It’s time for identity and access management (IAM) to grow up. Hackers are getting wise to the ways enterprises commonly approach security and coming up with subtler methods for infiltrating networks. Detecting unauthorized access attempts requires detailed scrutiny of which human monitoring is no longer capable. In response, enterprises are turning to artificial intelligence (AI) technologies, including machine learning (ML), to implement better IAM practices for improving access security and maintaining the integrity of user identities.
The concept of identity has expanded to include not only human users but also devices and applications, creating a challenging situation for those in charge of identity governance. There may be hundreds or even thousands of identities accessing resources across an enterprise network on a regular basis, each with its own unique set of circumstances. The landscape becomes more complex when cloud systems allow users to access networks from any location or device and flexible or remote workers enter the picture. Add access by customers, clients or third-parties to the picture, and consistent enforcement of IAM policies can become difficult or even impossible for IT teams to handle on their own.
Introducing AI puts eyes on everything, all the time, and a machine can detect nuances people can’t. Complex interactivity across the network becomes visible, which enables IT teams to implement smarter administrative actions and make more informed decisions regarding user permissions. Role-based access can be updated to a more nuanced approach with better privileged access management and a lower risk of privileged access abuse at times when temporary permissions must be granted.
Automation and Flexibility
Because AI is able to monitor subtle details of users’ actions, it’s possible to automate authentication for low-risk access situations, thereby offloading some of the burden of IAM administration from the IT department and preventing “security fatigue” among users. AI is capable of looking at the total set of circumstances surrounding access requests, including:
• Device type
• Resources being requested
Considering these details before granting network access makes IAM contextual and granular and can control potential problems caused by improper provisioning or deprovisioning. AI-powered systems are able to apply appropriate IAM policies to any access request based on needs and circumstances so that the IT department doesn’t have to waste time figuring out the basics of “least privilege” for every use case or resolving problems with privilege creep.
Breach Detection and Prevention
Contextual monitoring also reveals anomalies in user behavior, which could indicate malicious intent or breach activity. Machines can handle enormous amounts of data and scan it faster than even the most dedicated IT department is capable of and alert enterprises to abnormal behaviors far enough in advance to prevent serious network compromise or data loss.
Security policies incorporating ML “learn” patterns of user behaviors by observing how different identities interact with enterprise networks. In this way, the system can detect what’s normal and appropriate and what should be flagged as suspect. The process continues around the clock, providing continual monitoring and allowing the ML algorithms to form clearer pictures of routine network activity.
What happens if a hacker gains access to the system with a legitimate user’s credentials? The system picks up on changes in behavior or unusual activities during the session and alerts the IT department or responds automatically by denying access requests.
Going Beyond Compliance
Many enterprises make the mistake of thinking complying with security and privacy regulations is sufficient to keep hackers at bay, but these laws aren’t nuanced enough to meet the security needs of every organization. The basics of compliance involve ensuring information is only accessed by those who need it and shutting everyone else out. However, the specifics of these access requirements differ from industry to industry, and looking to compliance to solve security problems will inevitably leave loopholes.
To complicate the issue, regulations are constantly changing. Implementing compliance rules for new security laws can be a burden, and noncompliance is a common occurrence. The flexible, adaptable nature of AI-powered IAM is useful in these situations. Because AI and ML constantly monitor traffic, learn behaviors and apply granular access controls, enterprises face less of a challenge when enforcing security protocols, and it becomes difficult for hackers to get any use out of stolen credentials.
AI is no longer some vague, futuristic idea nobody can realistically implement, yet 83% of organizations haven’t yet matured the way they approach IAM. Because of a greater degree of interconnectivity, an increasing number of human and device identities and the trend toward global access, enterprises must begin to incorporate smarter technologies into security protocols. When AI and ML are introduced with the appropriate monitoring and reporting tools, it becomes possible to visualize network access and reduce overall breach risk using intelligent, adaptable IAM policies.