Stop Betting Your Brand on Scraped Data

Most buyers today face a simple but unnerving question: where did a vendor’s training data come from, and did the people behind that data agree to its use? If a company like Amazon can be forced to delete its algorithms, what would happen to yours?

What if regulators demanded proof that every piece of data used to train your fraud or lending model was lawfully obtained — could you show it? And if you couldn’t, would you still be allowed to use the model that drives your business decisions?
These aren’t hypothetical questions anymore. The FTC has already made them real.

Amazon’s Ring faced an order to delete its data products — including models and algorithms — after privacy violations. Everalbum had to destroy facial-recognition models trained on improperly obtained photos. WW/Kurbo was forced to delete children’s data and the algorithms built from it. Even Rite Aid was banned from using facial recognition for five years after deploying AI systems irresponsibly.

The message is clear: when your AI is trained on tainted or uncertain data, the model itself becomes the liability.

That’s why DeepXL was built differently.

Our models are trained exclusively on owned, verified, and consent-based datasets — never on scraped public data or customer information. Every dataset is traceable, auditable, and legally defensible. So when lenders deploy DeepXL, they’re not just reducing fraud risk — they’re protecting themselves from regulatory and legal exposure. With DeepXL, you don’t just get AI you can trust. You get AI you can defend.

DeepXL was built to remove that uncertainty. Our foundation models are trained exclusively on datasets we own and control. We do not use publicly available or web-scraped data, and we do not train on customer or end user data. When personal data appears in our owned datasets, we collect clear, explicit consent and maintain auditable records. This owned-only, consent-first approach eliminates ambiguity, reduces legal exposure, and improves model integrity for enterprise deployments. Customer data is processed solely to deliver the service purchased and it remains isolated to the customer’s tenant.

This position is also about quality.

Public datasets often act as a Trojan horse—introducing adversarial content, poisoned samples, or prompt-injected backdoors that quietly compromise model integrity. DeepXL’s owned-data strategy doesn’t just reduce legal risk; it structurally inoculates our models against these threats. By combining curated real-world examples with adversarially generated synthetic fraud inputs, we reinforce both data quality and model resilience at every layer of training.

At DeepXL, explainability extends beyond model outputs to model origins.

Our versioned training pipeline includes auditable dataset lineage, so we can trace exactly which sources contributed to any given model release. When data is deleted, it’s excluded from all future training runs—cleanly and verifiably. While we don’t retroactively retrain prior versions, we maintain forward-only consent records and can attest, with precision, what went into each model. This gives enterprises and regulators not just visibility into model behavior, but into model composition. Combined with our output explainability features—confidence scores, fraud heatmaps, and anomaly rationales—this ensures DeepXL models are transparent, accountable, and audit-ready from dataset to decision.

Enterprises choose this approach because it shortens diligence and reduces surprises. Legal teams appreciate clean provenance and consent; security teams value the absence of unknown public data; procurement teams get a consistent, auditable story; product teams see fewer regressions from noisy inputs; and communications teams avoid headlines about “mystery training data.”

We operationalize this with discipline. Every training dataset is inventoried with its source, ownership basis, consent artifacts, and retention and deletion schedules. Sensitive categories are flagged at ingestion and access-controlled throughout their lifecycle. We write plain-language notices, record explicit opt-in for any personal data used in our owned datasets, collect only what is necessary, apply robust de-identification where feasible, and document our methods. We engineer forward-looking reversibility so deleted data stays out of subsequent versions without disrupting customer deployment. 

If you want a provider your General Counsel, CISO, DPO, and Communications team can endorse on day one, DeepXL offers a clear answer: we train only on data we own; we never train on public data; we never train on customer or end user data; we obtain and record consent where applicable; and when deletion is requested, we remove the data and keep it out of future training while being transparent that past training remains in previously released models. This is how we deliver clean, consent-first AI you can take to production with confidence.