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On the surface, an “incremental stacking” approach to correlative analysis platforms like SIEM, XDR and UEBA is logical. Organizations can overcome some of the inherent limitations present in their security solutions by adding a network traffic analysis (NTA), for example. Industry analysts have been touting this approach for some time now as necessary for full coverage enterprise security.
In what the New York Times is calling, “One of the most sophisticated and perhaps largest hacks in more than five years,” malicious adversaries acting on behalf of a foreign government, likely Russian, broke into the email systems of multiple U.S. Federal agencies including the Treasury and Commerce Departments.
Russian Hack of U.S. Federal Agencies Shine Spotlight on SIEM Failures in Cybersecurity Read More →
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.
The Case Against Using a Frankenstein Cybersecurity Platform Read More →
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 Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.
Improving on the Typical SIEM Model Read More →
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.
3 Reasons Why a Rule-Based Cybersecurity Platform Will Always Fail Read More →
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.
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.
Why a Platform With a Generative Baseline Matters Read More →
While many security solution providers promise to protect your network by establishing a baseline of your network behavior, the definition of “baseline” can vary widely.
Redefining the Definition of “Baseline” in Cybersecurity Read More →
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
Encryption = Privacy ≠ Security Read More →
Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.
New Whitepaper: How Predictive AI is Disrupting the Cybersecurity Industry Read More →
MixMode’s leadership team came together to share more on how our context-aware AI builds network baselines for organizations across the globe, it’s predictive capabilities, and how MixMode helps you more intelligently discover anomalies within your network environment.
New Video: How does MixMode’s context-aware AI build a network baseline? Read More →
In today’s ever evolving cybersecurity landscape there are major problems facing professionals that continue to worsen. These problems center around a shortage of tools advanced enough to understand the baseline of a network in order to pinpoint anomalies and a massive information overload problem in the form of security alerts.
Whitepaper: Actionable Anomalies – How MixMode AI Makes Your Security Data Smarter Read More →
After suffering a possible breach, a client approached the team at Nisos for help evaluating the security of their AWS environment. The client was concerned about possible malicious activity on the part of a former employee who had maintained an AWS Identity and Access Management (IAM) account after being separated.
Featured MixMode Client Success Story: Nisos Read More →
With the right tool, your network data can now provide you with most valuable, actionable alerts in your security stack. What follows is a discussion of some of the reasons why you may want to look to your network first when trying to identify potential threats or attacks.
Network Data: The Best Source for Actionable Data in Cybersecurity Read More →
Companies are expected to spend up to $55 billion dollars on efforts to comply with the California Consumer Privacy Act (CCPA), which is still working out its final rules after going into effect this month.
Staying CCPA Compliant with MixMode’s Unsupervised AI Read More →
This year on the MixMode blog, we have covered headline stories, analyzed every pain point within network security, and shared what we believe to be some of the most innovative solutions to help you analyze network traffic, surface threats and anomalies, and stop attacks using autonomous AI.
Our Top 5 Cybersecurity Insights from 2019 Read More →
The new MixMode & RAVENii whitepaper, “The Evolution of ‘Next-Generation’ Manufacturing and the Need for Network Security,” is a comprehensive look at how third-wave AI is improving modern network security across connected manufacturing networks and beyond.
The Evolution of “Next-Generation” Manufacturing and the Need for Network Security Read More →
This is the final article in a three-part series on Network Baselining. Read the first two articles in the series here:
1) An Introduction to Baselining Technology
2) How to Create a Baseline for Your Network
How to Use Your Baseline for Network Security Read More →
We separate the parts that are wavelike and we separate the parts that are stochastic. Then, five minutes later we look again and compare what we’ve seen historically and what the AI has predicted to happen to the current state of the network. If they are different, that’s an anomaly detected.
How to Create a Baseline for Your Network Read More →