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The software industry has hyped up the use of artificial intelligence (AI) for years, and security – especially the identity governance and administration (IGA) sector – is no exception. As such, the question then becomes not whether a software is AI-enabled, but rather, what value is it really providing to businesses? 

Take compliance, for example. Today’s enterprises need to ensure individuals have the right access to the right applications and restrictions, where appropriate. IGA solutions achieve this, and they can leverage AI to automate the process. But the results are only as good as the data, and if there are organizational silos that prevent IT teams from seeing the full scope of access and privileges, AI isn’t going to be much of a help. 

Here’s why: Any IGA solution can spot a user who has too much access to more information they need or should have. But this is only half the battle. Without cohesive, company-wide data available and AI and machine learning technology to process it, it can’t provide insight into potential vulnerabilities associated with access. Without the full picture of where these issues are stemming from – whether it be antiquated IT policies or unknown security threats – what use is it? 

In order to avoid this fate and get the best possible results, enterprises must find a way to bridge siloed data from across their organization. Manual processes and poorly integrated business systems increasingly hinder a company’s ability to respond to threats. Automating identity governance systems is a great first step to combat this. Proper use of automation assists in compliance efforts and gives managers visibility into and control over what levels of access are most appropriate for certain users and groups within a company. 

Once these provisions are in place, IGA solutions can provide a holistic view of who has access to what, and then leverage AI to auto-approve anything that looks right and flag anything that looks amiss. Not only does this help with compliance and security, but it’s a massive timesaver. Instead of having to analyze thousands of access requests, managers review a fraction of that and are able to do it more efficiently. 

This is in part why many organizations turn to IT Service Management (ITSM) platforms to manage their IGA programs. By integrating these capabilities into an organization’s existing systems, IT teams can see the full spectrum of identity and access in the context of all enterprise systems. When this is the case, AI can be applied to many more use cases, and thus, provides better results. Not only does this improve the overall IGA and security of a business, but it has benefits for security operations (SecOps), Governance Risk Management and Compliance (GRC), and incident management processes as well.

For example, IGA can verify active GRC controls during access review and workflow approval and see which policies may conflict with an approval. It can automate the collection of proof that users are adhering to control policies and determine whether further analysis is necessary for auditing purposes. To take it one step further, AI enablement can help correlate access requests with change control items, which ensures access changes are not made in advance of system changes that would render the access change irrelevant.

Additionally, AI-powered IGA can correlate access requests with known security incidents or vulnerabilities that exist on a requester’s machine. This helps SecOps gain visibility into permissions in the event that a user’s machine is compromised. This also enables the immediate suspension of all account permissions, which can stop a breach from spreading to other machines. This is extremely valuable, as time is of the essence when it comes to threat protection.

While better GRC and SecOps protocols are some of the positive byproducts of an AI-driven IGA program, the real value is that it empowers organizations to take a proactive approach to identity governance. Rather than taking a reactive security and compliance stance, or running from fire to fire, teams can leverage the data they already have to make informed decisions and take action to prevent problems before they start.