Sectors / Public sector
Transparency and procurement governance across data and AI.
Public bodies answer to citizens, auditors and legislators. aerais helps you show how data and AI are acquired, used and explained.
Why it matters
Why governing data and AI together matters in public sector
Government agencies and public institutions use data and AI to deliver services, allocate resources and detect risk. Decisions can affect rights and access, so the public expects them to be explainable and contestable.
Much of the AI in use arrives through procurement or embedded in vendor platforms. Governance therefore starts before deployment: what was bought, what data it touches, and what the supplier must disclose and evidence.
Typical questions
Five questions we hear in this sector
- Which automated or AI-assisted decisions affect the public, and who is accountable for each?
- What do procurement documents require suppliers to disclose about models and data?
- Can we explain an outcome to a citizen or an oversight body?
- How is data shared across agencies governed, and by whom?
- How do records, access and retention rules apply to AI inputs and outputs?
Chain of custody
How the chain applies
Data plane controls here are cataloging of shared and open data, classification, lineage across agency boundaries, retention and access controls, and privacy operations for requests.
These seam loops tend to matter most:
Data-to-AI lineage
Explaining a decision to a citizen or ombudsman requires the path from outcome back to the data used.
Unified control crosswalk
Agencies face transparency, privacy, records and AI rules together. One crosswalk reduces duplicated assurance work.
Quality-gated release
Procured models should enter service only when data and acceptance checks have passed, with evidence retained.
Entry points
Where engagements usually begin
In public sector, a first engagement often centers on an inventory of automated decision systems and procurement governance, with cataloging and lineage for shared data. Most organizations start from AI governance and bring in data governance as the chain extends. A fixed-scope diagnostic is the usual first step; see engagements for how that is structured.
Regulatory context
Regulatory context, at a high level
Public bodies commonly operate under freedom of information and records rules, public procurement law, and algorithmic transparency or automated decision instruments (for example Canada's Directive on Automated Decision-Making). Privacy law applies to personal data, and the EU AI Act sets obligations for public-sector uses it classes as high risk.
This is general context only. Requirements vary by jurisdiction and change over time, so confirm current requirements with counsel and regulators. Alignment with a standard is not certification.
Diagnostic
What a diagnostic would look at here
- Inventory of automated decision systems and vendor-embedded AI
- Procurement language on model and data disclosure
- Cross-agency data sharing and the approvals behind it
- Ability to explain a decision from outcome back to data
- Records, retention and access practice for AI inputs and outputs
Each item is examined against the chain of custody: whether the control exists, whether it runs inside the systems rather than on paper, and whether it leaves evidence a reviewer can follow. Nothing is assumed from documents alone, and findings are written for executives and boards as well as for practitioners.
The output is a current-state maturity view, risk exposure and a prioritized roadmap. Request a diagnostic to scope it for your organization.
FAQ
Frequently asked questions
Can aerais support procurement of AI?
Yes. We help define disclosure, evidence and data requirements for supplier terms, and map them to your governance controls. Legal drafting stays with your counsel.
How does this support transparency?
By keeping a traceable record from a decision back to its data, model version and approvals, which agencies can use to explain outcomes to citizens and oversight bodies.
Do you work across agencies?
Governance is designed as federated with central oversight, so shared standards apply while individual agencies keep ownership of their data.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.