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.
In our upcoming webinar on Tuesday, May 18th, “Why Your Legacy Cyber Platforms Can’t Defend Against Modern Day Attacks,” CEO of RAVENii, Jeff Shipley joins MixMode Head of Sales and Alliances Geoff Coulehan to uncover why the cybersecurity industry is failing to deliver on its promises.
Unfortunately, the majority of cybersecurity solutions available today rely on outdated applications for AI.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.
The following is an excerpt from our recent whitepaper, “Why Traditional Cybersecurity Tools Cannot Defend Against Zero-Day and No Signature Attacks,” in which we dive into how traditional cybersecurity tools work, why this fundamentally limits them from being able to detect zero-day or previously unknown attacks, why the industry standard for breach detection is around …
What does Bitcoin’s energy consumption problem have to do with AI? At a fundamental level, it turns out, some AI applications require similarly massive amounts of computing energy to function.
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.
Within the first 24 hours after deployment, MixMode had enabled the government entity to regain control over the security environment and network data infrastructure. No longer limited to log data analysis, they were able to identify and address real-time threats as well as network and operational configuration challenges.
What are companies really gaining when they take on SOAR? At a high level, SOAR and legacy platforms are falling far short of their promises. SOCs are left with several pivotal questions.
Staying on top of cybersecurity risk can feel like a losing battle in today’s modern, hyperconnected reality. The influx of IoT devices and increased reliance of BYOD devices has created a diverse, complex threatscape rife with overlapping vulnerabilities across physical and cyber assets.
Most customers are surprised to learn that SOAR platforms rely on invoking 3rd party technologies, including next-generation firewalls and endpoint protection platforms via traditional API calls to isolate and quarantine malicious threats and users.
Every network vulnerability opened new opportunities for hackers to infiltrate systems, steal data and wreak havoc. Several notable security incidents have left governments, private organizations, medical systems and large enterprise networks reeling. Many of these entities have discovered that their security plans are simply not up to the task of mitigating modern cybersecurity threats.
The only truly workable network solution must bridge the inherent gaps that exist throughout [infrastructure] systems. It must also be capable enough to root out hidden vulnerabilities ripe for hacking.
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.
SIEM has failed to meet the needs of enterprises in the modern threatscape. One huge reason for this is that over time, most organizations will come to the sad realization that they will never achieve a full enterprise deployment of their SIEM. By its very nature, SIEM is always “in process.” It’s not unusual for an organization to have an SIEM in process for a full decade.
A large utility company approached MixMode with the following scenario: The enterprise SOC was utilizing a shared SIEM application that was being utilized by several stakeholders: the networking team, the SCADA team, the dev-ops team, the compliance team and cybersecurity teams for “basic search and investigation of log files to meet regulatory compliance requirements”.
Despite a three-year SIEM deployment and a two-year UBA deployment, government personnel needed an alternative to better detect and manage threats in real-time, as well as an improved platform for gathering comprehensive data.
Because the fundamental nature of SIEM requires infinite amounts of data, security teams are forced to constantly wrangle their network data and faced with an unmanageable number of false positive alerts. This means they have to devise efficient ways to collect, organize and store data, resulting in an incredible investment in human and financial resources.
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.
Despite its inherent flaws, today’s SIEM software solutions still shine when it comes to searching and investigating log data. One effective, comprehensive approach to network security pairs the best parts of SIEM with modern, AI-driven predictive analysis tools. Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.
