AI-DRIVEN SECURITY OPERATIONS
A Decade of AI Leadership
Machine learning, generative AI, and agentic AI accelerate threat detection, investigation, and data onboarding. The Exabeam Common Information Model (CIM) normalizes data from hundreds of sources to fuel the New-Scale Platform.
- Behavioral analytics for precise threat detection
- Generative AI that boosts analyst productivity
- Agentic AI that gives Tier 1 analysts Tier 3 insight
- Security-centric CIM built for AI and analytics

AGENTIC AI
Automate and Augment Your Team
Six integrated AI agents advise your team, accelerate investigations, and surface deep security insight within New-Scale Fusion. They work together to improve how you manage threats.
- Cut investigation time by over 50%.
- Increase analyst productivity by up to 80%.
- Save three hours per shift through faster triage.

REAL-WORLD AI
AI Focused on Measurable Outcomes
AI-powered analysis drives measurable productivity gains and precise risk detection. A decade of UEBA expertise supports high-fidelity results and helps your team govern AI platform and agent-driven risk.
- Improve coverage with the Advisor Agent.
- Track progress through industry benchmarking.
- Cut MTTR with automated TDIR workflows.
- Create reports that show business risk.
- Use curated data prepared for AI analysis.
より正確な検出
Detect Threats Other Tools Miss
Machine learning baselines behavior and applies business context to highlight risk. New-Scale Fusion uncovers activity other tools miss and enhances how your security operations team responds.
- Detect insider threats, AI abuse, and lateral movement.
- Cut false positives with correlation and analytics.
- Prioritize alerts to show the most significant risk.
- Exabeam Nova inspects every alert for risk scoring.
SAFETY AND SECURITY
AI Security and Strategic Management
Behavioral analytics reveal misuse in AI activity and agent behavior. Outcomes Navigator measures AI security use cases and uses prebuilt dashboards to turn technical detail into strategic conversations about risk and compliance.
- Address unmanaged AI risk with Agent Behavior Analytics (ABA).
- Gain visibility into AI platforms and agents.
- Communicate AI-driven business risk.
ウェビナー機械は学んでいるが、我々は学んでいるか?
今すぐ登録ADVANCED UEBA
Automated Timeline of Risk-Based Anomalies
Behavioral analytics establishes baselines for user and device behavior. Dynamic risk scoring surfaces threats and prioritizes events. Automated timelines show how activity unfolded, and ABA extends the approach to AI agents and non-human identities.
- Build case timelines with the Investigation Agent.
- Create query-based timelines with Search Agent.
- Generate charts from natural language prompts.

AGENT-DRIVEN INSIGHTS
Improve Analyst Skills and Speed Investigations
Agentic AI automates tasks, simplifies queries, classifies threats, and delivers targeted recommendations. These capabilities help your team work faster and improve accuracy throughout detection, investigation, and response.
- Search with natural language through Search Agent.
- Filter, correlate, and classify with Risk Scoring Agent.
- Get targeted guidance from Advisor Agent.
- Group related risks and alerts with Investigation Agent.
異常なユーザー活動の検出
Dynamic User Grouping for Behavioral Analysis
Patented detection grouping uses machine learning to combine related risks into single cases. This approach removes isolated alerts that teams often overlook and helps you see how activity connects during investigations.
- See the full scope of risk through user and entity grouping.
- Reduce unnecessary cases with detection grouping.
サードパーティアラートの優先順位付け
Machine Learning For Alert Triage
Machine learning evaluates rarity, frequency, and risk to organize third-party and internal alerts. Dynamic risk scoring helps your team focus on the highest-priority work for faster threat triage.
- Ingest third-party alerts for more context and precise scoring.
- View multi-vendor alerts in one interface.
- Reduce the number of alerts to triage.
Exabeamのデモを見る
脅威の検知、調査、対応(TDIR)のための業界最強のプラットフォームに関する詳細情報の請求やデモのご依頼はこちらから。
詳細はこちら:
- セルフホスト型SIEMとクラウドネイティブ型SIEMのどちらが適しているか
- クラウドスケールでデータを取り込み、監視する方法
- AIや自動エージェントの行動を監視・分析することで、人間以外の危険な行動を発見する方法
- ユーザーの活動を自動的にスコア化し、プロファイリングする方法
- インシデント・タイムラインを使って全体像を見る
- プレーブックが次の正しい決断に役立つ理由
- コンプライアンスの義務化
受賞歴のあるセキュリティ界のリーダー






