Presented by Jeff Cornelius, EVP
While the security policies implemented as part of a zero trust framework can help prevent predictable attacks, they remain too static to catch the unknown and unpredictable threats that inevitably get through.
By learning normal ‘patterns of life’ from scratch, Darktrace ‘assumes breach’ as well, yet with an adaptive self-learning approach that allows the system to detect, investigate, and respond to unforeseeable cyber-threats that evade zero trust policies, from novel external attacks to insider threats.
Without pre-defining ‘benign’ or ‘malicious’, Darktrace AI learns an evolving sense of ‘self’ bespoke to each organization it safeguards, continually revising its understanding in light of new evidence across cloud, email, endpoints, and the corporate network. This enables the system to spot subtle deviations and novel threats, deliver Autonomous Response actions to interrupt attacks with surgical precision, and investigate and report on the full scope of security incidents.
The zero trust model represents a critical part of any dynamic security strategy today. It is critical that Darktrace can complement, enhance, and natively interoperate
with a zero trust architecture. While this architecture
aims to deliver a pre-programmed measure of secure connectivity, the Darktrace complements zero trust by delivering real-time visibility and adaptive AI defense in the following ways:
AI detections, complete visibility and continuous monitoring
Autonomous Response
Autonomous Investigations
5 сен 2022
