Aetna is one early adopter of machine learning. The health insurance company replaced passwords with a behavior-based security system and some biometric protections in its consumer-facing apps. It put machine learning at the forefront by developing a risk engine that combines multiple pieces of data to authenticate users. This risk engine collects personal information, ranging from the operating system and apps you use to how you text or move within the application. It creates an individual risk score, which can change.

In 2017, the world witnessed a cyberattack of hideous proportions. The WannaCry ransomware attack infected hundreds of thousands of computers in more than 150 countries, throwing a wrench in the digital gears of many businesses and bringing several industries to their knees with malicious software designed to block access to files until a "ransom" was paid. One industry that was hit particularly hard was health care, including organizations such as the National Health Service (NHS) in the U.K. and Merck in the U.S. One study found that last year, 78 percent of health-care providers reported a ransomware or malware attack. And perhaps we shouldn't be surprised: Patient records are filled with valuable and private information, and ineffective cybersecurity measures make it far too easy to hold those records hostage. Health care is an easy target for malware.