Darktrace has enhanced its flagship AI cybersecurity platform with 70 additional machine learning models and over 80 new features.
The Cambridge-based firm was founded by mathematicians and cyber defense experts in 2013 and uses self-learning AI to protect enterprises across all industry sectors.
Machine learning is used to make thousands of “micro-level” decisions in the background as part of Darktrace’s autonomous response technology called Antigena.
Antigena has been improved with 70 new machine learning models to bolster its ability to autonomously neutralise attacks in real-time.
“The hallmark of a great AI solution is the ability to surpass automation to seamlessly blend into users’ everyday work rhythm,” said Jack Stockdale OBE, CTO of Darktrace.
“When developing Darktrace Cyber AI products, our goal is to augment and uplift the security team to make the task at hand more efficient, so the end product is very intuitive and helps users in their workflow journeys.”
Darktrace has made a conscious effort to adhere to XAI (Explainable AI) principles. XAI ensures that humans can access and understand the decisions taken by the AI.
The incident display for Cyber AI Analyst leverages natural language processing “to clearly outline the steps a human analyst would take if analyzing the same activity and highlights a concise incident summary outlining each stage, which is easy to understand and quick to triage.”
Furthermore, the incident display will also highlight all related events such as associated users, destination ports, and protocols used. The complete breakdown of the actions taken by Darktrace’s solution enables a human analyst to delve into any particular incident response.
Another key improvement in this release is to the Enterprise Immune System. Users can now use filters to narrow down incidents that have a particular severity or relate to specific classifications like compliance.
“With the latest release of Darktrace’s Enterprise Immune System, we really kept the user at the forefront of all UX/UI design decisions, from the beginning to the end of the AI product development life cycle,” explained Stockdale.
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