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Predictive AI

What is Predictive AI and How is it Being Used in Cybersecurity?

Blog, Network Detection and Response, Predictive AI, Self-Supervised AI, Threat Detection / By

The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.

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Guide: How to Choose an AI-Based Cybersecurity Platform

Blog, Context-Aware AI, Network Security, Predictive AI, Self-Supervised AI, SOC, Unsupervised AI, Whitepaper / By

Most cybersecurity vendors today tout some form of “Artificial Intelligence” as an underlying mechanism for the differentiation of their product among the market. But if everyone is saying they have AI, and everyone is also claiming theirs is the “best,” how can they all be telling the truth?

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Why The Future of Cybersecurity Needs Both Humans and AI Working Together

Blog, Context-Aware AI, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SOC, Threat Detection, Unsupervised AI, Zero Day Attacks / By

A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.

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The (Recent) History of Self-Supervised Learning

Blog, Context-Aware AI, Predictive AI, Self-Supervised AI, Unsupervised AI / By

Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.

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One Thing All Cybersecurity teams Should Have During COVID-19

Blog, Context-Aware AI, Network Traffic Analysis, Predictive AI, Self-Supervised AI / By

COVID-19 has caused most corporate businesses that remain open to shift to a work from home, remote workplace. Because of this, the cybersecurity industry has been turned on its head. Security teams went from monitoring and protecting established network environments to quickly pivoting their tools, resources, and oversight to manage a distributed workforce. This has

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The Cybersecurity Processes Most Vulnerable to Human Error

Alert Reduction, Blog, False Positives, Predictive AI, SIEM, Whitepaper / By

The world’s reliance on fast, reliable, secure networks has likely never been as apparent as it became in early 2020, when the world responded to the Coronavirus pandemic. Suddenly, vast swaths of the global workforce needed to access and send enormous stores of data from home. In some ways, it couldn’t have happened at a worse time.

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New Video: How Does MixMode’s AI Evolve Over Time With a Customer’s Environment?

Blog, Context-Aware AI, MixMode News, Network Security, Network Traffic Analysis, Predictive AI, Unsupervised AI, Video / By

MixMode leaders John Keister, Dr. Igor Mezic, Bryan Elliot, and Russell Gray share how the single algorithm that is the foundation of MixMode’s self-learning AI can understand and continually build a generative baseline of your network without human training.

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New Whitepaper: How Predictive AI is Disrupting the Cybersecurity Industry

Blog, MixMode News, Network Baselines, Network Security, Network Traffic Analysis, Predictive AI, Threat Detection, Uncategorized, Unsupervised AI, Whitepaper / By

Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.

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