Traditional security systems work by defining or utilizing rules to train vendor-supplied data models to detect harmful behavior. The problem is that the volume and frequency of the activity being tracked vary greatly. As a result, it’s hard to come up with enough regulations to cover everything. To function and recognize risks, MixMode’s self-learning AI does not rely on or require any rules or criteria. This relieves security or AI teams of the burden of spending thousands of hours designing useless rules and chasing false positives, allowing them to spot risks faster and more precisely.
MixMode is a cloud-native threat and anomaly detection technology for high-volume data streams that uses self-learning AI to discover sophisticated threats in any setting. MixMode is capable of detecting a wide range of known threats as well as new and never-before-seen dangers such as zero-day assaults. MixMode also assists the world’s greatest security teams in increasing efficiency and creativity in the SOC by serving as a system of record to supplement or replace existing security solutions (e.g., SIEM, NDR, NTA, UEBA). MixMode is the first cybersecurity technology with artificial intelligence (AI) capable of detecting zero-day and advanced assaults that have no known signature. Other security solutions that rely on conventional rules-based and signature-based attack detection approaches open up modern assaults and become hard to detect. MixMode is a self-learning AI designed specifically to detect dangers and irregularities in any data stream, at any size. Traditional security solutions such as SIEM, NDR, NTA, and UEBA can benefit from MixMode’s features. MixMode, which was designed specifically for the contemporary SOC or NOC, provides for unprecedented cost reductions and efficiency advantages throughout the whole enterprise. With a single, real-time, AI-driven platform, organizations can automate threat detection and spot unusual user activity. Regardless of data format or kind, MixModes’ sophisticated threat and anomaly detection work efficiently and independently. Threats and abnormalities can be found in network traffic, logs, APIs, time series, cloud data, and other places. In the first hour, MixMode’s Third Wave Unsupervised AI may learn about the network, generate a dynamic baseline, and begin detecting abnormalities. A typical AI takes roughly 18 months to learn the intricacies of a network and can help with anomaly detection and alert reduction.
Using sophisticated threat and anomaly detection, MixMode’s AI adjusts to changing network conditions in real-time, successfully blocking exploitation and zero-day vulnerabilities before they cause damage. MixMode delivers operators the data and answers they need when they need them, at scale, by offering real-time network analysis and automated threat identification, investigation, and action. MixMode provides high-fidelity insight into network and threat behavior with Full-Packet Capture and comprehensive L2-L7 visibility, allowing operators to observe and evaluate all east-west network activity. Cybersecurity MixMode uses the most powerful unsupervised AI to generate a baseline of the particular network activity in as little as 7 days, whereas alternative solutions might take up to 18 months. By integrating threat intelligence with AI-driven anomaly detection, MixMode can discover and reveal new threats and zero-day assaults on the network in real-time, allowing the security team to take action before harm is done.Recently, to fulfill both the SIEM and UBA requirements, MixMode collaborated with a government body to create a next-generation SOC platform based on 3rd Wave Artificial Intelligence.
MixMode is building the next generation of cyber-AI. “Enterprises today utilize cybersecurity systems that are largely reliant upon rules-based approaches, limiting detection to only known attacks. Cybercriminals and adversarial nation-states exploit these legacy systems, exposing organizations to ransomware and novel zero-day attacks, “says John Keister, CEO of MixMode. “We are excited to highlight the importance of going beyond rules-based and intel-based approaches and using advanced AI to address both known and novel threats. MixMode is honored to be recognized in the AI-enabled Attack Detection category. “
John Keister, CEO
“We are excited to highlight the importance of going beyond rules-based and intel-based approaches and using advanced AI to address both known and novel threats. MixMode is honored to be recognized in the AI-enabled Attack Detection category.”