Collibra on Thursday unveiled AI Governance, a new set of capabilities aimed at helping customers more safely and effectively develop and deploy AI models and applications, including generative AI.
Based in New York and Brussels, Collibra is a cloud-based metadata management vendor whose Data Intelligence Platform includes a data catalog. In addition, Collibra enables customers to automate data preparation tasks, measure data quality, and set guidelines for data to ensure its proper use and regulatory compliance.
AI Governance, which is now in preview and expected to be generally available in May, is built on top of the Data Intelligence Platform and applies some of its data governance capabilities to AI.
Given the explosion of interest in AI — and generative AI, in particular, over the last year — AI governance is a rising need for many organizations, according to Doug Henschen, an analyst at Constellation Research.
OpenAI’s November 2022 launch of ChatGPT sparked the recent surge in AI interest.
Since then, many data management and analytics vendors have made generative AI a focal point of their product roadmap while enterprises have begun developing domain-specific AI models and applications to help inform decisions.
“AI is absolutely in need of governance, and metadata management and governance platforms such as Collibra have been addressing this need for a long time,” Henschen said. “The flurry of interest in AI sparked by [the] GenAI craze has magnified the need as organizations will be using more data and creating a greater variety of models as they pursue AI innovation.”
Metadata management, data governance and data cataloging are not new capabilities, he added. However, with vendors developing more tools aimed at data science and enterprises emphasizing data science development, it makes sense for vendors such as Collibra that connect and categorize data to develop functionality geared toward doing the same for AI.
“We’re in the early stages of seeing capabilities specific to the AI development and deployment life cycle,” Henschen said.
Governing AI
Just as data needs to be governed, so too does AI.
When only teams of data scientists and other experts worked with their organization’s data, there wasn’t a pressing need for data governance frameworks. However, as regulations arose on the use of data and self-service BI platforms such as Tableau and Qlik emerged to enable business users to work with data, organizations put in place guidelines that allowed those business users to confidently work with data while simultaneously ensuring data privacy and the organization’s regulatory compliance.
Until recently, AI was almost exclusively the domain of data scientists and other data experts.
Generative AI is changing that.
Large language models (LLMs) have vocabularies as extensive as any dictionary. Therefore, when integrated with data management and analytics platforms, they enable users to engage with data using true natural language rather than code. In addition, LLMs understand intent, can be trained to generate code and can automate processes.
As a result, just as self-service analytics tools enabled business users to analyze data, generative AI technology enables business users to take on tasks that previously could only be done by trained experts. That includes using AI models and applications as part of their normal workflow.
Meanwhile, more regulations related to AI are coming, with the advent of the European Union’s AI Act the most immediate example.
Therefore, just as self-service analytics necessitated the advent of data governance frameworks, generative AI now necessitates development of AI governance measures.
Collibra developed its AI Governance platform to address that need, according to Felix Van de Maele, the vendor’s co-founder and CEO.
He noted that after a year of hype about generative AI, many enterprises are now developing models for specific applications. However, if not done carefully, risks can arise including exposing sensitive data and running afoul of not only existing regulations but also those that might be coming.
“There’s a lot a stake and there are a lot of risks if you don’t do [AI] well,” Van de Maele said. “With the hype, we’re also seeing a lot of recognition of the need to do [AI] well, and governance is critical there.”
In fact, the need for AI governance might be greater than the need for data governance, he continued. With analytics, there’s always a human to validate results before people make decisions and act. But with AI, models make scores of predictions in milliseconds without explanation and are sometimes trained to take action on their own.
“With AI, the problems are the same [as analytics] but the…
Read More: Collibra adds AI governance to data management platform