ML driven Data Intelligence

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ML driven Data Intelligence

ML Based Data Intelligence

Discover sensitive data across on-prem and multi-cloud by using our 4 step automated process of data asset discovery, classification, linking, and compliance.

Accurate Data Asset Discovery

It is critical for enterprises to classify structured & unstructured data and this is only possible by building a data discovery framework that integrates with a company’s on-premises storage infrastructure’s and also across various multi-cloud servers. By virtue of such intelligent integrations, our automated bot privIC can detect and search for shadow & native data assets across multiple systems/input sources and can consolidate all those data assets into a single centralized point such as an asset catalog.

Harness the power of discovering sensitive and personal data in various data assets

It is an inherent reality that some data is more sensitive in nature and hence it requires more protection, however knowing which data needs to be further protected would continue to remain unclear until and unless the silos around what data exists within an enterprise, where it resides, and it’s sensitivity level is made transparent. Thus, our bot privIC will catalog and classify the discovered data that resides in a centralized point based on sensitivity level, ultimately giving you the enterprise the utmost control over your sensitive data.

The complete A-Z Stack Ameerz utilizes in classifying Data Catalogs

Conform to an agreed scheme and be able to confidently organize the relevant data
Categorize data according to different file types, regulations, formats, retention, and sensitivity
Apply auto-tagging Active Learning technique in order to better identify your enterprise data
Assign risk profiles and scores to your enterprise Data
easily help enterprise employees to look up data attributes in a searchable fashion
Highlight where your sensitive data is present

How privIC and its Intelligent Cloud Computing capabilities help with Data Intelligence integrations

Ultimately our bot privIC is intended to help you search for sensitive data elements across structured systems such as relational databases and unstructured systems such as email for example, and last but not least it will help you populate and manage metadata associated with tables or columns that may be in non-standardized & different file formats e.g. pdf, parquet, csv.

Link Data to its personal owner

Once Data is discovered and classified, it needs to be linked via data linkages to its owner in order to fully enable enterprises to benefit from having access to customer data. However, with stringent regulations coming into play such as GDPR, CPPA, CCPA, and PIPEDA to name just a few, enterprises have to become better custodians of handling people’s data and therefore will need to link sensitive data such as First name, Last name, SIN#, Credit Card #, Health card #, and Bank Account # to its respective owner. Thus, when a customer executes their right of asking an enterprise what data they have of them, and whether they want it deleted, erased, or prohibited to resell, enterprises need to have internal workflows of handling such Data subject request queries, and that is where we can help the enterprise create those internal workflows and be more compliant.

How Ameerz help Enterprises be better custodians of people’s data

As per privacy regulation mandates, enterprises are required to have up-to-date and comprehensive records of their data processing activities. Hence, automating sensitive data intelligence tracking through our bot privIC will allow enterprises to have more credible compliance reports.

You may ask how we improve existing compliance reports?

It’s of paramount importance that sensitive data be made known across the enterprise so we at Ameerz can further help facilitate the process of enabling the enterprise to be better custodians of handling sensitive data. Furthermore, along with handling that data comes the opportunity to leverage automation that would significantly reduce the risk of breaches and human errors that are often associated with manual/mundane tracking of large data volumes.

Anomaly Detection