The world’s first Dynamic Threat Detection Platform
Learn about MixMode’s Dynamic Threat Detection Solutions
MixMode’s use cases for your cyber initiatives
How customers in different industries utilize MixMode
Organizations continue to face increasingly sophisticated cyber threats that require a proactive and adaptive approach to cybersecurity. As the volume and complexity of security data grows, security professionals are turning to advanced AI technologies to enhance their capabilities and stay ahead of emerging threats.
As organizations grapple with the limitations of legacy security controls in the face of increasingly sophisticated cyber threats, integrating AI-driven threat detection and response capabilities presents a compelling opportunity to bolster their defenses.
Gartner just released its Emerging Tech Impact Radar: Security, which looked at technologies that could help organizations effectively detect and respond to attacks and create better efficiencies through AI-based security hyper-automation.
Real-time threat detection is crucial for organizations to effectively maintain their security posture and gain enhanced visibility to defend against ever-evolving cyber threats. However, many organizations face challenges implementing and maintaining effective threat detection capabilities. Legacy rules-based solutions and signature-based threat detection are no longer sufficient to protect against malicious activity. And doing it alone can be costly.
In this buyers guide you’ll learn about the many approaches to AI Threat detection and Response, what to look for, benefits, drawbacks, and everything you need to know when choosing an AI platform for your security operations center.
The financial services industry handles vast amounts of highly sensitive financial information, including customer data, transaction records, and intellectual property, making them a prime target for cyber attacks.
Relying solely on legacy Security Information and Event Management (SIEM) technology is no longer sufficient to protect enterprise organizations from the rising amount of modern, ai-developed, sophisticated cyberattacks. In our newest eBook, we examine the limitations of SIEMs and emphasize the need for an AI-driven dynamic threat detection and response platform.
With the Cloud Era, the IT asset estate has expanded, and there is a diverse set of attack surfaces to defend. The term “attack surface” describes the growing IT asset estate, but there is no parallel term to describe where we can detect or respond to attacker activity.
In partnership with Ravenii, our 60-minute talk was hosted by MixMode’s Head of Sales and Alliances, Geoff Coulehan, CEO of Ravenii, Jeff Shipley, and MixMode’s CTO & Chief Scientist, Igor Mezic. They discussed key topics including:
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.
According to a recent M&A trends survey conducted by Deloitte, 51 percent of 1,000 Executives responsible for Mergers and Acquisitions (M&A) at U.S. companies and private-equity investor firms listed cybersecurity threats as their top concern in executing deals virtually.
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.
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.
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.
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.
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.
The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.
For the past few years, many have been talking about the changing “threat landscape” as it pertains to the increase in zero day, insider and phishing threats. While all of these threats are on the rise, and constitute a concern, there is, perhaps, an even larger shift presenting a threat to enterprises – the shift …
As organizations began to rely more heavily on networking to carry out their operations over the past decade, IT teams added security analyst positions. These professionals focused on network security and providing regulatory compliance oversight. Over time, the role of the security analyst has expanded to include threat hunting tasks. That is, evaluating security platform …
How the Role of the Modern Security Analyst is Changing Read More →
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.