Data Governance & Sovereignty for Mission-Driven AI

If you work in government or a non-profit, your data is precious. People have trusted your organization with sensitive information about themselves; in many cases, the most vulnerable community members—those who rely on the most services—have shared the most data.

Before you pilot AI, put essential data policies and protective practices in place. You should be clear about who can access which data, where that data lives, how long you keep it, and which laws, policies and community norms apply. This post gives leaders and project teams a practical path to move forward safely.

When to use this

Use this guide when you are launching an AI pilot that touches anything sensitive, like resident or client data, HR files, case notes, health information, identifiable data, or operational systems. It is also helpful for small teams that don't have dedicated staff for data security and governance, and for cross-jurisdiction efforts, like partnerships with county, city, and community-based organizations, collaborations with tribal nations, or programs serving vulnerable groups.

Four principles to anchor decisions

Minimize by default. Collect and expose only the minimum data needed for the task at hand.

Sensitivity matters. Treat data about a community with heightened care, especially if it is sensitive or identifiable (contains their name, address, a unique identifier, or just enough information that a determined person could figure out who the data is about). Raise the bar for consent and transparency accordingly.

Traceability is critical. Maintain the ability to trace where data originated, who touched it, and where it went so you can govern it well.

Law plus legitimacy. Comply with the law as a baseline and also sustain legitimacy with your community to earn and keep trust.

Data residency and sovereignty

Data residency means where your data is physically stored and processed; data sovereignty means which laws and communities get a say over that data. For mission-driven orgs—especially those serving vulnerable people or partnering across jurisdictions or with tribal nations—these choices affect legal risk, public trust, records obligations, and whether your vendor can keep data from silently crossing borders.

1. Same-region residency (lowest friction). Keep data and models within your required legal region (for example, if you operate in the US, store your data and models in the US). This approach is a good default for most agencies and NGOs.

2. Region-locked dedicated tenancy. Choose a setup where your vendor keeps data in a specific legal region, restricts admin access, and follows local laws. It usually costs more, but it lowers both security risk and political blowback.

3. Community-controlled data arrangements. When working with tribal nations or communities at higher risk, treat data as a shared resource. Use agreements that include tribal data sovereignty terms, a joint governance board, and explicit consent for any secondary use. Host the data under their jurisdiction—or in a mutually agreed sovereign environment—to strengthen trust.

4. No cross-border flows without a clear reason. If a vendor’s system moves data across borders for inference, logging, or support, require a technical fix and a contract change or pick a different vendor.

Quick litmus tests

• You should expect the vendor to prove, with auditable artifacts or other documentation, exactly where every byte of data is stored and travels.

• You should be able to delete or export your data (even your entire database) in days rather than months. Note that when you select “delete” in your software, it may not be completely deleted right away: companies often hold on to it in case you want it back or because the data is useful to them. Clarify the procedure for completely deleting data.

• You should be able to answer the community’s questions about how data security decisions are made with a named person or board that holds responsibility, rather than pointing to “the vendor.”

A one-week starter plan

Day 1: Name the steward and write the scope. Appoint a Data Steward for any pilot or other new implementation (a named individual, internal person, not “the IT department” or “the vendor”) and grant that person clear decision rights. Define the project scope in a page or less, documenting the purpose, the data types, the systems and partners involved, and the expected outputs.

Day 2: Map your data flows. Create an inventory of sources, fields, and levels of sensitivity. Describe how data is transferred: are you getting data from automatic updates, batch exports, or manual copy-and-paste? Where is it stored, which security measures apply, and which vendors are involved? Flag anything that crosses departments, agency, jurisdictional, or national boundaries.

Day 3: Create starter policies. Decide how you will minimize exposure, including whether you can use any technical measures (like synthetic, masked, sampled, or aggregated data). Specify authorization rules that clarify who may access which data and how access is granted and revoked. Establish retention rules for raw, transformed, and output data and define the triggers for deletion. List existing audit logs and identify gaps you must close—for example, access logs, prompt or query logs, output logs, and model call logs.

Day 4: Lock vendor terms and residency constraints. Set your non-negotiables before you decide. Typical baselines include a commitment not to train on your data, third-party security attestations (for example, SOC 2 or ISO 27001), timely breach notification windows, and clear rights to export or erase your data. Determine where data can physically and legally reside and decide whether your servers or the vendor’s environment is safer for this use case.

Day 5: Publish a one-page “How we use AI” notice. Write a plain-language summary of your AI use: the purpose, the data used, how outputs are checked, and how people can opt out or get help. Share it internally on your intranet, externally on a relevant program page, and incorporate it into staff onboarding.

By grounding your AI work in minimization, proximity, traceability, and legitimacy—and by making deliberate choices about residency and sovereignty—you protect people, meet your obligations, and strengthen trust. A modest week of focused effort can give your pilot a safe foundation and a clear public story about how and why you use AI.

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LLM Disclosure:
I got the idea for this post by uploading my book draft (it’s complete and going through editing right now!) and requesting 3 ideas for posts. To ensure that it didn’t overlap with past posts, I tried using Agent Mode to scrape all the posts so far from this blog and excluding those topics in my prompt. This is the first time I’ve used Agent Mode for anything that worked; once I have time to read up on it and get good at it, I will write a post about it!