At MixMode, we will look you in the eye when we explain that we know the marketplace is crowded with failed promises of cyber platforms that claim to be “AI-based, self-learning, offering real-time detection and prevention at scale.
MixMode’s Chief Strategy Officer, Matt Shea was invited to provide the opening keynote address, setting the stage for discussions on how businesses and municipalities can better protect their networks and environments from cyber attacks.
In a world where bad actors are focused on building sophisticated AI capable of side-stepping traditional Cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are exceedingly precise, and can detect an incident before it happens.
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.
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.
In our newest case study, “How a Major U.S. City Rapidly Modernized Its Cybersecurity Defenses,” we share how the City cut its cyber tool footprint in half, gained visibility into advanced foreign adversary attacks, and greatly improved the productivity of its SOC staff.
It’s the open secret no one’s talking about — too many cybersecurity solutions in the marketplace stand no chance of providing comprehensive coverage because they are incapable of handling data arising from all sources. Many available solutions are effectively legacy platforms hiding within fresh marketing packages.
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.
Keeping up with security alerts can be a Herculean task without the right tools on board. Security teams face more than 11,000 alerts per day on average, according to industry analysts — including thousands of false positives triggered by legacy security solutions.
MixMode’s Head of Sales and Alliances, Geoff Coulehan, discusses the data retention cost and challenges that arise when legacy cybersecurity platforms require historical data to be organized in a proprietary format for after-the-fact investigation processes.
As Forbes reports in the recent article, “The 7 Biggest Artificial (AI) Trends in 2022,” the World Economic Forum identified cybercrime as “potentially posing a more significant risk to society than terrorism” in 2021.
The much-anticipated fifth generation (5G) of broadband cellular technology has arrived, ushering in unprecedented network speed and connectivity. The tech is also spurring innovation into new tech solutions to meet an ever-growing appetite for instant, reliable connectivity, often, faster than most enterprise Cybersecurity teams can handle. If there was ever a time for AI to deliver on the promises made by Cybersecurity platform vendors, it’s now.
MixMode Inc., a leading global provider of Artificial Intelligence-powered Cybersecurity, announced today that the company was recognized as a supply side innovator in the November 2021 Gartner report: Emerging Technologies: Tech Innovators in AI in Attack Detection — Supply Side.
We recently released a new video to better explain how MixMode’s next-generation cybersecurity anomaly detection platform combines the functionality of SIEM, NDR, NTA and UEBA for advanced threat detection, zero day attack identification, false positive alert reduction, forensic investigation and more.
It’s evident that while organizations are spending more and more on legacy cybersecurity solutions, these platforms are not holding up their end of the deal and are not able to proactively defend in a modern, non-signature attack threatscape.
Unfortunately, the majority of cybersecurity solutions available today rely on outdated applications for AI. So-called first- and second-wave AI solutions don’t cut it, but few vendors have the technical capabilities and know-how to apply cutting edge, third-wave AI to their platforms.
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.
Cybersecurity vendors promise the moon when it comes to AI. As the recent TechRepublic article, “Why cybersecurity tools fail when it comes to ambiguity,” makes clear, often, these promises fail short in real world network environments.
The next cybersecurity challenge lies with the advances in quantum computing that are set to revolutionize tech while simultaneously equipping threat actors with a new arsenal of cyberweapons.
The transition from office to remote environments was abrupt and one of the most defining moments that the cybersecurity industry and professionals faced in 2020. We wrote about the top issues CISOs were facing throughout the year but also doubled down on sharing insights about the evolution of next-generation SOCs, the failure of SIEM platforms as organizations are experiencing them today, and how self-supervised AI fits into the equation.
