, even when actions appear legitimate.

特権アカウントの監視

Privileged identities, including human and AI accounts, are frequent targets. Exabeam analyzes behavior to detect abnormal activity, exposing misuse that would otherwise blend into normal operations.

アカウント操作

Exabeam detects unauthorized identity changes, including account creation, deletion, and modification for both human and AI identities. It surfaces suspicious manipulation performed through valid credentials or automated workflows.

データ流出

Exabeam detects data exfiltration by correlating behavior over time. It distinguishes normal usage from malicious activity, including automated transfers performed by AI agents.

攻撃回避

Exabeam detects evasion techniques by analyzing behavior patterns across identities. It identifies attempts to obscure activity, including automated actions that hide or alter evidence within normal workflows.

データ漏洩

Data leakage often resembles legitimate activity from both human and AI identities. Exabeam correlates events into unified timelines, helping analysts determine whether activity is accidental, automated, or malicious.

データ・アクセスの悪用

Human and AI insiders may misuse legitimate access. Exabeam identifies deviations from normal behavior to detect misuse early, even when access appears authorized.

監査の改ざん

Exabeam identifies audit tampering by detecting deviations in identity behavior, including attempts to alter or erase logs through valid credentials or automated processes.

データ破棄

Exabeam detects destructive behavior by identifying unusual deletion patterns and activity sequences that indicate disruption or misuse.

物理的セキュリティ

Exabeam detects suspicious physical access, such as badge activity in multiple locations within an impossible timeframe. These signals reveal stolen or shared credentials used for unauthorized entry.

リスクのある従業員

Exabeam identifies at-risk users by correlating HR signals with behavioral changes. These insights combine with broader identity activity to detect emerging insider risk among human and digital workers.

その他のユースケース・ソリューション

Exabeam provides prebuilt content and automated workflows mapped to critical security use cases. This helps your security operations team address high-priority requirements without added complexity.

使用例

コンプライアンス

Manual processes and disconnected tools make it difficult to meet requirements for regulations like GDPR, PCI DSS, and SOX. Exabeam automates monitoring and reporting so you can validate controls, reduce risk, and simplify audit preparation.

コンプライアンス

使用例

外部脅威

External attackers use phishing, malware, and credential theft to gain access for financial benefit, espionage, or disruption. Exabeam detects activity at each stage of the attack chain and helps your team investigate and respond before impact spreads.

外部の脅威を探る