Why the brakes are going on Enterprise AI

It wasn’t so long ago that deploying AI was something that businesses chose to do. Now, it’s something that businesses are finding hard to avoid. Most of the software platforms and packages that we rely on, from PDF readers to email clients and spreadsheets, have introduced AI functionality that it can be hard to switch off. And if you go with what is arguably the market leader, Copilot for Microsoft 365, AI options will be jumping out at your employees throughout the entire Office productivity suite.

Managing this isn’t so much about doing AI governance as ensuring compliance with stringent data protection regulations and leveraging built-in security features to reduce risks around the inadvertent sharing of company and client data and IP infringement. Vendor risk assessments will be required, along with internal policies and privacy notices – and the comms and training needed to get them understood and adopted by your workforce.

After all, a tool like Copilot will reach right inside your corporate email, Sharepoint and Teams systems in order to do its job, and will surface all kinds of things that might previously have been left hidden. Having a solid framework in place to anticipate and cope with this is crucial.

It’s not all about buying in tools though. With the recent release of Meta’s Llama 3.1 and Mistral’s Large 2, open source foundation models are matching the leading models of the proprietary AI companies (Anthropic, OpenAI) on multiple benchmarks. It’s now eminently possible to spin up your own AI instance without sacrificing performance, and tune it to your own ends.

While this does simplify the data protection requirements to some degree, it also demands that you put a customized governance framework in place that aligns with your specific use-cases and goals. This will include:

• rigorous auditing and validation processes to ensure model accuracy, fairness, and transparency;
• establishing guidelines to prevent biases and ensure ethical AI usage, fostering trust and accountability;
• identifying potential risks associated with model deployment and implementing proactive mitigation measures;
• drawing up an AI conformity assessment, dependent on the use cases and the jurisdictions.

As with a good privacy-by-design programme, implemented correctly this kind of oversight shouldn’t be a hindrance to your commercial success and growth, but a promoter of it. A partnership with aiEthix will ensure your AI deployments are not only cutting-edge but also secure, ethical, compliant and robust.

Contact us today to schedule a consultation and discover how we can help you harness the full potential of AI – while avoiding the pitfalls.