AI’s potential to detect and respond to threats in real-time is transformative, and NIST’s program rightly emphasizes the risks of data leakage during model training, potentially an overlooked vulnerability. As organizations train models on sensitive data, they inadvertently are creating this new attack surface. It will be interesting to see the types of strategies or controls leveraged to secure AI training pipelines without stifling the constant innovation we're seeing on an almost week by week basis.

@Vincent Primiani That’s very cool. Makes me think that self-policing models could introduce an even more compelling security layer. I’m starting to see how things like "model drift" and unintended biases might need more attention, possibly requiring ongoing adjustments and clearer explanations to keep oversight transparent. It’ll be interesting to see how these frameworks evolve.

The best way to understand this is that risk acceptance is the acknowledgment of a risk and deciding to bear it without any additional action. Whereas risk mitigation, transference, and avoidance all involve some form of proactive measure/action.

Very cool. Leveraging AI for to help study is quite an exciting prospect. I've been generating prompts for myself in ChatGPT to help create plans of action on material, so it's smart that you're thinking ahead with this. Interested in trying it out.

@Binoy Chacko The question can be thought of as "If Ed sees those ipconfig results what has occurred on his system?"