Cobwebs Technologies WEBINT Platform Enables Enterprises to Obtain Open-Source Intelligence to Enhance Their Risk Detection and Strengthen Their Assets Protection
Press Release (ePRNews.com) - Apr 15, 2021 - Cobwebs Technologies Ltd. announced today that several enterprises have deployed its AI-powered WEBINT platform for their cyber attribution and assets protection.
Enterprises need to protect their digital assets, such as business processes, their proprietary applications, Intellectual property, and online products and services. They need to get valuable, intelligent takeaways for optimal asset security in order to minimize risk and protect their business. This entails assessing the security of corporate digital assets, which requires an open-source intelligence tool for situational awareness in real-time. Cobwebs AI-powered WEBINT platform monitors the surface, deep, and dark web to extract and analyze relevant data to amplify corporate intelligence and security policies.
Once digital assets are breached or stolen, cyber attribution is needed to preserve open-source intelligence data extracted from all layers of the web for evidence. As a critical part of effective online investigations, investigators deploy cyber attribution to collect data and gather evidence, recover deleted files, and access open-source data on all layers of the internet. By adding digital or cyber forensics to an investigation, a combination of best-practice techniques and technology is achieved.
“Our leading web intelligence platform automatically extracts targeted critical insights from big data with advanced and powerful AI machine-learning algorithms. Designed to meticulously race through and scan endless digital channels of the web, our solution analyzes huge amounts of data to help enterprises to protect precious assets,” stated Shay Attias, Founder & CTO of Cobwebs Technologies. “In addition to gathering information, our WEBINT platform has the capability of analyzing content from OSINT websites, such as various social media platforms. The platform utilizes highly sophisticated algorithms in order to identify information on message boards and social media platforms in various languages. It uses social analytics features to gain social media insights, draw conclusions, and provide information that can be used proactively to prevent theft of digital assets before such takes place. Moreover, theplatform presents data in readable forms such as graphs or generate alerts when relevant information crops up to enable faster response times in time-sensitive investigations as well as cases that rely on digital intelligence.”
A growing number of enterprises use Cobwebs AI-powered web intelligence platform for detecting, analyzing, and monitoring threats and risks aimed at their assets and operations. The platform’s predictive analytics and machine-learning algorithms that collect and analyze big data, enable SOC engineers and analysts to take preemptive measures based on full awareness of the threat landscape relevant to their assets. More specifically, enterprises use the aggregated and analyzed OSINT pertaining to threats, risks, and incidents related to their organization and assets. This allows them to get in-depth insights and reports with topic, location, and demographic for smarter, data-driven decisions.
In general, automated threat intelligence technology, such as the WEBINT platform of Cobwebs, can search for and reveal anonymous threat actor details by intricately zoning in on an enterprise’s brand and business performance, followed by analyzing objectives, groups, and locations. This also allows enterprises to gain simplified detection of data breaches via monitoring of darknet marketplaces for stolen data. Moreover, the enterprise will be able to monitor its situational awareness on the open, deep, and dark web with AI-powered data collection using robust technology in real-time. Since such an open-source intelligence tool also employs Natural Language Processing (NLP) algorithms, the enterprise will be able to get insight into the sentiments and context in multiple languages.
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