A Real-time Dynamical Threat Detection and Response Platform for Your SOC
The MixMode Platform is an AI-driven dynamical threat detection and response platform that easily integrates with any security environment to detect threats in real-time. At scale.
MixMode uses self-supervised learning to predict network behavior without human input, providing key use cases that enhance security defenses and improve SOC efficiencies.
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Use Case: Cloud Detection and Response for AWS & Azure
With the increasing adoption of cloud computing, organizations face significant challenges in effectively detecting and responding to security incidents within their cloud environments. Traditional security solutions and approaches designed for on-prem infrastructures are insufficient to address the unique risks and complexities associated with cloud environments. Organizations struggle to gain comprehensive visibility, detect unauthorized access or malicious activities, and respond swiftly to mitigate potential damage.
MixMode provides an intuitive cloud threat detection solution capable of ingesting large volumes of diverse cloud data from multiple systems. The MixMode Platform offers continuous monitoring of your environment and correlates CloudTrail traffic with VPC Flowlogs, SIEM logs, and threat intel feeds to detect real-time threats.
With MixMode's patented self-learning AI, the platform autonomously learns, adapts, and evolves alongside your environment, effectively detecting threats that bypass traditional detection methods.
By delivering comprehensive visibility across cloud, on-premises, and hybrid environments, The MixMode Platform ensures the defense of cloud applications and infrastructure against both known and unknown threats.
Use Case: AI-Generated Attack Detection
As artificial intelligence (AI) continues to advance, there is a growing concern about the emergence of AI-generated attacks. Threat actors are harnessing the power of artificial intelligence to develop sophisticated attack techniques that can evade traditional security measures, making them highly elusive and difficult to detect using conventional methods. These AI-generated attacks can potentially cause substantial damage to organizations, compromising sensitive data, disrupting operations, and undermining trust.
MixMode’s patented self-learning AI platform identifies patterns and trends without predefined rules or training, customizing to the specific dynamics of individual networks, rather than relying on more generic machine learning models typically found among competitors.
MixMode AI was designed to identify and mitigate advanced attacks, including adversarial AI. An adversary would need to have a deep understanding of MixMode’s algorithms and processes to evade detection. However, in attempting to learn and replicate MixMode's AI, the adversary's behavior would likely be detected as anomalous by the platform, triggering an alert and preventing any further damage.
Use Case: SIEM Augmentation
Traditional Security Information and Event Management (SIEM) solutions have been the go-to approach for organizations to collect, analyze, and manage security events and logs. However, SIEM solutions often struggle to keep pace with the rapidly evolving threat landscape and the complexity of modern cyber-attacks. They lack advanced threat detection capabilities, leaving organizations vulnerable to sophisticated threats that evade traditional signature-based detection methods. The size and complexity of big data have also made most SIEMs incapable of effectively collecting, analyzing, and correlating data from multiple sources. These limitations hinder timely incident response, increase the risk of undetected breaches, and weaken overall security effectiveness.
The MixMode Platform helps enhance a SIEM’s capabilities by ingesting and analyzing large volumes of data in real-time, automating the threat detection process to drastically reduce false positives and surface relevant threats. This enables analysts to do more with less and save time by focusing on the threats that matter.
The MixMode Platform helps enhance existing investments, maximizing ROI and delivering long-term value. By leveraging AI-driven detection alongside a SIEM, organizations can strengthen their cybersecurity posture, enhance threat detection and response capabilities, and stay ahead of rapidly evolving cyber threats.
Use Case: Ransomware
Ransomware attacks continue to pose a significant threat to organizations, causing financial losses, operational disruptions, and data breaches. Traditional security solutions struggle to detect emerging or sophisticated ransomware variants, especially those with no known signatures or patterns. This can lead to delayed or missed detection, allowing the ransomware to spread within the network.
The MixMode Platform helps organizations adopt a multi-layered approach to ransomware defense. MixMode AI creates an intelligent evolving baseline of your environment to actively monitor real-time data for any deviations or abnormal behavior.
The platform autonomously learns, adapts, and evolves without relying on rules, training, or tuning to detect threats missed by legacy detection methods in real-time.
Use Case: Identity Threat Detection for Okta
Identity threats have become a pervasive and growing concern for organizations across various industries. Traditional security measures often focus on securing infrastructure and data but fail to adequately address the risks associated with compromised user identities. Organizations struggle to detect and mitigate identity-based attacks, such as account takeovers, credential theft, and insider threats. This leaves them vulnerable to unauthorized access, data breaches, and reputational damage, while compliance requirements related to identity protection remain unfulfilled.
Most organizations use identity and access management solutions like Okta but are not using the valuable log data they generate for detection purposes, despite paying for it to be stored.
By utilizing this existing data, MixMode’s Identity Threat Detection for Okta provides real-time visibility into user activity to discover abnormal activity against behavior baselines and policies to detect attacks and lateral movement.
MixMode’s Identity Threat Detection for Okta seamlessly integrates with your Okta environment in minutes to analyze existing log data and monitor for active users, login geolocation, and unusual application access activity.
Use Case: Automated Threat Hunting
Security teams struggle to keep pace with the ever-evolving threat landscape and identify sophisticated threats evading traditional security controls. Traditional threat-hunting methods heavily rely on manual analysis and human intuition, which can be time-consuming, resource-intensive, and prone to human error. This reactive approach leaves organizations vulnerable to undetected threats, resulting in potential data breaches, financial losses, and reputational damage.
The MixMode Platform includes forensic capabilities with full packet capture and file extraction that enables security teams to automatically and proactively hunt for malicious events in their environments.
The MixMode Platform autonomously analyzes data from various sources to identify patterns, anomalies, and relationships between seemingly unrelated events. This contextual analysis helps uncover hidden patterns and enables threat hunters to trace the attack chain, identify the root cause, and understand the extent of an attack.
This helps automate manual processes and allows threat hunters to focus on investigating and take proactive steps to mitigate risks and prevent future attacks.
Use Case: Insider Threat Detection
Insider threats pose a significant risk to organizations, as malicious or negligent insiders can cause substantial damage to sensitive data, intellectual property, and critical systems. Traditional security measures often focus on external threats, leaving organizations vulnerable to internal threats that are difficult to detect and mitigate. Identifying and addressing insider threats is a major challenge for security teams, leading to potential breaches, financial losses, and reputational harm.
MixMode’s dynamical threat detection platform continuously monitors user activity to detect suspicious behavior that may indicate an insider threat.
The MixMode Platform utilizes self-supervised learning to forecast expected behavior and detect potential threats by analyzing network activity and extracting patterns and trends from the underlying time-stamped data without predefined rules or training
By understanding your network's normal behavior, MixMode can identify and surface known or unknown attacks in real-time, providing unparalleled threat detection capabilities and increasing the efficiency and productivity of the SOC.
Use Case: Supply Chain Attack Detection
Supply chain attacks have become a prevalent and significant threat to organizations across various industries. These attacks target vulnerabilities in the interconnected network of suppliers and vendors, making it challenging for organizations to detect and mitigate them effectively. Traditional security measures often focus on protecting the internal infrastructure, leaving organizations vulnerable to supply chain attacks that can have severe consequences, including data breaches, financial losses, and reputational damage.
Attacks on the supply chain target blind sports and can go undetected for months, leaving organizations vulnerable. The MixMode Platform monitors every network aspect, including attached infrastructure, to uncover signs of an attack on your supply chain even before a threat is identified.
The MixMode Platform can be deployed seamlessly to ingest and analyze large amounts of network traffic, including supply chain activities, without relying on rules, training, or tuning by security operators.
The result is a truly autonomous defense system that dramatically enhances the efficiency of SOC teams, delivering tangible business outcomes in a matter of days.
Use Case: Zero-Trust
Organizations implementing a zero-trust security framework face the challenge of ensuring continuous monitoring and threat detection across their networks to maintain high security.
By integrating The MixMode Platform into their zero-trust architecture, organizations can quickly identify and respond to emerging threats.
The MixMode Platform utilizes self-supervised learning to forecast expected behavior and detect potential threats by analyzing network activity and extracting patterns and trends from the underlying time-stamped data without predefined rules or training This proactive approach enables security teams to take immediate action, investigate incidents, and prevent potential breaches before they escalate.