Securboration, Inc.

Securboration Awarded Army Contract for Automatic Redacting Capability

Securboration has been awarded a US Army contract for automatic redaction of information based on each requestor’s allowed access level. Existing Army technologies as well as open source alternatives that are interchangeable will be used to provide a computing environment that allows Warfighters access to data that would normally be inaccessible due to the lack of identity and attribute awareness in these systems. Currently, analysts are unable to keep pace and products are incorrectly marked or over classified. The DOD needs the ability to accelerate intelligence dissemination across security enclaves to support the tempo of modern warfare. Dynamic data redaction and filtering of granular intelligence data based on user attributes is required. The objective is to develop a standards-based Identity and Access Management (IdAM) and Attribute-Based Access Control (ABAC) computing framework that is capable of fined-grained access control of metadata queries and product retrievals from the current system.

Securboration Wins Navy Contract for Course of Action Recommender

Securboration has been awarded a contract from the Navy to build a general framework for a course of action (COA) recommender. Securboration will develop COA Recommendation Services (COARS), which is a service-based implementation which fits well with the Navy’s modular approach to maturing leading edge research towards a deployable COA recommender. Securboration is teaming with Dartmouth College and their renowned computational modeling professor, Dr. Eugene Santos, to specifically address predictive capability for COA recommendations. We will extend Dr. Santos’ research in Bayesian Knowledge Bases (BKBs) by adding deep learning concepts to fuse knowledge fragments and derive complex emergent behavior that is missed with existing reasoning techniques and algorithms. This accounts for the intricacies and dynamics of the operational environment and overcomes the limitations of current COA recommender approaches that are based on simplistic assumptions regarding adversary objectives, limitations, and intent.

Securboration, Inc.