What solution can help mitigate AI security risks?

The solution that can help mitigate AI security risks is Zero Trust. This approach is based on the principle of "never trust, always verify," which assumes that threats could be present both inside and outside the network. By implementing a Zero Trust architecture, organizations can enhance their security by ensuring that every access request is thoroughly authenticated and authorized, regardless of the user's location or the network from which the request originates.

Zero Trust incorporates several key elements, such as strict identity verification, micro-segmentation of networks, and continuous monitoring of user activity, all of which help minimize the potential vulnerabilities that could be exploited in an AI deployment. This is particularly relevant in AI contexts, where sensitive data and algorithms might be targeted by malicious actors. By utilizing Zero Trust principles, organizations can provide a more secure environment for their AI systems, thus reducing the risks associated with unauthorized access and data breaches.

The other options, such as data overload, public cloud dependency, and increased user access, do not inherently provide solutions to mitigate security risks associated with AI. In fact, they could exacerbate vulnerabilities by overwhelming security systems, being reliant on potentially insecure environments, or expanding the attack surface by allowing more access points than necessary, respectively.