AI Governance on the Fast Adoption Train?

It’s now over three years since Samsung became the first big global name to fall foul of some of the unintended security challenges posed by AI. 

Just to remind you, Samsung’s semiconductor engineers were using ChatGPT to help fix bugs in their source code, without realising the highly confidential data involved would be retained by OpenAI to further train ChatGPT’s future outputs for other people. And if it can happen to a business of the size and stature of Samsung… it can happen to smaller organisations like yours, too.

Even though AI use has come on leaps and bounds since then, it remains a valid reminder of what can happen when businesses rush to adopt AI without considering security and governance along the way. Or, perhaps even worse, when they know that AI won’t fit into their existing governance processes, but bow to stakeholder pressure and start using AI anyway.

Either way, the ease and speed with which anyone can use AI tools and platforms means security and governance processes have to be reconsidered. When large data sets can be copied and pasted into a web-based AI tool to generate responses in a matter of seconds, traditional governance is easily bypassed in its entirety. In short: AI means the old, reactive, policy and approval-based concept of governance just doesn’t make sense any more

The key to shutting off this vulnerability, without missing out on the potential of AI, is to refocus governance towards data usage, fully integrated within workflows so that vital controls are applied in real time. And the process of that refocusing starts with an AI Exposure & Risk Review, which can help you pinpoint the areas in your workflows and data processes where governance should take place. Book your free assessment with one of our community experts today.

This topic is sponsored by Palo Alto Networks, whose commitment to making enterprise-grade security accessible to mid-market organisations helped bring this discussion to the community.