Silent Push 6.0 Launches Advanced AI-Powered Cyber Defense Platform

Silent Push, a company specializing in proactive cyber defense, has unveiled Silent Push 6.0, a significant upgrade to its cyber defense platform. The latest version is designed to help security teams act more quickly and efficiently through improved usability, enhanced visibility into threat infrastructure, and expanded integration capabilities. The release introduces upgrades to Traffic Origin and new workflow features that support a threat-informed security strategy focused on identifying and mitigating risks before attacks occur. “Silent Push 6.0 is designed to cut through the noise and deliver actionable intelligence on adversary infrastructure before attacks materialize—something unmatched in the industry,” said Ken Bagnall, CEO and Co-Founder of Silent Push. “This release empowers enterprises and government organizations to identify emerging threats sooner, enabling informed decisions that strengthen cybersecurity defenses at the earliest stages of the attack lifecycle.” The new platform enhancement is built to integrate seamlessly into existing security operations, helping teams focus on high-priority threats without requiring additional tools or major workflow changes. Key Enhancements in Silent Push 6.0 Traffic Origin A newly developed capability exclusive to Silent Push, Traffic Origin provides organizations with deeper visibility into malicious activity, infrastructure shifts, and traffic behavior to accurately identify the true geographic source of internet activity. The feature includes a dedicated user interface, access to a full year of historical data, and more granular permission controls. It supports a range of use cases, including fraud detection, Know Your Customer (KYC) verification, recruitment validation, and incident response investigations. MCP Server Integration Silent Push has introduced a hosted endpoint that allows its threat intelligence data to connect directly with AI platforms such as Claude, ChatGPT, and other large language models. Security analysts can conduct investigations using natural language queries, enabling tasks such as indicator enrichment, historical DNS analysis, infrastructure mapping, adversary clustering, risk assessment, and creation...