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It's also a business decision. You need to invest staff time in testing and automating said updates, and will inevitably lead to more outages.There are many vendors attacking this exact problem from many directions, including my employers[0], but pretty much every platform vendor has something here.
I do agree that automation and risk are at the root of the tension between security and stability. It's a feedback loop that can run in two directions. If you embrace stability, the loop becomes "no changes ever" and security becomes much more difficult to achieve in practice. If you embrace security, then at first you face instability -- but embracing rapidity and building out tooling for it makes you both more secure and more stable in the long run.
> But its not like actuaries have a lot of data to model to the point of accurately imposing a cost per day of unpatched CVE.
I'd be interested to know if this is actually true.
I recently got interested in the FAIR risk modelling approach. It requires a bunch of legwork, so in practice it won't catch on without determination to use it or something like it.
Usefully it breaks risk down into more easily-estimated, easily-tracked subcomponents[1]. "Probability" becomes "Loss Event Frequency", which further breaks down into Threat Event Frequency and Vulnerability, these break down further in turn. Similarly, the usually-vague "Impact" becomes Primary Loss and Secondary Loss, each of which has 6 specific types that can be estimated. Those estimates can be based on available data, whether your own or industry statistics.
> Worse, many places have competitors, and if they're choosing to take on risks in the name of lower prices and higher market share, the customers of your secured app walk out the door.
[0] I work for Pivotal. We've been building towards the vision of "Rotate, Repair, Repave" in Cloud Foundry: https://builttoadapt.io/the-three-r-s-of-enterprise-security...
[1] https://cdn2.hubspot.net/hubfs/1616664/The%20FAIR%20Model_FI...