See Getvisibility in Action
If you're new to Getvisibility or looking to switch your organisaton to our tailored AI data risk and protection solutions, you can learn more about our Machine Learning innovation, customisation and getting started with our products.
A staff member classifies a document or email with the classification agent by selecting tags for compliance and classification. We also provide a suggested set of tags based on the actual content of the document or email. Staff can choose to use suggested tags or set completely different ones. If they select different tags the GV Classification software evaluates its own knowledge of the document and decides to either learn from the new tags or to generate an audit log if we believe the staff member has made a mistake. This allows for training and identifies staff errors, but crucially enables the GV Classification software to learn from expert users.
Yes that will be possible for the customers to configure their regexes.
Usually no data is needed from the customer. If our Al model reports new document types it has not seen before, we request a small sub-set of sample data (a few hundred files) of these new files, which are immediately converted to an anonymous descriptive number we then use to train and update our model. This will ultimately help in improving the accuracy of our classification results. This process uses none of the actual document data.
GV Classification scans your central registry of permissions, users, groups and access rights. It links this information to the files we find during a scan or that are accessed by staff using their laptops or desktops,
An Office365 plugin is available as well.
Getvisibility Classification provides a single server image thatcan be installed in under 1 hour. Including configuration, you canexpect to be ready to scan file shares in 4 hours. An agent installation takes 1 minute per machine. Once installed the agentconnects to the server and becomes active. Note architecture and sizing vary depending on number of users and file repositorysize.