The challenges of implementing Generative AI in identity management

One area experiencing this first-hand is identity management. This is the cornerstone of security and compliance at every organization. And while there’s big AI potential for GenAI transformation, there are several key challenges that smart leaders should keep in mind. 

1.) Data quality issues

All AI success is dependent on the quality of the data it processes. Unfortunately, in many organizations, identity data tends to be disorganized, outdated, and inaccurate. For instance, a recent survey found that 50% of respondents rely on email for managing permissions and entitlements. The principle of “garbage in, garbage out” is particularly relevant here; if the input data is flawed, the AI-generated outcomes will be equally flawed and essentially worthless.

2.) Business silos

A significant hurdle for both GenAI applications and IT departments is the integration of data from various isolated systems, including emails and spreadsheets. This integration challenge is compounded by the need to ensure the accuracy of the data. In identity management, this means verifying that all employees are current, in the correct positions, and have appropriate access rights, as reflected in the data.