Feature Spotlight

Machine Learningin AvePoint Compliance Guardian
There are many analysis technologies in AvePoint Compliance Guardian to help decipher unstructured data in
different ways. Used alone or combined together, they can help detect many things – from defined patterns,
keywords to context, and even similarity. There is no substitute, however, for the intuitive way a human can “feel” ashared pattern among documents.
To reduce the instances of false-positives, a new type of check based on machine learning is now available in
AvePoint Compliance Guardian. Machine learning can take sample documents and create an output that canaccurately discover similar documents in new content. This will revolutionize the way that documents are classified
and content is sorted while reducing the human effort required to accurately discover enterprise content.

How It Works

How to Use It

Using sophisticated machine learning algorithms, AvePoint
Compliance Guardian is able to deduce common statistical
patterns within a document, train the system to recognize
these patterns, and then use this information to analyze
similar documents. Building a learned model is as simple as
taking known content and feeding it into the tool. This will
generate a check that will be placed into AvePoint Compli-ance Guardian and can be used to scan new content.

The process of adding a machine learning check to your
AvePoint Compliance Guardian deployment is simple. There
are two primary steps to implementation. You can performthese steps locally on any machine, and there is no need toupload data to the cloud, so your data will never leave yourown environment.

During the training process, the machine learningalgo-rithms will perform multiple iterations of model
calculations with different sample allocation strategies tofind the model that is most accurate. This process will
typically take more computation resources, but after themodel is finalized it can quickly predict new documents.
Keep in mind that, although machine learning is an incredible tool, we must remember that it is not a one-size-fits-allsolution. When it comes to content like personally identifiable information (PII), pattern-based checks remain the bestsolution. However, machine learning is another powerfultool in your toolbox.

Step 1: The first step is to gather training samples. Thesesamples will be used to build out your machine learning
data. Feeding these sample documents into the machinelearning tool will create the analysis that is needed to drive
step two.
Step 2: The second step uses the prediction model to create
the check and predict new documents. This will run in a scan,
and is incredibly quick based on the learned data. The key
here is that you are taking
your document inputs andgenerating discoverable
classifications as an output.
ε3
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These classifications can thenε2
be used in your environment
to generate accurate classifiε1cations, and can be the firststep in a successful data
governance strategy.

The Content Classification Tool in AvePoint
Compliance Guardian

Data pre-processing and selecting positive from negative
training sample files

Building a learning model for prediction

Exporting the learning model to be used as a check in AvePoint
Compliance Guardian for prediction

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