To Developer Advocates, engineering leaders, Trust & Safety teams, and the broader developer community,
This is not a complaint from a “frustrated developer” and it is not an attempt to argue that platforms should not enforce their policies.
Quite the opposite.
I believe strong security controls, abuse prevention, and enforcement mechanisms are necessary. But I also believe that when automated systems make decisions with serious consequences, there must be a reliable way to correct a mistake.
That is the part I am worried about.
Recently, our OpenAI organization was deactivated because of an alleged policy violation. We took the notification seriously and immediately started investigating.
We reviewed our systems, integrations, logs, and usage. We could not find evidence explaining what had triggered the enforcement action.
Because we could not rule out the possibility that an API key had been compromised, we treated the situation as a security incident anyway. We deleted all API keys, rotated passwords, enabled and reviewed MFA, hardened the servers and services involved, and reviewed how credentials were stored and accessed.
Then we appealed.
And this is where the real problem started.
Our appeals have repeatedly resulted in essentially the same short response:
“Thank you for reaching out. We have already reviewed your appeal and the original decision stands.”
No indication of what happened. No approximate timestamp. No affected project. No information about which API key or account within the organization generated the problematic traffic. No indication whether the activity looked like compromised credentials, abuse through an integration, or something else.
We asked for information that would allow us to investigate.
We received none.
We contacted support and explicitly asked three times for the matter to be escalated to a human reviewer. The responses directed us back to the same documentation and the same appeal form.
We submitted the appeal four times.
The same result.
As developers, we would probably call this an infinite crash loop.
Appeal → automated rejection → contact support → appeal link → appeal → automated rejection.
There appears to be no exit condition.
OpenAI itself correctly reminds users that AI systems can make mistakes. The same principle must apply to systems used for fraud detection, abuse detection, account classification, and policy enforcement.
A false positive in a chatbot answer is inconvenient.
A false positive in an enforcement system can disable a company’s infrastructure.
What concerns me most is not even that our account was disabled. Security systems will inevitably produce false positives occasionally. What concerns me is that I cannot identify a functioning mechanism for correcting one.
We are effectively being asked to remediate an incident while being given no information about the incident.
From a security engineering perspective, that is difficult to reconcile.
How do we determine whether a credential was compromised if we do not know which credential was involved?
How do we improve our monitoring if we do not know what behavior triggered the system?
How do we harden an integration if we do not know which integration was implicated?
And how can we demonstrate that remediation has been completed if nobody will tell us what we are supposed to remediate?
We still took every precaution available to us. Every existing API key was revoked. Credentials were rotated. MFA was reviewed. Infrastructure was hardened. Access patterns and integrations were examined.
But even after explaining all of this and asking for a manual review, the answer remains essentially the same two sentences.
There is also a broader question here about the purpose of enforcement.
Some people will understandably say: “Why not just create another account and move on?”
But that is precisely the point.
A determined bad actor can create another account quickly. A legitimate company, on the other hand, will try to understand what happened, contact support, investigate its infrastructure, rotate credentials, improve controls, submit evidence, and ask for a review.
If the enforcement process makes no meaningful distinction between those two behaviors, who is the process actually optimized for?
Zero-tolerance policies can make sense for deliberate abuse. But zero-tolerance combined with zero transparency and no effective escalation path risks disproportionately affecting legitimate users who are actually trying to comply.
And this matters beyond one account.
We build systems for customers. We advise companies about architecture, security, reliability, and AI providers. To recommend a platform for business-critical workloads, we need to be able to trust not only the API, but also the operational processes around it.
Reliability is not only uptime.
Trust is not only model quality.
For businesses building on an AI platform, reliability also means knowing that when something goes seriously wrong, there is a path to reach a person, understand the problem, remediate it, and continue operating.
I am therefore writing this primarily to OpenAI’s Developer Advocates and community leaders.
Developer advocacy is not only about showing developers what can be built with a platform. It is also about carrying the developer community’s experience back into the organization, particularly when developers encounter systemic problems they cannot solve through the normal channels.
I am asking you to bring this issue to the people responsible for enforcement, Trust & Safety, and developer support.
Not simply our individual case, although I would obviously be grateful if someone reviewed it manually.
The larger question is this:
What is the recovery path when an enforcement system gets a classification wrong?
There should be one.
There should be a meaningful human escalation path for cases where a customer has investigated, remediated potential security issues, repeatedly appealed, and is still unable to obtain even basic technical information about the alleged violation.
There should also be enough information provided to allow responsible developers to improve their security. Nobody is asking OpenAI to disclose abuse-detection algorithms or information that would help attackers circumvent safeguards. But there is a large distance between exposing detection systems and providing no actionable information whatsoever.
I still believe in what OpenAI and this developer community are building. That is precisely why I am writing this publicly instead of simply walking away.
Long-term relationships between platforms and developers depend on trust. And trust cannot be one-directional.
Developers are expected to follow policies, secure credentials, monitor their systems, respond to incidents, and act responsibly.
In return, when something goes wrong, we need a process that is transparent enough to investigate, accountable enough to correct mistakes, and human enough to recognize when the automated loop is no longer working.
That is all I am asking for.
A human review.
A technically actionable explanation.
And, more broadly, an escalation mechanism that developers can trust when automated enforcement may have got something wrong.
Michael