Gone are the days of traditional datacenter perimeters and defense-in-depth security strategies; we’re living in a world where the enterprise is boundary-less and the attack surface seems unending. A new approach is required to address this security gap, one that approaches the challenge from within.
In the “Market Recommendations” section of the Market Guide, Gartner writes: “Enterprises should strongly consider NTA to complement signature-based and sandboxing detection methods. Many Gartner clients have reported that NTA tools have detected suspicious network traffic that other perimeter security tools had missed.”*
We believe that behavior-based ML detection is the future of security operations. The sophistication of modern threat actors has rapidly outstripped perimeter-based approaches and signature-based detection tools. But while behavioral techniques are important, in our opinion, they are not all created equal. Machine learning is uniquely suited to understanding and detecting anomalous behavior at scale with the ability to rapidly adapt to shifting conditions within the enterprise environment.
Just as in the case of encrypted traffic, you cannot protect what you cannot see. We believe that Gartner’s emphasis on scale is a leading indicator of how organizations will begin to delineate among NTA offerings.One of the key distinctions we believe Gartner draws in the Market Guide for NTA is between automated and manual response.
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