SBIR/STTR Research Award

Automated assessment of disclosure risk

Award identifier 2390aa8931c653cc064ebf75cdd2e346985a04d7e7951455fbbdd90de2cf87afAwarding agency Department of Defense · Air Force

Award amount

$149,999

Program and phase

STTR · Phase I

Award year

2014

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Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
SECURBORATION, INC.
Proposal award date
2013-10-23
Contract end date
2014-07-22
UEI
KMW3TK51HPN9
Solicitation number
2013.A
Topic code
AF13-AT14

Research abstract

ABSTRACT: Information systems continue to progress in terms of collecting, characterizing and assessing information. While this evolution has provided unprecedented intelligence capability to the U.S. and our allies, it has also raised unique challenges in the area of information security and disclosure risks. In particular, the intelligence community (IC) currently lacks the ability to understand how the continuous release of information through approved information sharing, intentional or unintentional leaks, or malicious, covert breaches risks divulging"secrets"either directly or through inference. To address this gap Securboration Inc. proposes an innovative approach that combines and extends Securboration"s semantics-based text analytic summarization pipeline with machine learning and probabilistic reasoning. Our solution, referred to as RIQUEST (Risk Quantification and Estimation Toolkit), will compute the disclosure risk for a given piece of information with respect to a large number of secrets. RIQUEST includes a robust ontology-based model of disclosure risk categories, and quantifies risks within subsets of those categories. RIQUEST addresses the problem of disclosure through probabilistic inference, and takes into account the contextual information that enables inference. BENEFIT: The commercial potential for RIQUEST is significant. Big-four auditing firms are aggressively pursuing data loss prevention (DLP) as a business line for their established customers. The process of identifying an organization"s sensitive data and understanding the risk of its exposure is a critical first step in any DLP business model. RIQUEST"s ability to automate this process represents a significant improvement over the manual data classification exercises currently employed. Whether in commercial enterprises or the Air Operations Center, RIQUEST will provide cumulative, continuous assessment resulting in greater disclosure risk situational awareness which, in turn, leads to improved information sharing and the ability to focus security countermeasures in response to leaks.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

113 SBIR/STTR records share this UEI (2002–2024). Reported award amounts total $64,663,050. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, Department of Health and Human Services

Agency intelligence

Department of Defense

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