You Have Logs. Logs Can Be Edited.
In Mobley v. Workday, a federal court allowed discrimination claims to proceed against the AI vendor itself, on the theory that it acted as an agent of the employers using it, and later certified a nationwide age-discrimination collective. The case does not turn on intentions, policies, or marketing. It turns on what the model actually did.
That is the new shape of the problem. Every AI vendor selling into a regulated industry now has to be able to prove what its model actually ran, on which input, producing which output. Today, almost none of them can.
What happens when the question arrives
When your AI denies a claim, scores a loan, or screens a candidate, someone eventually asks the question in an adversarial setting: an examiner, an auditor, a plaintiff's lawyer. Which model produced this decision, and did it run correctly?
Today the answer is a stack of self-kept records. Internal logs and dashboards, written by the party under examination. A SOC 2 report. The vendor contract and its indemnities. Framework self-attestations.
Each one collapses the moment it turns adversarial. Logs are editable, and everyone in the room knows it. SOC 2 certifies the security of your systems, not the correctness of any output they produced. The contract is thinner protection than most buyers assume: in TermScout's dataset of AI vendor contracts, 88% cap the vendor's liability, often at a month's fees, and only 17% warrant regulatory compliance. And certifications attest process and paperwork, not the decision in front of the examiner.
The pattern underneath is simple. Self-kept records stop being evidence precisely when evidence is needed.
The receipt
Cyberian gives every AI inference a receipt. It binds together the exact model that ran, a fingerprint of the input, a fingerprint of the output, who executed the job, and who verified it, sealed so that changing anything breaks the receipt. The verifying party is never the party that ran the job, and any third party can check the receipt later without re-running the work.
Think of it as a flight recorder for AI decisions. It does not make the decision better. It makes the decision provable, to someone who has no reason to trust you.
This is live in production today. Jobs run across independent compute providers, customers can bring their own models, and every result comes back with its receipt.
How strongly can you prove it ran correctly?
Proving what ran is the baseline, and it is live on every job. The harder question is how strongly you can prove the computation was done right. We think about it as a ladder, where each rung buys more certainty per unit of cost.
The first rung is probabilistic replay, live today. An independent prover secretly re-runs a sample of the work and compares results. Any single job has a 20% chance of being checked, at roughly 20% compute overhead. That sounds modest until you consider repetition: a provider that cheats routinely faces near-certain detection, so cheating becomes a losing bet.
The second rung is the one we have just added, and it is the subject of our second US provisional patent filing (24 claims, July 2026) and a companion paper: committed computation traces. The idea is seal first, answer later. The executor runs the job and records the output of every internal layer of the network, then seals the full trace before any question is asked. Only after the seal does a secret-keyed challenge pick a handful of links in the chain, say 5 of 24, and each chosen link is checked directly: does this layer's output really follow from the previous one? Nothing is re-run.
Why do a few links suffice? Because of a cascade. Each layer's output feeds the next, so one falsified layer violates every constraint after it. The lie cannot be contained, and any opened link downstream exposes it. The closed form works out to an 86.8% detection rate for a 24-layer model with only 5 links opened, a 4.34x improvement over replay, at 5 to 10% overhead on the executor. A prover-side variant of the same idea enriches today's replay tier with an auditable reference trace, adding forensic depth to every receipt.
The top rung is a full mathematical proof of correctness. Zero-knowledge proofs already do the job for statistical machine learning and smaller models, and that is the destination we are building toward for the most sensitive work: a proof that this model produced this output, checkable in moments, without revealing the model or the data.
The question is being written into exams and procurement
None of this is a forecast. It is dated and checkable.
In March 2026, a federal court ordered UnitedHealth to hand over records of its AI-driven claim denials in Estate of Lokken v. UnitedHealth. The same month, insurance examiners in 12 states began piloting the NAIC AI Systems Evaluation Tool, a structured questionnaire probing how insurers govern their models. In February, COSO's guidance on internal control over generative AI set the expectation that organizations keep records sufficient to reconstruct which model version acted, and on what. In April, US banking regulators replaced the fifteen-year-old model risk guidance SR 11-7 with SR 26-2, and explicitly left generative AI out of its scope, an assurance vacuum sitting exactly where the risk is growing fastest. And in Canada, OSFI's Guideline E-23 takes effect in May 2027, covering AI models across all federally regulated financial institutions.
The direction is consistent. Prove what your AI did is moving from a nice-to-have into examination language and procurement questionnaires, one dated document at a time.
The cheapest time to add verification
If you sell AI into insurance, healthcare, legal, or financial services, the question is coming toward you on someone else's schedule. The cheapest moment to add verification is before the examiner asks, while you can design it in rather than reconstruct it under discovery.
The receipt is live. You can try it today at cyberiansystems.ai, run a batch on your own infrastructure or ours, and hold the result in your hand: proof of exactly what your AI did, from a party with no stake in the answer.