Bitglass has added Zero-day Shadow IT discovery to its Zero-day CASB CoreTM, expanding Bitglass’ Shadow IT index to more than 100,000 apps, which is more than three times the competition. The technology automatically indexes and dynamically computes reputation ratings for known and unknown cloud applications using machine learning techniques.
“The only constant in the enterprise cloud footprint is change,” said Anurag Kahol, Bitglass CTO. “Bitglass Next-Gen CASB is designed from the ground up to automatically adapt to changes in applications risks, malware threats and user behaviour risks, ensuring superior data & threat protection at all times.”
Zero-day Shadow IT Discovery works by crawling the web, as well as several other curated datasets, to identify, categorise and classify new cloud applications in near real-time as they are discovered on customer networks. This approach delivers results in seconds, versus days, and ensures reporting that is always accurate and up-to-date.
With the new functionality, the Zero-day CASB CoreTM delivers:
Zero-day Shadow IT Discovery – Automatic discovery and classification of hundreds of thousands of unsanctioned cloud applications.Zero-day Unmanaged App Control - Patent–pending automatic detection and control of data leakage paths in any unmanaged application.Zero-day Malware Protection – AI–based known and unknown malware protection, powered by Cylance.Zero-day Managed App Control – Data protection, threat protection, identity and visibility for any SaaS, custom, or packaged software application. No catalog, signatures, or changes to application required.Zero-day Agentless Proxy with AJAX-VM – Provides robust, agentless reverse proxy for any application.
“Just as malware protection vendors were forced to shift from signature-based technology to automated zero-day detection, so too is the case in the CASB space,” said Anoop Bhattacharjya, Bitglass chief scientist. “Discovering and classifying three times more applications than the nearest competitor is a testament to the strength of automated machine-learning approaches over traditional, signature-based technologies.”