Mixmode Blog
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Recent Ransomware Attacks on U.S. Hospitals Highlight the Inefficiency of Rules-Based Cybersecurity Solutions
A number of recent high profile ransomware attacks on U.S. hospitals have demonstrated the urgency for organizations, municipalities, and critical services to take a proactive approach to protecting networks with a predictive AI solution.
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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, as well as an improved platform for gathering comprehensive data.
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Stop Patching Leaks in Your Cybersecurity Boat: A Streamlined Cybersecurity AI Solution to Adversarial Attacks
At MixMode our one algorithm is capable of catching any anomaly that may appear on the network. In contrast, other security programs rely on a reactive method of patching and constantly adding to their algorithms each time a hack occurs so that the network learns what to look out for.
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How Vendors Capitalize on SIEM’s Fundamental Flaws
Because the fundamental nature of SIEM requires infinite amounts of data, security teams are forced to constantly wrangle their network data and faced with an unmanageable number of false positive alerts. This means they have to devise efficient ways to collect, organize and store data, resulting in an incredible investment in human and financial resources.
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The Case Against Using a Frankenstein Cybersecurity Platform
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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Improving on the Typical SIEM Model
Despite its inherent flaws, today’s SIEM software solutions still shine when it comes to searching and investigating log data. One effective, comprehensive approach to network security pairs the best parts of SIEM with modern, AI-driven predictive analysis tools. Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.
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Webinar Recap: The Failed Promises of SIEM
MixMode teamed up with Ravenii to host a webinar focused on the history and evolution of SIEM platforms, their ideal role in a SOC today, and how they fall short as a threat detection tool in today’s modern cybersecurity environment.
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The Evolution of SIEM
It should be noted that SIEM platforms are exceptionally effective at what they initially were intended for: providing enterprise teams with a central repository of log information that would allow them to conduct search and investigation activities against machine-generated data. If this was all an enterprise cybersecurity team needed in 2020 to thwart attacks and stop bad actors from infiltrating their systems, SIEM would truly be the cybersecurity silver bullet that it claims to be.
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Whitepaper: The Failed Promises of SIEM
The fundamental SIEM flaws lie in the platform’s need for continual adjustment, endless data stores, and a tendency to create an overwhelming number of false positives. When organizations instead turn to a next-generation cybersecurity solution, which predicts behavior with an unsupervised (zero tuning) system, they are poised to save on both financial and human resources.
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