Enterprise AI Data Security: A Buyer's Checklist

Rolling out AI agents across a company means handing your most sensitive data — customer records, contracts, source code — to models you do not control. Before you sign anything, work through this buyer's checklist so security is a design decision, not an afterthought.

Start with where your data lives

The first question is geography. If your data must stay in the EU, "the vendor has a European office" is not the same as "your prompts and documents are processed on EU infrastructure." Ask exactly where inference runs, where embeddings are stored, and where logs are retained.

AgentWorks is EU-native — built in the Netherlands — and offers EU data residency with EU model endpoints where they are available. Because the platform routes across multiple providers, you should confirm residency per model: some frontier models are only hosted outside the EU, so a residency-strict workload may need to be pinned to EU-hosted or local models. For the strictest cases, the Enterprise plan supports self-host and private-cloud deployment, so data never leaves your own boundary.

A practical checklist item: can you restrict a given agent or team to EU-only endpoints, and can you prove it in an audit? If the answer is "trust us," keep asking.

Demand no-training and zero-retention contracts

The single biggest fear with enterprise AI is that your data becomes someone else's training set. The mitigation is contractual, not just technical. You want written commitments that your inputs and outputs are never used to train models, and that providers retain nothing after a request completes.

AgentWorks uses no-training, zero-retention model contracts with the providers behind the platform, and a Data Processing Agreement (DPA) is available on request. When you evaluate any vendor, get these terms in writing and check they flow through to every underlying model — a no-training promise on the platform means little if a sub-processor quietly retains prompts. The AgentWorks compliance and trust pages set out how these commitments are handled.

Mask PII before it reaches the model

Even with strong contracts, the safest data is data the model never sees in raw form. Personally identifiable information — names, emails, national IDs, card numbers — should be masked at the gateway, before any prompt leaves your environment for a model.

On AgentWorks, PII is masked at the gateway before any model call, including for retrieval-augmented generation over your own documents. That matters for knowledge and RAG workloads: you can upload PDFs, DOCX, CSVs, or connect Notion and Confluence, and the system answers with citations — and says "I don't know" when an answer is not in your knowledge base, rather than inventing one. Grounded answers plus gateway-level masking keep sensitive fields out of model context while still letting agents work with your real data.

When you score vendors, ask whether masking happens before or after the data reaches the model. "After" is not masking.

Lock down access with SSO/SAML and least privilege

Data security is also identity security. If AI agents can act on your CRM, your repositories, or your finance system, then who can use which agent — and what each agent is allowed to touch — becomes a governance question.

Enterprise AgentWorks supports SSO/SAML, so access is managed through your existing identity provider and deprovisioning is instant when someone leaves. Below that, the platform enforces role-based access, shared versus personal knowledge bases, and org, team, and user budgets so no single user or runaway agent can burn through spend. Integrations with Slack, Teams, Salesforce and more should always be scoped to least privilege: connect only the systems an agent genuinely needs.

Checklist items here: SSO/SAML on your IdP, role-based permissions, per-connection scoping, and budget caps that a compromised account cannot exceed.

Insist on auditability and human-in-the-loop control

Security is not only about prevention — it is about being able to answer "what happened, and who approved it?" after the fact. That requires two things: a complete record, and a checkpoint before irreversible actions.

AgentWorks keeps an immutable, append-only audit trail that you can export as CSV or JSON, so every step of every agent run is traceable. On multi-agent pipelines — research to draft to review to publish — each step is logged with its own risk class, and human-in-the-loop approval is required on state-changing actions. That means an agent can draft an email or prepare a record, but a person signs off before it sends or writes.

This governance layer is also how AgentWorks stays EU AI Act-ready. Note the wording carefully: ready, not blanket "compliant." Whether a specific deployment is high-risk depends on your use case, so the platform gives you the tools — per-agent risk classification, approvals, audit trails — while you own the classification. The EU AI Act overview explains how these pieces map to the regulation.

Match the plan to your risk profile

Not every workload needs the same controls, and you should not overpay for ones you will not use. Lighter internal tasks can run on standard plans; regulated, high-sensitivity workloads belong on Enterprise, where self-host, SSO/SAML, local models, and an SLA are on the table.

It helps to pilot before you commit. You can trial 50+ pre-built agents and multi-model chat on the Free plan, prove the security model on low-risk data, then scale up. Review the pricing tiers against your requirements: Free and Pro for experimentation, Team for shared knowledge and admin, and Enterprise when residency, self-host, and identity integration are non-negotiable.

Summary: For enterprise AI data security, verify five things in writing: EU data residency (or self-host), no-training and zero-retention contracts, PII masked at the gateway, SSO/SAML with least-privilege access, and an immutable audit trail with human-in-the-loop approval. AgentWorks provides all five, with the strictest controls on the Enterprise plan.

Frequently asked questions

Does AgentWorks train its models on our data?

No. AgentWorks uses no-training, zero-retention contracts with the model providers behind the platform, so your inputs and outputs are not used for training and nothing is retained after a request completes. A DPA is available on request to put these terms in your own agreement.

Can we keep all data inside the EU?

Yes, with a caveat worth checking. AgentWorks is EU-native and offers EU data residency with EU model endpoints where they exist. Because some frontier models are hosted outside the EU, residency-strict workloads should be pinned to EU-hosted or local models, or run via self-host on the Enterprise plan.

How is personally identifiable information protected?

PII is masked at the gateway before any prompt reaches a model, including for retrieval over your own documents. Combined with grounded, cited answers and an immutable audit trail, this keeps sensitive fields out of model context while agents still work with your real data.