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87 percent of organizations use network traffic analysis (NTA) tools for threat detection and response according to ESG, an IT strategy firm. In their 2020 study, 43 percent of organizations surveyed said NTA is a “first line of defense” for detecting and responding to threats.
Trying to decipher Cybersecurity jargon can feel like trying to make sense out of a spoonful of alphabet soup. Is your SIEM equipped with sufficient NTA? What about your XDR? Or wait, was it NDR? What’s IRM, anyway? And whatever happened to UEBA?
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 Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.
Geoff Coulehan, MixMode’s Head of Strategic Alliances, joined Secrutiny’s “Magnify Podcast,” to discuss the priorities CISOs should focus on to better protect their now-remote team of employees.
The evidence indicates that these attackers are traditionally specialized in hijacking social media accounts via SIM Swapping.
One thing is clear: more spend does not equal more security and the next generation of cybersecurity tools will route out these inefficiencies.
MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.
Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.
One of the most prevalent issues impacting the effectiveness of security teams who use SIEM as their primary means of threat detection and remediation is the fact that data logs are an attractive medium for modern hackers to exploit.
Traditional security vendors offering solutions like SIEM (Security Information and Event Management) are overpromising on analytics while also requiring massive spend on basic log storage, incremental analytics, maintenance costs, and supporting resources.
While many security solution providers promise to protect your network by establishing a baseline of your network behavior, the definition of “baseline” can vary widely.
While SIEM is undoubtedly a step up from unmonitored network environments, the inherent nature of today’s SIEM software often falls short in several important ways. SIEM is an outdated solution for adequately protecting networks within the modern threatscape.
Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …
Whitepaper: Self-Supervised Learning – AI For Complex Network Security Read More →
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 …
COVID-19 has caused most corporate businesses that remain open to shift to a work from home, remote workplace. Because of this, the cybersecurity industry has been turned on its head. Security teams went from monitoring and protecting established network environments to quickly pivoting their tools, resources, and oversight to manage a distributed workforce. This has …
One Thing All Cybersecurity teams Should Have During COVID-19 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.
The 2020 Clearview AI data breach spawned hundreds of attention-grabbing headlines, and for good reason. The company works closely with law enforcement agencies and other entities by sharing personal information about millions of people, for a variety of purposes. The breach raised many questions about the vulnerability of personal data in general.
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 …