Cyber attacks and breaches have soared recently and security budgets have grown in response. Until now it has been necessary to juggle a multitude of expensive tools from multiple vendors. The industry doesn’t need another point solution to cobble together. It needs a scalable, intelligent central platform, that is fed with pervasive data collection and visibility and armed with advanced artificial intelligence.
Today’s environments consist of physical, virtualized, containerized, public, private, and hybrid clouds. This creates visibility challenges and an unmanageable amount of alert noise. In this state, it is extremely difficult to identify the critical threats and respond to the right ones before data is stolen or other damage is done.
INDUSTRY AVERAGE CYBER BREACH STATISTICS
DAILY SECURITY EVENTS
DAYS TO DETECT
COST TO DETECT (USD)
Think of Aella as the industry’s first Virtual Security Analyst Assistant, powered by advanced AI. Under the covers, Aella delivers Pervasive Breach Detection by using Distributed Security Intelligence™, empowering organizations to proactively detect and thwart attacks on their critical data systems before damage is done. Instead of overwhelming security teams with countless false alarms, Aella intelligently uses multiple Machine Learning techniques to cut through the noise and deliver high-fidelity alerts that enable fast, effective responses, reducing detection and response time from months to minutes. With Aella it’s like having a relentless virtual security assistant on your team.
AELLA AVERAGE CYBER BREACH STATISTICS
DAILY HI FIDELITY ALERTS
MINUTES TO DETECT
COST TO DETECT (USD)
With Starlight’s pervasive breach detection technology, organizations can scale security detection across hundreds of thousands of systems, without blind spots, performance problems, or exorbitant costs. Security analysts can achieve unprecedented breach detection time and accuracy by reducing the time to detect from months to minutes, and reducing the number of alerts seen from thousands to just a few.