2026 SC Award winner Exabeam — Best AI/ML Data Analytics Security Solution

Exabeam is the winner of the 2026 SC Award for Best AI/ML Data Analytics Security Solution for its Nova autonomous AI system, part of the company's New-Scale Security Operations Platform.

The AI system analyzes data, classifies threats, and automates investigations without depending on analyst prompts, and its six purpose-built agents work together to correlate complex signals and prioritize real threats.

Since its April 2025 launch, Exabeam Nova has been adopted by more than 1,100 customers, including global enterprises, financial institutions, and government agencies that need AI-powered automation to modernize their SOCs.

Now in its third generation, Nova delivers value without adding infrastructure, accelerating time to results. Customers report triage time cut by more than 50%, improved accuracy, and productivity gains of up to 80%.

Nova's proprietary threat-classification model distinguishes between compromised insiders, malicious insiders, and unknown entities with high precision, enabling faster and more accurate decision-making.

The system proactively correlates signals, updated investigations, and learns from case outcomes to improve its threat classification over time. Analysts can view the reasoning behind every classification and decision, increasing trust and speeding resolution.

Based on interviews and financial analysis of Exabeam customers, Forrester found that a composite organization using Exabeam achieved $3.73 million in benefits over three years versus costs of $1.08 million, resulting in a net present value (NPV) of $2.65 million and a 245% return on investment. Exabeam Nova contributes directly to this value by reducing alert fatigue, cutting investigation time, and enabling lean teams to operate with greater precision and speed.

"Exabeam clearly demonstrated real-world adoption at scale with over 1,000 customers," said Vinod Bottu, Senior Software and Platform Engineer at Microsoft. "The agentic approach and explainable AI feel well integrated into existing workflows rather than experimental."