A recent Compliance Week story on how artificial intelligence could revolutionize compliance depicted how technology firms “are offering software platforms that promise to automate otherwise routine tasks and improve upon fraud detection audits, anti-money laundering protocols, and know-your-customer screening.” With the advent of cyber-security attacks, developers of advanced artificial intelligence security monitoring solutions have also emerged. However, understanding when and how often monitoring solutions should be executed presents trade-offs to be considered.
Legacy approaches to risk monitoring look for recognized threats by known signatures and pre-built event detection logic. Often these standby methods rest on technology confines and as a result are not aligned to business risk. These limitations can lead to serious detection challenges such as “data” overload (missing the important needles), “alert” overload (too many false alarms where all the needles look the same), along with gaps in skills needed to quickly analyze, recognize, and act on a real event. Trying to monitor every transaction or activity (though essential for some compliance and security functions) to manage threats can be just as ineffective as completely locking down all entry points in an attempt to secure everything.
