MIxmode Blog
MixMode Product Updates, Stories on Cybersecurity, AI, and Everything in Between.
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 a no-rules Cybersecurity platform, serving large enterprises with big data environments across a variety of industries. MixMode delivers a patented, self-learning platform that acts as the Intelligence Layer℠ to detect both known and unknown attacks, including novel attacks designed to bypass legacy cyber defenses. This is accomplished in real-time, across any cloud or on-premise data stream. Trusted by global entities in banking, public utilities and government sectors, industry cyber leaders rely on MixMode to protect their most critical assets. The platform dramatically improves the efficiency of SOC teams’ previously burdened with writing and tuning rules and manually searching for attacks. The MixMode platform can be deployed remotely, with no appliances, in under an hour with business outcomes evident within days. Backed by PSG and Entrada Ventures, the company is headquartered in Santa Barbara, CA. Learn more at www.mixmode.ai.