Artificial intelligence can help businesses through automation or by improving existing tasks, but like any technology it comes with risks if not managed well. For those businesses that decided to build their own AI or buy software that has AI embedded in it, assessing its risks is an important step to ensuring compliance and data security.
The explosion of generative AI adoption has magnified those and new emerging risks. Generative AI adoption is the top-ranked issue for legal, compliance, and privacy leaders for the next two years, according to a Gartner survey from December.
To counter the risks, organizations can thoroughly evaluate their exposure to AI, assess the risks, and set guiderails and mitigation strategies in place to deal with the most business-critical issues. The assessment strategies differ based on the kind of AI that’s involved, which generally falls into three categories: internal AI projects, third-party AI, and AI used maliciously by attackers.
How to assess internal AI risks
Whether by using existing risk and quality management frameworks or setting up an internal framework for how AI models can be deployed, companies need to know how AI is being used internally.