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Research Topic Description, including Problem Statement:
Data, and the insights analysts obtain from it, are crucial for IC agencies to perform their mission. The volume and variety of data are increasing, and they are interconnected so that insights are obtained from the combination of data from many sources. Data classification is traditionally based on the content of the data, although context and metadata may also have an impact on its sensitivity. Typically, classification of the data is based on the potential impact on the national interest, organizations, or individuals if the data is compromised. Classifications range from no business impact for unclassified data to catastrophic impact for top secret data. In some cases, appropriate classification of data is straightforward because either the nature of the data or the way in which it had been collected clearly indicate its level of sensitivity. Increasingly, organizations in the IC are drawing on a variety of data derived from unclassified or low classification sources. In this case, the level of sensitivity of the derived data is not clear, particularly when it is composed of a range of data-types including structured, unstructured, and multimedia data.
The classification level of data has substantial implications for its ability to be shared and analyzed or combined with data from other sources, which can limit its usefulness and the ability of IC agencies to partner with other agencies, industry, or academia. At present the risk-based guidelines do not provide clear guidance on the sensitivity of derived collections of disparate data. Hence, the goal of this project is to use mathematical and statistical principles to establish a framework for classifying disparate collections of security relevant data based on its importance, value, or sensitivity, taking into consideration the need to maximize the availability and usefulness of the data.
Graph networks are widely used for social network analysis. When applied to entities extracted from text-based data, for example, they can help to quantify the amount of information within a given dataset, providing guidelines for the scope of potential damage if different types or quantities of data are compromised. There are already many publicly available datasets that can be used to test these methods and develop principles for the potential impact of a data breach. An important aspect of this work will be to identify the type and extent of damage and to relate that back to statistical properties and characteristics of the data.
Machine learning (ML) and artificial intelligence (AI) can be used to classify the content of data collection into relevant groupings and to identify outliers and anomalies. These methods show substantial promise for analyzing aggregated datasets of disparate data to find the sensitive information they may contain. Applying these methods to disparate collections of data will help quantify the level of risk associated with these collections and inform the appropriate classification of the data.
Historic data breaches and unauthorized disclosures provide an opportunity to evaluate the amount of damage that can be attributed to a given volume and type of data. Methods for evaluating identification of sensitive information stemming for privacy research, as well as methods outlined above, can be allied to these datasets to quantify the probability and extent of compromise for a given dataset (which may depend on the type, volume and nature of the data), providing empirical indicators of damage.
An alternative approach could be to consider the potential level of compromise if sensitive attributes were made available at a low classification (for example if shared between agencies or made available to industry partners) with or without context and in either an open or encrypted form as a reference for AI or ML analysis, or for context based searching.
Relevance to the Intelligence Community:
This is an escalating problem for Intelligence Community agencies as there is an increasing need to collaborate across agencies, and with industry and academia. Higher classification of data restricts its availability, usefulness and hence its value. Moreover, the classification of data has an impact on its use for IoT applications, edge technologies and AI. Having a well-defined set of objective principles for classifying disparate collections of security-relevant data would assist in balancing the risks associated with sharing data against the benefits of sharing the data.
Key Words: Information, Classification, Information Security, Data, Classification Standards, Machine Learning, Artificial Intelligence, Prediction, Analytics, Graph Networks