MIxmode Blog

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

Data Overload Problem: Data Normalization Strategies Are Expensive

By Christian Wiens | September 9, 2020

Financial institutions spend five to ten million dollars each year managing data. A recent Computer Services Inc (CSI) study reveals that most banks expect to spend up to 40 percent of their budgets on regulatory compliance cybersecurity, often adopting expensive data normalization strategies.

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

By Ana Mezic | September 3, 2020

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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Webinar Recap: The Next-Generation AI Powered SOC Platform

By Christian Wiens | July 30, 2020

One thing is clear: more spend does not equal more security and the next generation of cybersecurity tools will route out these inefficiencies.

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Why a Platform With a Generative Baseline Matters

By Christian Wiens | July 23, 2020

MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.

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

By Ana Mezic | July 21, 2020

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 More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.

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NTA and NDR: The Missing Piece

By Christian Wiens | July 14, 2020

Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.

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The Problem with Relying on Log Data for Cybersecurity

By Christian Wiens | July 9, 2020

One of the most prevalent issues impacting the effectiveness of security teams who use SIEM as their primary means of threat detection and remediation is the fact that data logs are an attractive medium for modern hackers to exploit.

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Guide: The Next Generation SOC Tool Stack – The Convergence of SIEM, NDR and NTA

By Christian Wiens | June 30, 2020

Traditional security vendors offering solutions like SIEM (Security Information and Event Management) are overpromising on analytics while also requiring massive spend on basic log storage, incremental analytics, maintenance costs, and supporting resources.

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Whitepaper: Self-Supervised Learning – AI For Complex Network Security

By Christian Wiens | June 4, 2020

Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …

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Encryption = Privacy ≠ Security

By Russell Gray | June 2, 2020

For the past few years, many have been talking about the changing “threat landscape” as it pertains to the increase in zero day, insider and phishing threats. While all of these threats are on the rise, and constitute a concern, there is, perhaps, an even larger shift presenting a threat to enterprises – the shift …

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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.