Major brands, such as Target and Home Depot, have fallen at the hands of payment system data breaches over the past couple of years.

It’s safe to say that after the investigations, consumers were upset at the lack of stringent security systems and protocols in place to keep their data out of malicious hackers’ hands. Because of high-profile breaches, the expectation is that the governing bodies will make PCI compliance and regulations even more stringent. Unfortunately, compliance does not equal data security.

Compliance checks today are not continuous or automatic, even in the largest of global enterprises. Traditionally, compliance audits are performed by organizations just once a year when, in reality, they need to be continuous in order to be effective.

And what’s worse, because most of these checks are a manual, mundane process, they require time, repetitive accuracy and a large surplus of resources to complete, something humans historically are not good at doing.

In the financial services industry, a check can be completed one day and an organization can be compliant. But with the amount of data continuously being processed and the way IT infrastructure is rapidly evolving to improve the customer experience, an organization might fail a compliance check just days or even hours later. Much can be attributed from a data breach perspective to the infrequent and arduous systems currently in place for compliance checks.

A simple, or even unintentional change to a network setting can grant hackers access to critical information. But, even as a highly regulated financial services company, this network vulnerability is likely to go unnoticed even after an in-depth compliance check. Furthermore, there are numerous attack vectors that simply cannot be manually monitored due to the high expense and the deficit of cybersecurity staff.

To quickly identify and solve critical security vulnerabilities before critical data is exposed or stolen, financial services organizations need a system that can automatically and continuously surface security holes beyond their annual compliance checks. Organizations need to put their faith in expert systems that are devoted to monitor a multitude of attacks and effectively notify key security personnel when gaps are found so that the organization can act immediately.

Artificial intelligence expert systems have been used to combat cybersecurity threats, mostly to detect malware and breaches as they happen. But organizations are missing out on the true potential of AI.

Highly prized assets in possession of critical data need robust security systems that don’t rely on human expertise and legacy security tools. AI-based predictive systems that employ statistical techniques (e.g., Levenshtein distance), can easily parse out fake sites from the real ones and warn users of phishing threats, as well as flag malicious apps from non-recognized sites before they’re downloaded. AI predictive systems can also flag when network administrators are sharing passwords, or patterns of employees that are frequently browsing malicious websites that could launch a phishing attack against the organization down the line. These systems enable organizations to get ahead of the breaches (prevent them before they happen) versus reacting to them.

As banking and payments move to mobile devices, the bull's-eye has begun to shift to devices as a common target for malicious attacks. We saw this with the recent Android Marcher malware attack, which targeted financial services and banking customers.

Organizations are having trouble getting a handle on mobile device management (MDM) from an internal security standpoint. Employees have been rejecting the installation of traditional MDM software on their bring your own devices (BYOD) due to privacy concerns, meaning there are numerous unprotected devices that hold critical corporate data.

This growing number of unmanaged end points calls for predictive security systems to monitor not only traditional infrastructure, but unmanaged device traffic as well. This requires big data behavior and pattern analysis, advanced analysis that is best suited for an AI predictive expert system to handle.

Compliance is static, not continuous, not automated and doesn’t mean your organization’s systems are secure. Organizations that implement holistic, AI/automated, continuous breach risk will be able to identify data leaks and cyberattacks against traditional enterprise infrastructure and mobile and internet-connected devices before they happen.