This could involve requiring every new model to ship with the “equivalent of a safety and security datasheet” outlining, in detail, the model’s capabilities, known failure modes, and testing history. Extending existing change management processes to AI models is another means of minimizing deployment-related risk, Levy noted.
In addition, Yaz Palanichamy, senior advisory analyst at Info-Tech Research Group, pointed out, data privacy and IP leaks into public-facing AI frontier models are big concerns.
He advised enterprises to take great care to enforce effective acceptable use case policies (AUPs) around AI safety, and to consider establishing an internal AI proof of concept or governance committee. Equally crucially, they should obtain appropriate safety guarantees from their technology and AI vendors.