MIxmode Blog
MixMode Product Updates, Stories on Cybersecurity, AI, and Everything in Between.
Featured Content
SOAR - The Acknowledgement that all of your cybersecurity platforms have failed
Is having 5 disparate cybersecurity platforms truly helping make your enterprise more secure? In this whitepaper we explain why it's not and why we believe the adoption of SOAR is just an admission that all of your other cybersecurity platforms aren't doing their job.
Featured Use Case: Why a Large Government Entity Replaced Their SIEM with MixMode
Despite a three-year SIEM deployment and a two-year UBA deployment, government personnel needed an alternative to better detect and manage threats in real-time. They turned to MixMode.
MixMode Guide: The Failed Promises of SIEM
In this whitepaper we'll discuss the ways in which SIEM has failed to deliver on promises made to the cybersecurity industry and why cyber teams must instead turn to a next-gen platform powered by unsupervised AI.
ALL BLOGS
The Security Operations Center (SOC) of today is fundamentally flawed. Currently enterprise cybersecurity spend is higher than ever, but despite multi-million dollar cybersecurity investments, organizations remain vulnerable to attacks. One of the major reasons for this is legacy SIEM deployments. More spend does not equal more security.
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When it comes to advancements in cybersecurity, rule-based systems are holding the industry back. Relying on humans to constantly input and label rules in order to detect and stay ahead of threats is a bottleneck process that is setting security teams up for failure, especially with tools like SIEM, NDR, and NTA.
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Handling and managing data today has become unwieldy for IT teams on multiple fronts, but the security impact is especially troubling.
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The time required for data processing, transition, aggregation, and the normalization does not allow real-time threat detection using today’s SIEM solutions. The only beneficiary of security through log aggregation is the SIEM vendor.
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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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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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The very nature of data is its infinite capacity for growth. For security teams at large, highly integrated and complex enterprises like financial services institutions, that growth can quickly become unwieldy when the approach is to store, normalize and prepare all of this data in order to extract value.
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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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MixMode is proud to release our platform sensor for Google Cloud! With this capability, customers can now monitor their Google Cloud infrastructure for security anomalies and zero-day attacks, using the same industry-recognized MixMode platform enterprises already use for on-premise security monitoring, Amazon Web Services monitoring, and Microsoft Azure monitoring.
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The evidence indicates that these attackers are traditionally specialized in hijacking social media accounts via SIM Swapping.
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.