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Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.
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.
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.
The following is an excerpt from our recently published whitepaper, “Self-Supervised Learning – AI for Complex Network Security.” The author, Dr. Peter Stephenson, is a cybersecurity and digital forensics expert having practiced in the security, forensics and digital investigation fields for over 55 years. Section 4 – Why Training Matters – And How The Adversary …
Why Training Matters – And How Adversarial AI Takes Advantage of It Read More →
With MixMode in the center of a program, we will make all the other security investments that you’ve made, better. So when you send data to your SIEM, when you send data to your SOAR, you don’t want those products to be overwhelmed with false positive alerts, with data you don’t need.
Deep learning makes decisions based upon the data it sees and the data that it doesn’t see but infers from what it does see. This became useful in the AV industry when the adversary introduced polymorphic viruses. These are viruses that change their appearance on the fly and not always in the same way.
While SIEM is undoubtedly a step up from unmonitored network environments, the inherent nature of today’s SIEM software often falls short in several important ways. SIEM is an outdated solution for adequately protecting networks within the modern threatscape.
Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …
Whitepaper: Self-Supervised Learning – AI For Complex Network Security Read More →
As organizations began to rely more heavily on networking to carry out their operations over the past decade, IT teams added security analyst positions. These professionals focused on network security and providing regulatory compliance oversight. Over time, the role of the security analyst has expanded to include threat hunting tasks. That is, evaluating security platform …
How the Role of the Modern Security Analyst is Changing Read More →
The world’s reliance on fast, reliable, secure networks has likely never been as apparent as it became in early 2020, when the world responded to the Coronavirus pandemic. Suddenly, vast swaths of the global workforce needed to access and send enormous stores of data from home. In some ways, it couldn’t have happened at a worse time.
MixMode leaders John Keister, Dr. Igor Mezic, Bryan Elliot, and Russell Gray share how the single algorithm that is the foundation of MixMode’s self-learning AI can understand and continually build a generative baseline of your network without human training.
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.
Imagine serving as a CISO for a prominent enterprise and being thrown into a situation where overnight 80 to 100 percent of your workforce moves inside and begins working remotely. The logistics challenges alone are almost too daunting to imagine.
Companies of all shapes and sizes are under siege from a cybersecurity perspective as we navigate the waters of the Covid-19 crisis. As enterprises adjust to a work-from-home population that grew from 20% to greater than 80% of employees almost overnight, CISOs must navigate a radical change in the shape of their corporate networks, and …
Supporting Cybersecurity Programs throughout the Covid-19 Crisis Read More →
As enterprises establish their custom hybrid environments, many security departments are realizing that while they have many sophisticated tools on the on-premise corporate traffic side, they don’t have the same level of visibility of their cloud traffic.
IDC, the premier global market intelligence firm, recently released a vendor report on MixMode’s Next-Generation AI Powered network traffic analysis platform. Ritu Jyoti, Industry AI and Automation Analyst wrote the profile. In it, she examines how MixMode’s AI-enabled, multistream security platform empowers security teams to solve the information overload problem by combining and correlating data …
IDC Report: MixMode – An Unsupervised AI-Driven Network Traffic Analysis Platform Read More →
Today we announced the completion of our Series A round of funding, raising a total of $4 million led by Entrada Ventures. The round included participation from existing investors including Keshif Ventures and Blu Venture Investors. The new funding will be used to expand MixMode’s AI-powered network traffic analytics platform and to fuel the growth of our worldwide network of customers and partners.
Over the past two quarters, MixMode partners, clients, and internal team members have worked together to develop extensive whitepaper and case study resources to share the research, technology, and successes associated with the MixMode platform.
Our engineering team has been working hard to deliver on the promise that MixMode offers a heterogeneous, “single pane of glass” view and analysis into multiple kinds of workloads.
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.