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Forbes Technology Council: Why Large Language Models (LLMs) Alone Won’t Save Cybersecurity

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The star of the moment is Large Language Models (aka LLMs), the foundational model that powers ChatGPT. There are plenty of documented examples of truly impressive feats built on this technology: writing reports or outputting code in seconds. At its core, LLMs basically ingest A LOT of text (e.g., think Internet) as a corpus of training data and rely on human feedback in a type of supervised training called reinforcement learning.

Video: Modernizing Phoenix’s Cybersecurity to Combat Nation-State Attacks

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Shannon Lawson, CISO for the City of Phoenix, and Geoffrey Coulehan, Head of Sales for MixMode recently joined forces for a fireside chat at the Evanta CISO Summit in Phoenix, AZ. Lawson and Coulehan have been partnering together for almost three years to transform the City’s cybersecurity initiatives and Security Operations Center (SOC). 

451 Research Finds Self-Learning Technology to Address Cybersecurity Blind Spots and Reduce Analyst Burnout

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In the report, 451 Research explains why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.

MixMode Recognized as a Supply-side Innovator in AI-enabled Attack Detection Technology by Gartner®

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MixMode Inc., a leading global provider of Artificial Intelligence-powered Cybersecurity, announced today that the company was recognized as a supply side innovator in the November 2021 Gartner report: Emerging Technologies: Tech Innovators in AI in Attack Detection — Supply Side.

What is Anomaly Detection in Cybersecurity?

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Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.

Incremental Stacking of Correlative Analysis Platforms Will Ultimately Prove Ineffective and Costly

Blog, CISO, Network Baselines, Network Security, Predictive AI, Self-Supervised AI, SIEM, SOC, Unsupervised AI / By

On the surface, an “incremental stacking” approach to correlative analysis platforms like SIEM, XDR and UEBA is logical. Organizations can overcome some of the inherent limitations present in their security solutions by adding a network traffic analysis (NTA), for example. Industry analysts have been touting this approach for some time now as necessary for full coverage enterprise security.

The Case Against Using a Frankenstein Cybersecurity Platform

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The cybersecurity market has, simply put, been cobbled together. A tangled web of non-integrated systems and alerts from siloed systems. Enterprises are now being forced to utilize a “Frankenstein” of stitched together tools to create a platform that might cover their security bases.

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

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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.