Your colleague secretly sends an IM to a peer discussing plans to start a competitive business venture. A disgruntled employee discloses plans via Facebook to launch a new software product. An employee who has recently left the company takes valuable IP with them. Legal and compliance risks to your company are brewing… and there’s no better place to uncover them then in your employees’ and company’s data.
How can legal and compliance teams protect their company from rogue employees? LegalIT Insider’s recent “The Danger Within: How to Protect Your Company from Your Own Employees, Using Nothing but Data Trends,” discusses how leading organizations are going beyond traditional approaches to enterprise risk management to detect signs of noncompliance early on, potentially saving the company fines, reputational risk and litigation. Below is an excerpt:
“In litigation, investigations and regulatory compliance matters, legal counsel are increasingly relying on analytical tools such as technology-assisted review, concept searching, e-mail threading and relationship analysis, to name a few, to quickly winnow down data volumes and find meaningful patterns in data sets. While effective when used appropriately, these analytics are used on a case-by-case basis, requiring that legal teams reinvent the wheel with each new matter. This is because there is limited (or no) knowledge transfer from case-to-case or even within single cases involving multiple law firms and vendors, so attorneys often re-review the same documents over and over again. This process is not only costly and inefficient, but prone to inconsistencies, risk of misclassifying or inadvertently exposing trade secret, privilege, private or other sensitive data.
Enter ‘big data’ analytics. Organisations that take a holistic ‘big data’ approach to documents across all legal and compliance cases by continuously combining the intelligence they have generated in prior cases with new information collected as matters arise can gain even more insights from their data. Emerging analytics platforms can amass billions of previously reviewed and classified records, across internal and hosted third-party platforms, into a unified repository. This collective data history gives counsel the ability to repurpose their past work and view new data through a historical lens. Once this information is assimilated, the power of these platforms can reorganise data and extract unexpected trends and relationships for future matters.
With the collaboration of subject matter experts and data scientists, organisations can customise multiple algorithms to detect and monitor specific types of regulatory and legal risk across all types of data, including e-mails, voicemails, social media messages, word processing files, video and even structured data. More importantly, they can mine these records using a veritable arsenal of traditional analytics tools, including text analytics, natural language processing, sentiment analysis, machine learning, statistical learning, anomaly detection, and audio analytics. These tools yield both diagnostic and predictive analytics by organising data from disparate sources and custodians into an informative array that organisations can use to identify red flags that point to illicit or negligent behaviour.”
By getting ahead of bad behavior using predictive and prescriptive insights into unstructured data, legal and compliance teams can better manage risk and reduce costs before undetected risk turns into costly penalties, fines and litigation.