OpenAI launched generative AI (GenAI) into the mainstream last year, and we haven’t stopped talking about it since – and for good reason. When done right, its benefits are indisputable, saving businesses time, money, and resources. Industries from customer service to technology are experiencing the shift. In fact, a recent study showed a significant increase in GenAI budgets across the board, with close to one-fifth of all healthcare technical leaders witnessing a more than 300% budget growth.
But the hard truth is that many don’t realize those benefits because most GenAI projects fail. There are many reasons for this, ranging from unrealistic expectations to a lack of AI talent to drive the project to concerns with hallucinations and accuracy. While all of these factors are important, there are several identity-specific challenges that make it hard to realize value from GenAI.
Here are several reasons identity leaders may want to think twice before going all in on GenAI initiatives:
Dirty Data
Your GenAI program is only as good as the hong kong whatsapp number data data you feed it. Yet, we know that for a majority of enterprises, identity data is disorganized, messy, and outdated. For example, email (50%) was cited as the most popular option for controlling permissions and entitlements among respondents of a recent survey. The old adage “garbage in, garbage out” holds true, and it’s no different with GenAI. If the inputs are incorrect, the AI-generated results will be too, rendering them effectively useless.
Organizational Silos: One of the biggest challenges for GenAI there have been other investigative applications and IT departments alike is bringing data together from numerous disparate systems – including the aforementioned emails and spreadsheets. Not to adb directory mention, once you get this information, is the data correct? Again, in the case of identity, are all employees still current, in the same position with the same access and privileges as the data reflects?
Complexity of Data Handling
Generative AI requires large volumes of data to function effectively. Identity governance programs handle sensitive and diverse data sets, including personal and access-related information. Ensuring that GenAI models can process and manage this data while maintaining privacy and security is complex and requires significant effort in data anonymization and encryption.