Network Security Blogs
The latest stories on Cybersecurity, AI, and everything in between from MixMode
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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Geoff Coulehan, MixMode’s Head of Strategic Alliances, joined Secrutiny’s “Magnify Podcast,” to discuss the priorities CISOs should focus on to better protect their now-remote team of employees.
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The evidence indicates that these attackers are traditionally specialized in hijacking social media accounts via SIM Swapping.
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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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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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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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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 More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.
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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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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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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.
Read MoreAbout 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.