Enterprise executives, still enthralled by the possibilities of generative artificial intelligence (genAI), more often than not are insisting that their IT departments figure out how to make the technology work.
Let’s set aside the usual concerns about genAI, such as the hallucinations and other errors that make it essential to check every single line it generates (and obliterate any hoped-for efficiency boosts). Or that data leakage is inevitable and will be next to impossible to detect until it is too late. (OWASP has put together an impressive list of the biggest IT threats from genAI and LLMs in general.)
Logic and common sense have not always been the strengths of senior management when on a mission. That means the IT question will rarely be, “Should we do GenAI? Does it make sense for us?” It will be: “We have been ordered to do it. What is the most cost-effective and secure way to proceed?”
With those questions in mind, I was intrigued by an Associated Press interview with AWS CEO Adam Selipsky — specifically this comment: “Most of our enterprise customers are not going to build models. Most of them want to use models that other people have built. The idea that one company is going to be supplying all the models in the world, I think, is just not realistic. We’ve discovered that customers need to experiment and we are providing that service.”
It’s a valid argument and a fair summation of the thinking of many top executives. But should it be? The choice is not merely buy versus build. Should the enterprise create and manage its own model? Rely on a big player (such as AWS, Microsoft or Google especially)? Or use one of the dozens of smaller specialty players in the GenAI arena?
It can be — and probably should be — a combination of all three, depending on the enterprise and its particular needs and objectives.
Although there are thousands of logistics and details to consider, the fundamental enterprise IT question involving genAI developments and deployments is simple: Trust.
The decision to use genAI has a lot of in common with the enterprise cloud decision. In either case, a company is turning over much of its intellectual crown jewels (its most sensitive data) to a third party. And in both instances, the third-party is trying to offer as little visibility and control as possible.
In the cloud, enterprise tenants are rarely if ever told of configuration or other settings changes that directly affect their data. (Don’t even dream about a cloud vendor asking the enterprise tenant for permission to make those changes.)
With genAI, the similarities are obvious: How is my data being safeguarded? How are genAI answers safeguarded? Is our data training a model that will be used by our competitors? For that matter, how do I know exactly what the model is being trained with?
As a practical matter, this will be handled (or avoided) via contracts, which brings us back to the choice of…
2023-12-20 13:41:02
Original from www.computerworld.com rnrn
