Using Big Data Analysis To Protect Against Internal Threats

Legal and compliance teams have a number of strategies they can employ against risky employee behaviors and communications. Cross-disciplinary legal and IT teams are using big-data analytics technologies to analyze a variety of sources to detect signs of malicious activity and intent early on. This approach may save the company expensive fines, reputation damage and costly investigations.

Although a lot of corporate fraud does involve financial transactions manifest in structured records, troves of unstructured data often present a greater threat. Single-case analytics is unable to identify patterns of non-compliance across matters and different data vaults. It’s merely reactive, processing information when directed. If reviewers do not know about a suspicious locus of communications or what to look for, then they won’t know to run an analysis and the threat remains.

Big-data analytics addresses these shortcomings, augmenting the capabilities of existing software and other tools. It can apply accurate predictive algorithms across all the cases a legal team is working on, thereby identifying related data patterns for further investigation. This can significantly reduce manual work, detect risk before it becomes a liability, and reduce e-discovery costs.

Forward-thinking organizations are spending time and money to protect their employees, their brand and their assets from deliberate harm. Legal departments are working with IT and risk management teams to transform traditional ERM from a reactive process into a proactive one that integrates big data analytics with expert human judgment.