Accuracy and Trustworthiness: user identification, access control, and compliance monitoring. GenAI models can sometimes produce inaccurate or unexpected results due to biases in training data or model limitations. Ensuring the AI’s decisions are reliable and trustworthy enough for critical governance tasks is a significant challenge.
Regulatory Compliance: Identity governance programs must adhere to strict regulatory and compliance standards, such as GDPR, HIPAA, and others. Integrating GenAI into these programs necessitates rigorous compliance checks and auditing capabilities. The AI must be transparent and explainable, which is often difficult to achieve with complex generative models, potentially leading to regulatory issues.
The moral of the story
It all comes down to the quality and integrity of your greece whatsapp number data data. The single most impactful thing organizations can do, not just for AI, but overall business operations and security, is getting your house clean. Otherwise the results from GenAI won’t be what you expect, likely costing you time, money, and headaches.
There are certainly products and features that can help with this. The first, establish the scope of zero trust Common Service Data Model (CSDM), for example, is a standardized set of terms and their definitions that can be used with all ServiceNow products. The adb directory system acquires all the duplicated platform data across functions such as SecOps, CMDB, and others and organizes it in one central repository. But this takes time, and AI teams still need to dedicate time for data cleansing and governance.
The Solution
The appetite for GenAI is definitely a catalyst for getting your organizational data in a good place. But keep in mind that even data synchronization tools like CSDM – although a great first step – may not be enough. If you’re up for the challenge of leveraging GenAI in your identity program, that’s great. Just make sure you’re dedicating the time and resources to cleaning your data first.