AI Security: Lessons from the UK Government's Playbook
The UK government’s AI framework isn’t just another bureaucratic checkbox exercise.
It’s a stark reminder that Machine Learning and Large Language Models play an increasingly important role in decision-making and that AI governance is no longer optional - it’s essential.
If you want to develop an AI security policy for your organisation, their framework is a terrific starting point.
Let’s peek at a few of their 10 key principles:
“You know what generative AI is and what its limitations are.” This goes beyond surface-level understanding. It demands a deep grasp of your AI’s inner workings, biases, and failure modes. Are you truly aware of where your AI might stumble or, worse, amplify existing biases?
“You have meaningful human control at the right stage.” The keyword here is “meaningful.” Rubber-stamp approvals won’t cut it. How are you ensuring human oversight is substantive, especially in high-stakes decisions?
“You understand how to manage the full generative AI lifecycle.” This principle forces us to think long-term. From data sourcing to model retirement, each stage presents unique security challenges. Have you mapped out these risks?
“You know how to keep generative AI tools secure.” This isn’t just about protecting the AI itself, but also the data it processes and generates. How are you safeguarding against data poisoning, model theft, or adversarial attacks? Are your AI tools integrated securely within your broader security framework?
More here: Generative AI Framework HMG
These principles aren’t just guidelines; they’re a call to action. They challenge us to elevate our AI governance from superficial compliance to robust, thoughtful implementation.
What’s your take on these principles? Are they comprehensive enough, or do you see glaring gaps?
Cheers, Craig