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Introduction to Forcepoint DLP Machine Learning
Introduction to Forcepoint DLP Machine Learning
Machine Learning | Forcepoint DLP | v8.4.x, v8.5.x, v8.6.x
Machine learning is a branch of artificial intelligence, comprising algorithms and techniques that allow computers to learn from examples instead of predefined rules.
Administrators can provide examples that train the Forcepoint DLP machine learning system to help protect sensitive, proprietary, and confidential information. After training, the system creates a classifier to identify documents based on how similar they are to the positive examples provided during the learning process.
There are two main types of machine learning algorithms:
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Supervised learning algorithms
The algorithms are given labeled examples for the various types of data that need to be learned.
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Unsupervised learning algorithms
Data is unlabeled and the algorithms attempt to find patterns within the data or to cluster the data into groups or sets.
Forcepoint DLP machine learning uses both types of algorithms.
This article offers a general introduction to Forcepoint DLP machine learning and explores the types of data that can be effectively protected using machine learning. See:
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Knowing when to use machine learning
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How Forcepoint DLP machine learning works
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Selecting examples for training
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What happens during training
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Accuracy of machine learning
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Using the classifier
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Tuning the classifiers
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Comparison with other types of classifiers

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Introduction to Forcepoint DLP Machine Learning
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