[oscarcp]

Just to make my point: can I really trust humans to keep up with the required identity data that will give me enough "credibility" so the AI will give me access?

Let's say I had a promotion, who changes my title in the system, who changes my responsibilities and my place in the org chart, more importantly, will they do it or is <HR_NAME_HERE> on leave and forgot? those are data points required by the agent to determine if I'm "good enough" to access a certain resource.

What if... someone spoofed my address and did a flood in one of the resources that are lateral to what I'm allowed to access (let's say I don't have access to company sales but I do to department sales and the attacker floods company sales with requests under my address), would the AI determine that I'm a high-threat actor and not allow me to access legitimate files going forward?

Will exceptions be made by humans? In which case we go back to human-managed permissions.

Sorry, I might be barking up the wrong tree but I think these are questions that are not meant to be solved during implementation. And they add to what @firasd said about legitimate-but-odd behaviour

[firasd]

I think the dynamic risk signals is already an assumed part of the system. The proposal here seems to be shrinking the trust boundary from "can Alice use Google Drive?" to "should this specific read/write/export API call on this specific resource be allowed right now?"

So yeah you're right in that it does add more probabilistic randomness just by virtue of changing the boundary of when the permission gate kicks in

[thewebguyd]

Correct. In existing zero trust identity, we already have deterministic risk signals.

In Entra where I work we already check things like "Is this person on a managed device? Is it compliant? Where are they? What MFA methods do they have registered/did they use?" on top of existing RBAC, etc. and its continuously evaluated. Entra watches for leaked passwords, assigns risk scores, etc. and you can make access decisions based on user risk or sign in risk, force password changes, require different MFA methods depending on the resource and the risk level, etc.

"Should this API call on this resource be allowed right now?" is mostly already determined by the above.

Where I see adding AI into the evaluation is to watch for unusual behavior that's not picked up by the deterministic signals. "Alice is trying to download gigs worth of data from the company file share, however she has never done that in the past, and there hasn't been any recent role/job changes" and so the LLM flags it or denies the request, or pushes it for a human approver, etc.