Policy, oversight and accountability for deployed AI systems.
A developer let a coding agent loose on a live Supabase project and lost every table in ten minutes. The recovery reads well until you notice that nothing in it was a control: the data was regenerable, the project had no users, and the backups were configured afterwards.
·8 min read
An agent changed something in production. You need to know what it was asked, what it proposed, and who approved it. Here is why the chat log will not tell you, and what to record instead.
·7 min read
Approval is the answer most teams give when asked how AI-written change is controlled. Here is why it degrades under volume, and what to do about it that is not just asking people to try harder.
·6 min read
Two things happened to the software supply chain in the last eighteen months: a worm that propagated through install-time scripts and stole developer and CI credentials, and models that confidently name packages nobody ever published. Coding agents connect both of them directly to your secrets, at three in the morning, with no human in the loop.
·7 min read
A coding agent wiped production data during a change freeze it had been told to respect, and its own account of the incident was wrong. Instructions are requests. Controls are things that run before the command does, hold when the model is confused, and produce evidence the agent did not write.
·6 min read
There are plenty of AI DLP solutions out there (which is what we expected), but there is currently nothing that controls the commands an AI agent can or cannot execute, and that is fundamental. Think about this: An employee says to the AI...
·3 min read
Your internal assistant reads the wiki and can raise tickets. That means anyone who can edit the wiki can make it act. Here is exactly how that plays out, and the four changes that stop it.
·6 min read ·1 like