Tech Mahindra xMDR
Tech Mahindra xMDR
By TECH MAHINDRA LIMITED
Tech Mahindra xMDR service is designed from the bottom up with common security operations concerns in mind, minimizes the burden of continual technological stack maintenance and enables responsive, cost-effective, and scalable SOC service. We offer seamless integration with the Client's security operations to improve detection, response, and remediation efforts.
Software version
V1
Delivery method
TechM xMDR Our Platforms are enterprise-ready, containerized software solutions that provide an open, faster, and more secure way to move core business applications wherever you need them. Because each of our modules is based on containerized middleware and common software services for development and management, on top of a common integration layer.
Our platform is client patrol on the digital frontier
It empowers with Extended detection and response is a security solution that delivers end-to-end visibility, detection, investigation, and response across multiple security layers. Core components of an XMDR architecture include federation of security signals, higher-level behavioral and cross-correlated analytics, and closed-loop and highly automated responses.
Unified Analyst Experience (UAX)
With Our UAX platform you can predict, prevent, and respond to current threats in a single Unified Console. Our UAX is an open extended detection and response (UAX) ecosystem that integrates NDR, SIEM, UEBA, SOAR and Threat Intelligence, while leaving data where it is, for a holistic approach. Our UAX solution can also offer more automation and AI enrichments at all levels of detection, analytics, investigation, and response.
Advanced Correlation and Threat Hunting
Tech Mahindra xMDR leverages machine learning and artificial intelligence to correlate events from different sources, identify patterns, and prioritize incidents based on severity and potential impact. expand _more This enables you to proactively hunt for threats and uncover hidden connections that might be missed by traditional rule-based detection.