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Introduction to Machine Learning for TRITON AP-DATA : Knowing when to use machine learning
Knowing when to use machine learning
Machine Learning | TRITON AP-DATA | v8.3.x | 15-Dec-2016
Forcepoint Machine Learning, like any other decision systems that need to handle complicated data, may generate "false positives" (unintended matches) and "false negatives" (undetected matches). The total fraction of false positives and false negatives is sometimes referred to as the "accuracy" of the system.
Since the accuracy of machine learning is derived from the properties of the data and finding the best data sets can sometimes be challenging, you may want to first determine if other types of classifiers, such as fingerprinting or pre-defined policies, can help you classify and protect your data – before considering using machine learning.
A use case in which machine learning could be effective is if you need to differentiate between proprietary and non-proprietary data, like you might find in source code. It may be hard to fingerprint source code that is under constant development and continually changing, and pre-defined policies cannot distinguish between proprietary and non-proprietary source code.
Forcepoint provides several pre-defined content types that address some common use cases, including source code (in C, C++, Java, Perl, and F#), patents, software design documents, and documents related to financial investments. If you need to protect content that belongs to these content types, consider using machine learning, and select the content type that is pre-defined by the Forcepoint system. Machine learning can also be used to complement and enhance fingerprinting and predefined policies and other TRITON AP-DATA detection and classification methods.

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Introduction to Machine Learning for TRITON AP-DATA : Knowing when to use machine learning
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