I work in risk analytics and think this comment is dead on. Tools such as Splunk can wrap metadata facilitating analysis around arbitrary input data (e.g. admin logs, vulnerability scan results, etc). I expect that such tools will reduce the need for some of the work currently done by data scientists.

With that said, there is still a need for human driven analysis of risk data in order to compose models that prioritize and collate incoming signals in ways to assist in risk management decisions. These models are highly domain specific (e.g. a model for evaluating IT infrastructure assets' risk across a global banking business) and are nontrivial to design.

I suspect that data scientists will be useful in bridging the gap between automated risk analytics collection tools and corporate risk management by assisting in coming up with risk models based on the volumes of incoming data.

Edit: or maybe "quants", not "data scientists" will fill this role. Regardless of the naming convention, it's an interesting and complex space to watch.