Zero Trust Security: What Small Businesses Need to Know Explore the solution
Overview 

Understand Behavior. Uncover Intent. Prevent Insider Threats.

NewEvol turns traditional monitoring into proactive insider threat detection by analyzing user actions, access patterns, and behavioral deviations. Using AI-driven UEBA, identity intelligence, and contextual risk scoring, it uncovers misuse and compromised accounts—enabling faster response and reducing internal risks across global organizations and worldwide digital environments.

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use cases

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200+

integrations

Features

Detect Risky Behavior. Protect Critical Assets. Strengthen Trust.

AI-Powered UEBA Models

Continuously learn normal user and entity behavior to detect subtle anomalies, deviations, and suspicious activity.

Identity & Access Risk Scoring

Assess user risk levels based on login behavior, access patterns, privilege usage, and threat intelligence context.

Insider Threat Detection Engine

Identify malicious actions, account takeover, credential misuse, and attempted data exfiltration in real time.

Deep Activity Monitoring

Track access, movement, file activity, cloud interactions, and system changes to uncover suspicious intent.

Benefits

Reduce Insider Risks with Intelligence You Can Trust

Early Detection of Insider Threats

Identify unusual behavior before it becomes a security incident.

Protection Against Compromised Accounts

Spot account takeovers and unauthorized access immediately.

Reduced Investigation Effort

Correlated events and risk scoring minimize manual analysis.

Stronger Access Governance

Gain complete visibility into privilege usage and user actions.

Improved Compliance & Audit Readiness

Maintain traceable records of user behavior and access events.

Region-Specific Behavior Insights

Leverage intelligence fine-tuned for USA, MEA, India, Spain, and Malaysia.

Inside the NewEvol UEBA Dashboard

Every Action, Every Anomaly, Clearly Visualized

Real Insight Into Every User Action.

The NewEvol UEBA Dashboard gives analysts unified visibility into user behavior across systems, applications, and cloud environments. It highlights risk scores, anomalies, and access deviations, while drill-down timelines and identity heatmaps help teams validate alerts, understand intent, and act quickly against internal threats.

How It Works

From Behavior to Insight, Automatically and Continuously

1
Steps

Collect & Baseline

Ingest user activity, identity data, and entity logs to establish behavioral baselines.

2
Steps

Analyze & Detect

Use machine learning to detect deviations, anomalies, and suspicious user patterns.

3
Steps

Score & Prioritize

Assign risk scores using context, intelligence, and historical behavior models.

4
Steps

Investigate & Respond

Trace behavior patterns, validate anomalies, and initiate automated or guided responses.

Redefine How Your SOC Operates.

See risky behaviors early. Act before damage occurs.

Strengthen internal security with AI-driven insights that detect compromised accounts, abnormal actions, and insider threats across global operations and worldwide digital environments.

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testimonials

Hear what our clients say real stories, real impact

This case study highlights the deployment of a secure, machine learning–driven security analytics platform for a government environment operating a fully air-gapped, high-availability SIEM infrastructure. To address strict compliance requirements, offline operations, and the need to detect unknown threats, a UEBA solution was integrated alongside the existing SIEM to support 16 critical use cases using machine learning–based behavioral baselines. The solution enabled selective data collection for only high-priority use cases, ensured full compliance with CIS OS benchmarks and a 24-hour critical patch SLA, and introduced an offline threat intelligence capability with internal enrichment via SOAR. As a result, the customer achieved enhanced anomaly detection, improved threat visibility, and a compliant, scalable security architecture aligned with stringent government security standards.

Case study

Singapore

This case study describes how a government entity partnered with NewEvol to implement a comprehensive cybersecurity management platform to protect a large-scale deployment of connected devices across public environments. Facing challenges related to regulatory compliance, device tampering, and the detection of both emerging and unknown threats, the client required an advanced, intelligent security solution. NewEvol delivered an integrated platform combining SIEM, SOAR, analytics, threat intelligence, and log collection, enhanced with machine learning–based analytics. The solution enabled continuous monitoring, automated incident response, proactive threat intelligence updates, and anomaly detection, ensuring regulatory compliance while strengthening overall operational resilience.

Case study

India
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