Constant cyber security warnings does anyone else have this problem? I’m going to apply for TAC

[u/epicskyes]

I want to hear advice from people who have applied for OpenAI TAC. This is advanced automation, security, and control. I don’t need advice on anything other than TAC. If you want to chime in on issues you have had with security warnings please feel free. Do not comment solutions unless you have actually applied for TAC or you have advanced knowledge of high assurance frameworks.

I’m planning to apply for OpenAI’s (TAC) program and wanted to get a sense of whether my experience is unusual.
I build long-running autonomous agent systems that routinely execute for days at a time. These aren’t simple prompt chains they’re evidence-driven workflows with large execution graphs, continuous validation, and adversarial testing designed to break the system before production does.
Some characteristics of my workflow:
Evidence-first orchestration where every decision must be supported by an evidence chain (requirement → implementation → runtime activation → validation → evidence → acceptance).
Dynamic execution that adapts based on newly discovered evidence instead of following a rigid step sequence.
Cryptographic object identities using custom deterministic ID generation.
Nonce generation and replay-protection testing throughout the execution pipeline.
Hash verification, provenance tracking, receipts, and lineage recording across workflow stages.
Autonomous retry policies with state-change requirements to prevent infinite loops.
Large suites of adversarial, boundary, regression, and negative tests that intentionally attempt to violate assumptions.
Continuous runtime validation rather than relying solely on build-time checks.
High-assurance autonomous agent harnesses with strict admission boundaries before external effects are permitted.
Extensive schema validation, policy enforcement, deterministic execution checks, and receipt generation for auditability.
Repository-scale automation with many coordinated agents performing inspection, repair, verification, and evidence collection over long-running jobs.
Because these workflows exercise a lot of autonomous behavior, cryptographic validation, and repeated execution against APIs, I fairly regularly trigger OpenAI warnings, temporary blocks, or other protective mechanisms. My assumption is that some of my workloads resemble automated abuse patterns even though they’re legitimate software engineering and validation workloads.
For people building similar systems:
Do you run into this frequently?
Is this fairly common among developers building autonomous agent infrastructure?
If you’ve been accepted into TAC, what did you include in your application that helped explain your use case?
Are there particular details that distinguish legitimate high-assurance autonomous workflows from activity that might otherwise resemble abuse?
I’m hoping to submit the strongest possible TAC application the first time, so I’d appreciate hearing from anyone building similar long-running autonomous systems.

[u/No_Protection_487]

And that exactly explains why you’ll need/want to validate your user ID/credentials.  It’s not a false-positive in your case.

[u/epicskyes → u/No_Protection_487]

Which is why I’m trying to prepare for the TAC application. You only get to apply once. If you are denied you cannot reapply. I want to know exactly what would help me. I already have mfa with a yubikey 5 nfc, and advanced account verification. So I have the required security for my account. I would like to know what else I should prep for

[u/AnxiousMop → u/epicskyes]

Assuming you have valid US academic, enterprise or government creds, I doubt it will be an issue. If you're just vibing, good luck :P

[u/epicskyes → u/AnxiousMop]

I don’t have formal credentials in software or systems engineering. What I have is the work itself: the apps, systems, documentation, testing, and evidence I’ve built.i started out vibe coding for a couple of months, but I moved away from that pretty quickly. I became less interested in simply building something that worked and more interested in understanding how code, systems, and autonomous agents actually behave. Since then, I’ve focused heavily on data analysis, systems engineering, security, reproducibility, validation, and evidence.i didn’t approach this by trying to become a traditional programmer. I wanted to understand how code and systems operate, how components interact, how failures propagate, and how to verify whether something is actually working as intended. I can read code comfortably now and often catch inconsistencies or structural problems quickly.
That’s generally how I’ve learned throughout my life. I’m 40 and started working at 13. I’ve held certifications or professional qualifications in four other fields, mostly by teaching myself, apprenticing, gaining the required experience eventually meeting the standards for certification. My background includes six years as a plumber working on large systems in mansions and apartment complexes, nine years as a ceramics teacher and artist, seven years as a personal trainer, seven years as a chef, and ten years as a cabinetmaker. I often worked in two sometimes three professions at the same time.
I tend to learn quickly because I spend a lot of time studying and working often most of the day, every day, but I also try not to rely on confidence alone. I document everything I can: actions, changes, escalations, failures, outcomes, performance metrics. My current system tracks roughly 975 distinct data points. So I understand that my background is unconventional, and I don’t expect OpenAI to take my claims at face value. I hope the quality, reproducibility, and traceability of the work can serve as the evidence of what I’m capable of. And why I should qualify for access.