AI-Driven Identity Security Blog

Historically, identity security was not discussed on an executive level. But thankfully, that has changed. This shift was apparent throughout the sessions on the expo hall floor and in many of SailPoint’s customer conversations held at the Gartner IAM Conference two weeks ago.   

From user onboarding and access/change requests to certifications, password resets, and stopping risky access – already overloaded security and IT teams are facing mounting responsibilities concerning organizational and change management. One might say these responsibilities go beyond what’s humanly possible to perform successfully.  

“Today, as never before, organizations need to perform IAM tasks quickly and accurately, with maximum security, but with the least annoyance to users and minimum disruption to the business.”¹ How can Security/IT resources achieve this conflicting balance? 

Having been a business manager myself, I know well about certification fatigue when it comes to identity security and access control. A few times a year, the managers get the list of entitlements to review for his/her team. Without context, many of them are just rubber stamping and approving everything, fearing that they may accidentally revoke the needed access for the employees. As a result, overprovisioning and noncompliance happen. What if the managers are provided with insights to make informed decisions? How about less certification and more revocation?

What if you could discover risky identities within an organization as easily as you search for your next favorite song on Spotify? Leverage SailPoint’s security expertise to surface Outlier identities. Press “remediate” or “ignore” to train the AI/ML to adapt to your organization’s unique security preferences.

Security professionals, identity managers, and IT operations teams are under growing pressure to keep workers productive while ensuring their organizations are protected from security threats. In today's dynamic IT environment, a human-based identity security approach can no longer keep up. Organizations are increasingly turning to artificial intelligence (AI) and machine learning (ML) to address these challenges.