# Model Provenance > Source: https://ibl.ai/resources/glossary/model-provenance > Last updated: 2026-08-19 **Definition:** Model provenance is the documented record of where a model came from — who produced the weights, what it was trained on, under what licence, and what has been changed since — sufficient to answer an auditor asking what produced a given output. **On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.** ## What is Model Provenance? Provenance is the AI equivalent of a software bill of materials, and it is currently far weaker. Most organizations can name the model they call but cannot state its training data, its exact version at the time of a decision, or whether it has been silently updated. That gap matters because a hosted model can change under a stable name. An output produced in March and one produced in September may come from materially different systems, which breaks the reproducibility that regulated review assumes. Open-weight models improve provenance substantially: the weights are pinned, the version is verifiable by hash, and behaviour is reproducible. Training-data transparency remains the weakest link across the industry, though efforts to make training corpora named, versioned and licence-attributed are emerging. ## Why It Matters Provenance underpins every audit, incident investigation and regulatory response involving AI. Where a decision must be explained, the record must identify the model and version that produced it, which is difficult when the model is a hosted endpoint that changes without notice. ## Key Characteristics ### Version Pinned and Verifiable The specific weights in use are identified and checkable by hash, so an output can be tied to a model state rather than to a product name that may have changed. ### Licence and Usage Terms Recorded Open-weight licences carry conditions on commercial use, attribution and specific applications. Provenance includes knowing which terms apply to each model in service. ### Training-Data Disclosure Where Available The weakest link across the industry. Efforts to publish named, versioned, licence-attributed training corpora are what would make this genuinely auditable. ### Modifications Are Tracked Fine-tuning, adapters, quantization and system-prompt changes all alter behaviour and belong in the lineage record alongside the base model identity. ### Hosted Models Can Change Silently An endpoint name is not a version. Providers update models behind a stable identifier, which breaks reproducibility unless a pinned version is available and used. ### Reproducibility Requires Held Weights Only weights you hold guarantee that the same input and settings produce the same output next year, which is what regression testing and incident reconstruction depend on. ## Examples - **Financial Services Firm:** A regulator asks an institution to explain an automated decision made eleven months earlier. — *The hosted model behind the endpoint has been updated since, so the original behaviour cannot be reproduced and the explanation rests on inference rather than evidence.* - **Public Sector Agency:** An organization pins an open-weight model by hash and records the version against every decision it supports. — *Any past decision can be reproduced exactly, which turns an audit response from a reconstruction exercise into a lookup.* - **Enterprise:** A team discovers a model in production carries a licence restricting the commercial use it is being put to. — *The exposure existed for months because licence terms were never captured as part of the model's provenance record.* ## How does ibl.ai support model provenance? By keeping the model and the record on your side of the boundary. ibl.ai is the agentic AI platform where you own all the code and the data, so the models you host are pinned to versions you control, and every interaction logs the model and version that produced it into storage inside your perimeter. Because it is model-agnostic, you can run open-weight models whose weights are verifiable by hash and reproducible indefinitely, rather than depending on a hosted endpoint that may change behind a stable name. It carries no per-seat pricing, and you can deploy anywhere. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## FAQ **Q: Why does model provenance matter for compliance?** Because explaining a past decision requires identifying what produced it. If the model changed behind a stable endpoint name, the output cannot be reproduced and the explanation becomes an argument rather than evidence. **Q: Do hosted models provide adequate provenance?** Usually not on their own. Providers may offer pinned versions, but training data is generally undisclosed and update policies vary. Where reproducibility is a regulatory requirement, held weights are a materially stronger position. **Q: What is an AI bill of materials?** The model equivalent of a software bill of materials: the base model and version, its licence, its training-data disclosure where available, and every modification applied — fine-tuning, adapters, quantization and system prompts. **Q: Does quantization affect provenance?** Yes. Quantization changes the weights and can change behaviour at the margins, so the quantization method and precision belong in the lineage record alongside the base model identity and version. **Q: Is training-data transparency realistically achievable?** Partially, and it is the industry's weakest link. Efforts to publish named, versioned and licence-attributed training corpora are emerging, but most widely deployed models still disclose little about what they were trained on. ## How does ibl.ai approach Model Provenance? **ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.** - **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours. - **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform. - **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ. - **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.