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Shawn Riley Assumes Integral Role as Chief Data Officer at DarkLight
[March 15, 2017]

Shawn Riley Assumes Integral Role as Chief Data Officer at DarkLight

DarkLight, a cybersecurity analytics and automation platform driven by artificial intelligence, today announced that Shawn Riley has been named Chief Data Officer following his successful eight months as a Senior Advisor to the company. As Chief Data Officer, Riley will drive the product strategy and vision by advancing the Artificial Intelligence (AI) solution to the cyber ecosystem to support security operations, analytics, and reporting.

"Our leadership team was seeking a thought leader who possessed a deep technical knowledge of cyber data and it's massive potential as an asset," said John Shearer, CEO and Co-Founder of DarkLight. "Shawn Riley possesses this very unique skill set, and clearly understands the complex intersection of analytic tradecraft, data and artificial intelligence. We are confident that having him on board will strengthen our product strategy and transform our business."

Riley, a regular contributor to the Science of Security (SoS) Virtual Organization, has two decades of experience in the defense and intelligence communities, initially as part of the U.S. Navy's Cryptologic Community specializing in Information Assurance and Information Operations before transitioning to Lockheed Martin (News - Alert) where he last served as a Senior Fellow and Head of Cyber Intelligence. Just prior to joining DarkLight, Riley spent a year as the Director of Cybersecurity Science at Monsanto Company.

While at Lockheed, Riley was a co-inventor of the "Cyber Threat Intelligence" research project which used AI-based knowledge representation and reasoning to create a "yber Threat Intelligence" knowledge-based solution. Riley continued his pursuit of applying AI to cybersecurity at the Centre for Strategic Cyberspace + Security Science (CSCSS), an international cyber think tank where he has volunteered as an Executive Vice President since 2013 and leads the CSCSS Security Science Directorate. Riley has authored numerous online articles and papers including "Science of Cybersecurity" on how to apply AI-based knowledge representation and reasoning to develop a scientific foundation to the operational cybersecurity ecosystem.

"We've been applying data mining and data science in cybersecurity since the '90s and we started applying machine learning over a decade ago in areas like SPAM detection and filtering. Data mining, data science, and machine learning each approach the problem space in a similar way, with bottom up logic (specific to general). The problem is that each of these solutions is generating a hypothesis that needs to be tested to confirm if it is or is not a valid detection. For example, this is probably malware, this is probably a high-risk user or this is probably some bad behavior, etc." said Riley. "DarkLight's AI takes a completely different approach by applying top down logic (general to specific) and "Sherlock Holmes-style" deductive reasoning that ties the evidence in the data to the analytic claim being made by the AI. This allows DarkLight the ability to receive the wide range of hypotheses coming from different machine learning and other cybersecurity solutions an organization has already invested in and to validate the claim based on the evidence in the same way a human cyber expert would."

Riley's early involvement in the DarkLight technology, and his practical experience using the product, bolsters his ability to enhance the analytics, user experience and machine learning within the DarkLight product line while supporting investor engagements as a product visionary and customer engagements as a domain expert and requirements translator.

About Champion Technology Company's DarkLight

DarkLight is a cybersecurity analytics and automation platform driven by artificial intelligence (AI). This groundbreaking solution is a force multiplier which leverages the logic, knowledge, and reasoning of security analysts to deliver human-quality results, at scale. To learn more, please visit

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