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The Gartner® Market Guide for Network Detection and Response sheds light on the key trends and recommendations for security and risk management leaders looking to leverage NDR capabilities. MixMode was listed as a representative vendor within the market guide, offering advanced AI capabilities that enhance network detection and response.
MixMode, a leading provider of network detection and response (NDR) solutions, has been highlighted as a key vendor in Gartner’s 2023 Hype Cycle for Network Detection and Response. This is a significant achievement for MixMode, as it recognizes the company’s innovative approach to NDR and its potential to help organizations protect themselves from cyberattacks.
This guide covers what security professionals need to know about NDR – what it is, its key features, the differences between NDR and XDR, what makes it so effective, and what you should consider before implementing an NDR solution in your environment.
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.
Network detection and response, or NDR, has been established as a key tool for companies seeking to improve their threat response. It has become a network security strategy which developed in response to perceived shortcomings in existing network security systems.
MixMode announced today their inclusion in the 2021 Gartner report, ‘Emerging Trends: Top Use Cases for Network Detection and Response.’ The report, available only to Gartner users, provides in depth analysis on the top four use cases driving the NDR market including detection, hunting, forensics and response, as well as NDR development recommendations for product leaders.
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.
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 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.
It should be noted that SIEM platforms are exceptionally effective at what they initially were intended for: providing enterprise teams with a central repository of log information that would allow them to conduct search and investigation activities against machine-generated data. If this was all an enterprise cybersecurity team needed in 2020 to thwart attacks and stop bad actors from infiltrating their systems, SIEM would truly be the cybersecurity silver bullet that it claims to be.
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.
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.
In a recent blog post, our Head of Customer Success, Russell Gray, outlined the reasons why network data is the best source for actionable data in cybersecurity. He covered the limitations of each of the elements of a typical security stack (SIEM, Endpoint, and Firewall) and the importance of network traffic analysis (NTA) in the …
New Video: Why is network data the best source for actionable data in cybersecurity? Read More →
After suffering a possible breach, a client approached the team at Nisos for help evaluating the security of their AWS environment. The client was concerned about possible malicious activity on the part of a former employee who had maintained an AWS Identity and Access Management (IAM) account after being separated.
Data breaches are expensive. By now, most organizations are well aware of this fact. When it comes to resource planning, however, SecOps teams need concrete data to ensure adequate funding is available to handle a breach.