Traditionally Electronic Records Management (ERM) has been done with a large amount of human work. This can come from manual data recording and finding where different records are stored as well as checking the legitimacy of records to ensure they are not redundant or obsolete. This is currently being done by a group of employees known as the Records Manager. A Records Manager needs to have a deep understanding of all laws and regulations that govern records within their sector. They then need to use that knowledge to classify every record in some way so it can be found easily, this classification also provides a chronological order of the record’s life. Records Managers also need to know how long a record needs to be stored for and what action must take place when it expires. This management process is time-consuming and manually intensive.

Uses of AI/ML

AI/ML can be used for a variety of ERM tasks such as identifying and classifying metadata and records and separating them into different record-keeping systems; automating and streamlining a variety of compliance activities, including the management of email records, handling requests for records under eDiscovery and litigation holds, document review processes, and identification of files that need retention or retirement.

In this blog post, we’ll look at how AI/ML can be used to manage the records lifecycle, from creation through processing and ultimately retirement or destruction.

AI/ML technologies can be used to make three significant contributions to the ERM program: 1) discover all information assets; 2) classify data based on content and metadata tags; 3) process documents for review, retention, e-Discovery, and destruction.

Using AI/ML technology, we can discover all of the organization’s information assets, including those that may have been previously unidentified or not cataloged. For e.g. we can use AI/ML to analyze the contents of documents that provide greater insights into the analysis of document content when in turn can be used to automatically classify information assets into categories or buckets. These categories can then be prioritized for review by specific individuals within an organization based on AI/ML’s analysis of content and metadata tags. This greatly speeds up the compliance process.

AI/ML can be used to apply metadata tags and automatically sort and tag documents based on AI/ML’s analysis of the content and metadata. For example, AI/ML technologies can provide valuable information about what tags or keywords to use during an e-Discovery process or assist with compliance by identifying potential issues such as unneeded records retention or redacted email messages. It also enables a better ranking of records for review, which in turn reduces the cost of e-Discovery while increasing accuracy. We can also use AI/ML to identify duplicate records and flag them for deletion, greatly accelerating the process of identifying unneeded files for deletion or retention.

Finally, AI/ML can be used to automate data masking and redaction. AI/ML masks information assets in a privacy-compliant way through its use of AI and can learn what content is sensitive and automatically remove or mask that information in the document.

For more information on how we can help with your organization’s ERM requirements compliance, please reach out to info@neevsys.com

Author: Shainan Patel, Managing Director, NeevSys Inc.