"9 Seconds. Zero Backups." drew more engagement on Coder Legion than anything we've published here, and the sharpest part of it wasn't in the piece. It was in the comments. Two readers, Mike Dabydeen and sibasispadhi, raised questions specific enough that we took them straight back to Archit Lohokare, AppViewX's CEO, instead of paraphrasing around them.
Dabydeen's challenge was the harder one. He argued the original piece framed the PocketOS database wipe as a permissions failure, when the more useful frame is that nothing imposed a cost between the two destructive calls — delete the database, then delete the backups — no hold, no rate limit, no second identity required to proceed. He pointed to prior art that predates AI entirely: S3 Object Lock in compliance mode makes an object undeletable for a retention period no matter who asks, and RDS deletion protection forces a separate API call to disarm a guard before any delete lands. His sharpest point was about units. A velocity control that blocks a second destructive call inside a time window would have caught PocketOS, since that incident was two calls. But a single delete against a bucket holding years of backups is one call, and any control that counts calls waves that through. If a control is time-based, he argued, it has to attach to blast radius, not call count — and estimating blast radius before a call runs is the actual unsolved problem.
Lohokare's answer: the platform's real-time policy doesn't key on action type alone. It runs what he calls a risk engine across five dimensions simultaneously — the agent's identity and permissions, what data it's touching and in what mode, the action itself (a routine read call versus a privilege-elevation command), the agent's full bill of materials and posture (has it started talking to an unsanctioned model), and its runtime history. Those combine into a single risk score that decides whether an action proceeds, gets flagged, or gets stopped. On the disarm question, he described four escalating checkpoints, chosen by how risky an action scores: human-in-the-loop approval before it runs, human-on-the-loop visibility with the ability to reverse it after, a kill switch scoped to one action or the whole agent, and multi-factor checkpoints — a passkey or one-time code — before a high-risk step proceeds.
On velocity specifically, Lohokare says the platform already blocks sequentially: if one action raises an agent's risk score enough, the next action gets blocked before it runs, based on accumulated history rather than a fixed call count. His example: if an agent's underlying model changes from Claude Opus to DeepSeek, AppViewX flags that drift and can halt the next action before the agent has done anything with the new model at all. On blast radius, his answer split the difference Dabydeen was pushing toward — customers can explicitly tag specific actions or resources as high-risk, and the platform separately watches behavior in real time to catch what wasn't tagged. He said this is running today at a large IT company, a trucking manufacturer, and multiple financial institutions, though he didn't name any of them.
Pressed on whether time- and blast-radius-based controls — the kind that don't require predicting what an agent will attempt — deserve more investment right now than behavioral prediction, Lohokare pushed back on the premise. His view is that you need both, because blast radius itself doesn't hold still. He pointed to the OpenAI-Hugging Face incident from our first conversation: nobody building deterministic controls for those agents could have anticipated they'd find and exploit a proxy-cache vulnerability to reach the open internet. Controls scoped to a known blast radius will eventually meet an agent that finds a path nobody scoped for. That's his case for running a probabilistic risk engine alongside the deterministic rules, not instead of them.
sibasispadhi's question was more direct: why didn't a secret scanner catch the AWS key in the first place? Lohokare says AppViewX does integrate with secret-scanning tools, but doesn't treat scanning as the real fix. An agent can acquire a credential from places no static scan covers, including from other agents it's communicating with. The value, in his framing, is catching a credential at the moment it's used, not the moment it's created. On the deeper issue — that the key carried admin-level access — he says the platform flags over-permissioned credentials and pushes a model past least privilege entirely: provision access just-in-time instead of standing, and start every agent with close to zero agency, expanding it only through human approval as trust is earned.
One thing came up that neither commenter asked about, and it's worth flagging on its own. Lohokare says the newer conversation with customers isn't only about agents destroying data — it's about agents destroying budgets. He described organizations whose AI spend got burned through by a runaway or manipulated agent well ahead of schedule, serious enough that AppViewX now monitors token consumption and cost in real time, with the ability to cut an agent off before it exhausts a budget, the same way it would cut off a destructive database call. Security teams are used to measuring blast radius in data and infrastructure. For an agent with a live credit card, that math needs a line item most security tooling was never built to track.