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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.
Although it is not surprising at all that hackers are taking advantage of the global pandemic —phishing threat reports are always highest when there is some natural disaster happening— we have never before had such an unsafe environment to protect. Here are a few of the most popular malicious acts:
We interviewed MixMode CTO Dr. Igor Mezic to better understand how machines should be protecting themselves with AI, what roles humans play, and how the DoD and the private sector both benefit from rapid innovation in cybersecurity.
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 →
Throughout the tech community, “artificial intelligence” has become a blanket term often used to describe any computing process that requires little human input. Tasks like routine database functions, scheduled system scans, and software that adds automation to repetitive actions are regularly referred to as AI. In truth, AI can play a part in these processes, …
Not All Artificial intelligence is created Equal Read More →
The 2020 Clearview AI data breach spawned hundreds of attention-grabbing headlines, and for good reason. The company works closely with law enforcement agencies and other entities by sharing personal information about millions of people, for a variety of purposes. The breach raised many questions about the vulnerability of personal data in general.
For the past few years, a major problem has been mounting in the cybersecurity industry: a people shortage. Even before the outbreak of the current global pandemic, enterprises were hurting in the cybersecurity hiring department. Companies are struggling to find employable cybersecurity professionals to handle an ever increasing and evolving number of new threats from …
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.
Many companies are scrambling to find a way to better protect their now-remote team of employees, and as they do so, hackers will take every advantage to find the weaknesses in these spread-out company networks.
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.
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.
Phishing scams feeding off of Coronavirus fears have taken advantage of the current state of national emergency to trick concerned citizens into opening malicious PDF’s claiming to help protect them from the disease.
One of the biggest challenges facing enterprise security teams today is information overload created by massive tool sprawl. Many security teams are forced to utilize upwards of 40-50 security tools to monitor their environment which is leading to an overabundance of alerts. Recent reports showed that security analysts on average ignore up to 30% of …