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In this on-demand audio webinar, featuring IDC Group Vice President Ritu Jyoti and MixMode Chief Scientist and CTO Dr. Igor Mezic, you’ll learn about cyber AI that’s built for the speed and scale of the modern enterprise.
In a world where bad actors are capable of building sophisticated AI capable of sidestepping traditional cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are deadly accurate, and can predict an incident before it happens.
Unsupervised learning actually draws inferences from datasets without labels. It is best used if you want to find patterns but don’t know exactly what you’re looking for.
In the report, 451 Research explains why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.
MixMode’s unsupervised, third-wave AI computes patterns of interaction over many different timescales, contrasting it over the next 5-minute interval with what was seen previously. Should patterns deviate, the platform performs an assessment of the security risk implied in that deviation and presents it to the user.
Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.
Organizations are exclusively depending on selective information forwarded to the SIEM. The information that inevitably exists outside the system of record — information relevant for zero-day attacks — is ignored.
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.
A modern SOC should not be entirely dependent on human operators and their personal experience. The issue has been a foundational problem with not only the methodologies used by SOCs for the past 15 to 20 years, but it should be questioned whether the problem is actually compounded by the technology itself.
Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.
Most cybersecurity vendors today tout some form of “Artificial Intelligence” as an underlying mechanism for the differentiation of their product among the market. But if everyone is saying they have AI, and everyone is also claiming theirs is the “best,” how can they all be telling the truth?
Many CISOs and SecOps teams were faced with a gut-wrenching choice: addressing the operational challenges of keeping workers connected, or shoring up vulnerabilities before hackers exploited them. Both options involved time-consuming, repetitive, manual work.
Since we determine everything on data here at MixMode, we went into our website data to see which of our Q2 articles got the most traffic over the past few months. Not surprisingly, the majority of our top articles covered topics on the advancement of AI in cybersecurity and network traffic analysis (NTA).
Yann LeCun and Yoshua Bengio were recently interviewed by VentureBeat Magazine on the topics of self-supervised learning and human-level intelligence for AI. Our CTO Dr. Igor Mezic sat down with our team to discuss some of the most interesting pieces of the LeCun article, and offer a potential solution to a search for truly self-supervised …
MixMode CTO Responds to Self-Supervised AI Hopes Read More →
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.
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 …