Cobwebs' creative and extensive WEBINT solutions include a web investigation platform, a threat analysis solution, a stable analyst assistant, active web intelligence, a financial investigation platform, and a position intelligence framework.
FREMONT, CA: Based on its latest study of the global demand for artificial intelligence (AI), Frost & Sullivan honors Cobwebs Technologies (Cobwebs) with the 2020 Global Technology Innovation Leadership Award. Its AI-and ML-powered analytics help delivers innovative WEBINT solutions for detailed research and data analysis. It allows clients to search and access data from the open web, deep web, dark web, social media networks, and mobile apps to perform case investigations. Its WEBINT tools allow corporate security, financial institutions, law enforcement, national security and public security agencies to gain insight into clients, workers and partners during the onboarding process.
Cobwebs' creative and extensive WEBINT solutions include a web investigation platform, a threat analysis solution, a stable analyst assistant, active web intelligence, a financial investigation platform, and a position intelligence framework. Its cloud-based and on-site applications gather and integrate data from all web layers to allow operators to incorporate an entire event and relationships between different individuals.
"Its WEBINT search engine is equipped with proprietary AI and ML algorithms that dig beneath the surface web to reach 90% of hidden Internet content. It supports law enforcements' case investigations with data from the deep web and dark web and interacts with threat actors through a secured analyst assistant tool, all while maintaining complete anonymity," stated Danielle VanZandt, Industry Analyst at Frost & Sullivan. "Cobweb's platform scans and monitors myriad sources, including social media platforms and applications, and provides operators with information that meets their search parameters. The WEBINT solutions use AI and ML analytics to list found case information on a relevance scale of positive, neutral, and negative."