SBIR/STTR Award attributes
This SBIR Phase II proposal is for continued development of the Persistent AI based Threat Detection (PAIT) system that uses the power of artificial intelligence and machine learning capabilities to detect emerging human-generated threats by monitoring open source, dark web, and security-related data sets.. In Phase II of this project we will work closely with transition partner US Army Training and Doctrine Command to gather requirements, gain access to high quality data, demonstrate PAIT prototypes, and secure a viable transition path for Phase III. PAIT’s innovative approach includes analyzing potentially threatening individuals and groups of individuals in the context of their overall networks of relationships across disparate data sources, using advanced artificial intelligence methods to learn to automatically detect patterns of threatening behavior in the data, and alerting military analysts to identified threats with supporting evidence and analysis that is designed to facilitate immediate action to address the threat. To accomplish this, PAIT includes the following key elements: (1) PAIT fuses data from open source digital media sources and from security-related data sources. Information on potential threats as well as their associations are gathered across data sources, creating a single unified view of the threat, presented in the context of their overall network of relationships: (2) Deep machine learning methods can learn from user-supplied feedback, allowing PAIT to continually improve threat detection performance. Threats are identified and assessed by evaluating several different threat factors associated with individuals and networks of individuals (e.g., ideological radicalization, propensity for violence, proximity to installation, etc.); (3) The PAIT machine learning algorithms do not look at any one aspect of individuals, but instead we employ a wide range of analysis routines to analyze threats from multiple facets.
