Unlocking the Secrets to Building AI That Navigates the Twists of a Shifting IP World
Independent research shows governance gaps are systemic. A joint review by MIT, CSAIL, and MIT FutureTech finds that even the most comprehensive AI risk frameworks miss roughly 30% of known risk categories. MIT Sloan experts conclude that AI adoption has “exceeded the operational capabilities of most organizations,” leaving risk programs underdeveloped.
Recent high‑profile litigation underscores the exposure created by ungoverned IP and copyright practices in AI workflows. A Harvard Law Review analysis of The New York Times v. OpenAI details the Times’s allegation that OpenAI and Microsoft used a “mass of Times copyrighted content” to train GPT models without permission — a direct example of unlicensed training data and unverifiable provenance. The case illustrates how unclear rights and licensing gaps can quickly escalate into legal, operational, and reputational risk for organizations deploying or relying on AI systems.
