MIxmode Blog

The latest stories on Cybersecurity, AI, and everything in between from MixMode

Getting Ahead of the Adversary with Third-Wave AI

By Christian Wiens | September 14, 2022

In a world where bad actors are capable of building sophisticated AI capable of sidestepping traditional cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are deadly accurate, and can predict an incident before it happens.

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Understanding the Evolution and Impact of AI on Cybersecurity

By Dr. Igor Mezic | August 11, 2022

MixMode’s unsupervised, third-wave AI computes patterns of interaction over many different timescales, contrasting it over the next 5-minute interval with what was seen previously. Should patterns deviate, the platform performs an assessment of the security risk implied in that deviation and presents it to the user.

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Zero-Day Attacks

Updated for 2022: What are Zero-Day Exploits and Attacks and How IS AI Being Used to Combat Them?

By Russell Gray | May 30, 2022

Zero-day attacks are considered the number one cybersecurity threat to company networks large and small. Understand what they are, why it’s so hard to detect them, and how artificial intelligence (AI) is helping to solve this modern problem.

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

By Christian Wiens | November 18, 2021

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.

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What is Anomaly Detection in Cybersecurity?

By Christian Wiens | July 21, 2021

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.

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SIEM Cannot Detect (and Ignores) Zero-Day Attacks

By Christian Wiens | April 29, 2021

Organizations are exclusively depending on selective information forwarded to the SIEM. The information that inevitably exists outside the system of record — information relevant for zero-day attacks — is ignored.

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

By Geoffrey Coulehan, Head of Sales | April 8, 2021

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.

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Maximize ROI with Greater Efficacy Using Unsupervised AI

By Christian Wiens | March 18, 2021

Within the first 24 hours after deployment, MixMode had enabled the government entity to regain control over the security environment and network data infrastructure. No longer limited to log data analysis, they were able to identify and address real-time threats as well as network and operational configuration challenges.

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The Hidden Costs and Challenges of Log Data Storage Using a SIEM

By Christian Wiens | February 24, 2021

Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.

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The Case Against Using a Frankenstein Cybersecurity Platform

By Ana Mezic | October 29, 2020

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.

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About MixMode

MixMode is the first to bring a third-wave, context-aware AI approach that automatically learns and adapts to dynamically changing environments. MixMode’s monitoring platform, PacketSled, better understands network behavior as it adapts to baseline changes and enables both misuse detection and anomaly detection, as well as predictive maintenance. Used by enterprises and MSSPs for real-time network analysis, threat hunting and incident response, the platform leverages continuous stream monitoring and retrospection to provide network forensics and security analytics. Security teams can integrate PacketSled into their orchestration engine, SIEM, or use PacketSled independently to dramatically reduce false positive alerts and the resources required to respond to persistent threats, malware, insider attacks and nation state espionage efforts.

The company has been named an innovator in leading publications and by security analysts, including SC Magazine, earning a finalist award in 2018 and 2019 for "Best Computer Forensic Solution.” Based in Santa Barbara, with offices in San Diego, the company is backed by Keshif Ventures and Blu Venture Investors. For case studies, continuous product updates and industry news, please visit us at www.mixmode.ai.