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.
MixMode Whitepaper: Authentication Does Not Equal Zero Trust A quick glance at current Cybersecurity vendor offerings will reveal an emerging marketing buzzword: Zero Trust. But it’s important to not dive into a Zero Trust investment until you’re well armed with the fundamentals. What is Zero Trust? Why do you need it? How do you know …
MixMode Whitepaper: Authentication Does Not equal zero trust Read More →
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.
Log4j is the latest example of a zero-day exploit to be discovered and put a big part of the industry into chaos. Given its wide adoption by developers, the impact of the log4j exploit is quite broad and will take a tremendous amount of time to resolve.
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.
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.
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.
A number of recent high profile ransomware attacks on U.S. hospitals have demonstrated the urgency for organizations, municipalities, and critical services to take a proactive approach to protecting networks with a predictive AI solution.
At MixMode our one algorithm is capable of catching any anomaly that may appear on the network. In contrast, other security programs rely on a reactive method of patching and constantly adding to their algorithms each time a hack occurs so that the network learns what to look out for.
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.
MixMode teamed up with Ravenii to host a webinar focused on the history and evolution of SIEM platforms, their ideal role in a SOC today, and how they fall short as a threat detection tool in today’s modern cybersecurity environment.
