A formal way to frame this insight is how a formal methods security researcher might: graph-based program verification.<p>A lot of core confidentiality and integrity security problems come down to 'safety property' verification (a notion from model checking), which in turn comes down to reachability on a program flow graph (a notion from program analysis). This is also true of access control verification, but that's a topic for another day.<p>Imagine a dataflow or points-to analysis on a program, and extend it all the way to include the code in your OS and the cloud and the database. These analyses create a graph, and the question is can an attacker get from an entrypoint and precondition of some node A (a line of code) and traverse to the assets on point B (another line of code.)<p>Interestingly, the security field is increasingly getting there, with ideas like CNAPP, IAM/Cedar, AD/bloodhound, where we are getting these basic access graphs modulated by estate, identity, access policy, etc. Often we don't even really need the programs, because it's more about a distributed system where we can focus just on identities and policies across trust zones. (Eg, If a box gets hacked, that exposes other credentials on the same box.)<p>At the same time, anyone working in these things also knows graph reachability is simplistic 80's & 90's stuff: there can be complex logical policies at each node, So we're seeing things like modeling those harder points, not just as pure reachability, But also things we can actually peek into and more richly verify, such as by modeling fancy ABAC policies using smt solvers.<p>I don't think that's really where the author is coming from, but it's a reason the article resonated with me for so many years from a principled perspective, and I think it's incredibly practical and important today.<p>(Disclaimer: we do crazy GPU graph AI power tools for folks in the space at Graphistry / Louie.AI, in my first verification papers here were almost 20 years ago, so I've been thinking about this a lot.)