01 – AI Continuity Escrow
Protect the continuity of an AI solution when a vendor or technology drops out.
Solutions
AI Escrow
Independently record the models, prompts, data, workflows, and configurations your AI solution relies on. For continuity, compliance, and control.
Introduction
Your AI has more dependencies than you think
AI is increasingly part of business-critical processes. From document analysis and customer service to fraud detection, decision-making, and fully automated AI agents.
But an AI solution is more than software. How it works can depend on a combination of source code, models, model weights, fine-tuning, system prompts, datasets, agent logic, workflows, configurations, and external AI platforms.
If one of those components disappears, a copy of the source code alone often isn’t enough.
AI Escrow from Escrow4all secures the relevant components of your AI solution independently with a Trusted Third Party. Not only so the solution can be restored if a vendor drops out, but also to create a verifiable record of how an AI system was configured at a given point in time.
Use cases
AI Escrow: one name, three solutions
AI escrow can serve different goals. That’s why an arrangement with Escrow4all doesn’t start with a standard list of files, but with a question: what do you need to be able to prove, restore, or continue?
02 – AI Evidence Escrow
Independently record how an AI system was configured at a given point in time. For compliance, audits, or internal accountability.
03 – Internal AI Escrow
Safeguard in-house AI and reduce key-person risk and the loss of critical knowledge.
Three ways organizations use AI
Not every AI dependency is the same. In practice, there are roughly three implementation patterns, and what you can realistically place in escrow differs per pattern.
This distinction determines which AI Escrow solution is relevant in each situation, and what can actually be deposited within it. Below, we work this out per solution.
| AI Continuity Escrow | When your organization depends on an AI vendor |
You rely on an AI solution from an external vendor for a business-critical process. The vendor manages the technology, while your organization depends on it working. If the vendor drops out, you want to be able to restore the solution, keep it running, or migrate it in a controlled way.
AI Continuity Escrow defines in advance which components that requires. What you lose, and therefore what you secure, differs per implementation pattern:
| Pattern | What you lose if the vendor stops | What Continuity Escrow typically focuses on |
|---|---|---|
| Embedded (Copilot, Einstein) | Access to the platform, not the underlying model | Your own agents, prompts, and connected knowledge sources |
| Platform, or compose with a closed model (Microsoft Foundry, Bedrock, API) | The implementation partner who built the application | Your own code, prompts, configuration, and fine-tuning dataset |
| Compose with an open-weight or in-house model | The builder who manages the model | Nearly everything: weights, checkpoints, training data, and configuration |
Possible deposit components
We tailor what you deposit to your situation for each arrangement. Think of:
Model versions and model weightsFine-tuning parameters and checkpointsSystem prompts and prompt librariesAgent definitions and agent logicAI and automation workflowsDeployment and infrastructure configurationTraining, test, and evaluation materialsDatasets or data referencesDependencies on external models and APIsRecovery and migration instructionsSource code and technical documentation
For the AI user: you have an answer ready to the question “what do we need if this vendor disappears tomorrow?”
For the AI vendor: you meet enterprise customers’ growing demand for demonstrable continuity, without handing your model, prompts, or source code directly to the customer.
| AI Evidence Escrow | Record how your AI was configured at any given moment |
AI isn’t only about continuity. Organizations increasingly need to reconstruct which technology, data, configuration, and controls were in use at a specific point in time. Which model version was running, which system prompt was active, and which data and evaluations went with it?
AI Evidence Escrow periodically and independently records selected AI assets and documentation. Each deposit is a dated snapshot of the AI system. The question it answers differs per implementation pattern:
| Pattern | What you need to be able to show | For example, during |
|---|---|---|
| Embedded (Copilot, Einstein) | Which knowledge sources and instructions were active at a given moment | An incident in patient communications |
| Platform, or compose with a closed model (Microsoft Foundry, Bedrock, API) | Which model version, system prompt, and evaluation results were active | A DORA audit or financial regulator review |
| Compose with an open-weight or in-house model | Which dataset, training setup, and validation results belonged to a model version | An EU AI Act assessment |
Possible deposit components
We tailor what you deposit to your situation for each arrangement. Think of:
Model and software versionsPrompts and agent configurationsTechnical documentationModel and dependency registersTraining, test, and validation informationDataset manifests and data provenanceEvaluations and test resultsDeployment configurationsLogs and audit informationGovernance and approval documentation
From continuity to proof: the EU AI Act imposes documentation, logging, and record-keeping obligations on certain AI systems and models. AI Escrow is not a legal requirement and does not by itself make you compliant. It can, however, add an independent layer to your documentation, traceability, and evidence.
| Internal AI Escrow | Protect the AI you build yourself |
Not every dependency lies outside your organization. More and more organizations are building their own AI: agents, RAG solutions, copilots, automations, and specialized models. That creates new business-critical knowledge, which often sits with a handful of developers, data scientists, or external consultants, or in environments that only a few people can access.
Internal AI Escrow secures that knowledge independently of the people who built the solution. Here too, the risk differs per implementation pattern.
| Pattern | Where the knowledge often sits | For example |
|---|---|---|
| Platform | With a single consultant or developer | An internal copilot built on Microsoft Foundry or Bedrock |
| Compose with an in-house model | With the data science team that built the model | A custom-trained fraud detection model |
| A combination of both | Spread across in-house agents, the platform, and an internally trained component | A RAG solution with a custom embedding model |
Possible deposit components
We tailor what you deposit to your situation for each arrangement. Think of:
In-house AI applicationsInternal agents and copilotsSystem prompts and prompt chainsFine-tuned modelsRAG configurations and knowledge basesAutomation workflowsModel and API configurationsDeployment scriptsTechnical documentationEvaluations and test sets
You decide in advance who gets access, under what conditions, and through which authorization procedure. That way, alongside your production environment, you have an independent safeguard for your critical AI knowledge.
AI-Stack
What does your AI depend on?
An AI application rarely stands on its own. It can consist of proprietary software combined with foundation models, agent frameworks, cloud platforms, vector databases, and workflow technology.
Models & AI platforms | Agents & Orchestration | Workflows | Cloud & Deployment |
|---|---|---|---|
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AI Escrow starts by mapping these dependencies: what can be deposited, what needs to be documented, and where a fallback or migration strategy is needed.
Deposit
What goes into an AI Escrow deposit?
No two AI stacks are the same. That’s why we determine in advance which components the escrow arrangement needs for its purpose.
Software & deployment
Source code, libraries, containers, infrastructure code, deployment scripts, configuration files, and technical documentation.
Models & fine-tuning
In-house or transferable model weights, checkpoints, adapters, parameters, hyperparameters, and information on the model versions used.
Prompts & agents
System prompts, prompt templates, prompt chains, agent definitions, tools, instructions, and orchestration logic.
Data & provenance
Training, fine-tuning, test, and evaluation data where legally and technically possible, plus dataset manifests, schemas, provenance information, and references.
Workflows
AI pipelines, automations, integrations, routing logic, and configurations of agent or workflow platforms.
Documentation & evidence
Model documentation, architecture, test results, evaluations, change records, approvals, and any other information needed to understand how the system works or to reconstruct it later.
Verification
Drawing on our expertise, we’ve developed three levels of verification:
Integrity Check
We check whether the deposited AI assets are present and readable. We verify that the deposit matches the agreed scope: have all expected files and components been delivered, are the files intact and undamaged, and do the hashes and metadata match?
The Integrity Check shows that the material is there and the deposit is in order, without assessing whether the assets are sufficient to restore or run the AI solution.
Recovery Verification
We examine whether the deposited assets are technically usable for the purpose they’re being kept for. Can the relevant components of the AI solution be set up or redeployed from the deposit? We assess whether the deposit contents, including configurations, dependencies, and instructions, are sufficient to carry out that process.
Reproducibility & Functional Verification
For critical applications, we set up a more extensive verification in which a model, workflow, or AI solution is actually restored in a controlled environment and tested against pre-agreed test or evaluation scenarios.
Not just showing that the material is there and can be restored, but determining whether it’s fit for the purpose it’s being kept for.
Legal
Legally and technically sound
AI assets can involve intellectual property, copyright, licenses, privacy, trade secrets, and contractual restrictions. That’s why, with AI Escrow, we look beyond the technology, at questions such as:
- who owns each asset
- which rights are needed for storage
- which usage rights apply after a release
- which data can and cannot be deposited
- which third-party licenses or platform terms apply
- who has access to the material
- how long evidence or compliance data must be retained
- which release or access procedure applies
An escrow arrangement doesn’t create rights that don’t exist. That’s why the legal scope is aligned with the technical reality of the AI solution.
Process
How does AI Escrow work?
Every AI environment is different. Our approach is structured.
01
Analysis
We map out the AI stack, critical assets, external dependencies, and the purpose of the arrangement. Continuity? Compliance? Internal safeguarding? Or a combination?
02
Advice & Setup
Together, we determine what needs to be deposited, how often updates are needed, which verification is appropriate, and which legal agreements go with it.
03
Execution & management
Escrow4all stores the agreed AI assets in a secure, ISO 27001-certified environment. Updates run periodically or, where possible, automatically, via the Escrow Repository for repository-based assets.
Schedule a call
Has AI become business-critical for your organization?
Then it makes sense to establish which parts of your AI you can actually restore, reproduce, and prove. Escrow4all works with you to map out your AI stack, its dependencies, and the assurances you need.
Frequently asked questions
AI Escrow is an arrangement in which relevant components of an AI system are independently recorded with Escrow4all. These can include source code, models, prompts, data, workflows, configurations, and documentation. Depending on the purpose, these assets can be made available under pre-agreed release conditions or retained as independent evidence and compliance information.
Traditional Software Escrow focuses on source code, documentation, and the ability to rebuild software. With AI, how a solution actually works can also depend on model weights, fine-tuning, prompts, agents, datasets, evaluations, workflows, and external AI platforms. That’s why AI Escrow looks at the entire relevant AI chain.
Already have a Software Escrow or SaaS Escrow arrangement with an AI vendor? Then it’s wise to check whether that arrangement adequately covers the AI-specific components.
Only if the depositing party has the necessary technical access and legal rights. With proprietary foundation models, that’s usually not the case. So we record which external model the solution depends on and secure the assets that are under the control of the vendor or user, including a recovery or migration strategy where needed.
No. The EU AI Act does not require AI Escrow. The regulation does impose obligations on certain AI systems and models around documentation, traceability, logging, and record-keeping. An independent escrow deposit can help record certain information in a verifiable and auditable way, but it is not in itself proof of full compliance.
Not necessarily. Sometimes storing the full dataset is both desirable and permitted. In other situations, privacy, copyright, license terms, size, or security make that undesirable or impossible. In those cases, dataset manifests, hashes, schemas, provenance information, selection criteria, or controlled references can be recorded instead.
That depends on how quickly the AI solution changes. For some systems, a periodic snapshot is enough. When code, prompts, or configurations change frequently, a higher frequency or automatic synchronization may be more appropriate. The frequency is set when the arrangement is established.
Yes, depending on the agreed verification scope. This can range from checking for presence and integrity to restoring components of the AI stack and running pre-agreed technical or functional tests.
Yes. Independent storage, version history, and controlled access can be especially valuable for AI developed in-house. For example, to reduce key-person risk, dependence on external developers, or the loss of critical AI knowledge.
As a Dutch Trusted Third Party, Escrow4all offers three distinct applications: AI Continuity Escrow for vendor risk, AI Evidence Escrow for compliance and proof, and Internal AI Escrow for safeguarding internal knowledge. We combine a joint legal and technical approach, ISO 27001-certified processes, more than 17 years of escrow experience, and a focus on European data sovereignty. Our approach starts with an analysis of your specific AI stack, not with a standard contract.
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