Matthew Hogan
Matt is a technologist and engineering leader with over 20 years of experience spanning space systems, IoT, machine learning, big data, and cybersecurity. He founded Twin Tech Labs in 2018 to bring serious security capability and AI-driven tooling to organizations that don't have the budget or bureaucracy of an enterprise.
Most recently, Matt served as CTO of LifeRaft — an OSINT threat intelligence platform — where he led a team of 31 engineers, grew the company from $10M to $16M ARR, and oversaw a full modernization of the data and search architecture. LifeRaft was acquired by Securitas in March 2026, a successful exit that underscores the quality of the platform and team he built. Prior to LifeRaft, Matt served as Assistant Vice President of Architecture and Engineering for Cybersecurity at John Hancock/Manulife, where he built and managed a global cybersecurity engineering organization responsible for vulnerability scanning, penetration testing, and security platform adoption across the US, Canada, and Asia — within a $20M annual budget.
Matt also spent seven years at MathWorks, where he founded and led the Internet of Things organization, drove the acquisition and integration of ioBridge (ThingSpeak), and built out a cross-functional platform team. Earlier in his career, he led ground control software development for experimental DoD satellites at General Dynamics and built electronic medical record data exchange systems at Scientific Technologies Corporation — giving him a practical understanding of both mission-critical systems engineering and healthcare data requirements that few security consultants can match.
Matt holds a Master's in Informatics from Northeastern University (3.96 GPA) and has completed executive education programs in Cybersecurity (University of Oxford), AI Leadership (MIT Sloan), and Platform Strategy (Section4). He has published on Agile development, ML democratization, and analytics team building, and has been interviewed by CIO.com.
Matt holds a registered patent on AGI model training and intelligent agent architectures — foundational research that directly informs Arca's autonomous investigation and response capabilities. The patent covers methods for training general-purpose AI models that can operate as independent agents within constrained operational environments, with applications to security operations, infrastructure monitoring, and automated decision-making.