Sectors / Technology
Governance at the scale of AI-native operations.
Software and platform companies ship AI continuously. aerais builds governance into the pipelines, so controls move at engineering speed.
Why it matters
Why governing data and AI together matters in technology
Technology companies train, fine-tune and deploy models as part of the product, often across many teams and regions. Customers, partners and regulators increasingly ask how training data was sourced, how outputs are monitored and how requests are honored.
Manual review cannot keep pace with continuous delivery. Governance has to be embedded in data pipelines, model registries and release workflows so evidence is produced as a by-product of shipping.
Typical questions
Five questions we hear in this sector
- Where did training and evaluation data come from, and what were its permitted uses?
- Which models, agents and AI features are in production, and who owns each?
- How are customer deletion requests carried through to derived data and models?
- What do enterprise customers ask us to evidence, and do we answer from one source?
- How do we govern agents and retrieval systems that access internal data?
Chain of custody
How the chain applies
Data plane controls here are cataloging of datasets and features, classification and purpose tagging, lineage from source through training to deployment, and quality gates inside data and ML pipelines.
These seam loops tend to matter most:
Right to be forgotten across data and models
Deletion and withdrawal requests must reach training sets, embeddings and fine-tuned models, not only primary stores.
Contaminated-data blast radius
When a data source is found to be unsuitable, the affected models and features must be identifiable fast.
Unified control crosswalk
Customer questionnaires, privacy law and AI regimes overlap. One crosswalk lets teams answer from shared evidence.
Entry points
Where engagements usually begin
In technology, a first engagement often centers on an AI inventory and pipeline controls, with lineage and deletion handling built into data and ML delivery. 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
Technology providers face privacy law such as GDPR and US state privacy statutes, the EU AI Act (including obligations for general-purpose AI models and for providers of high-risk systems), and customer assurance expectations such as security and management system reports. ISO/IEC 42001 and the NIST AI RMF are common references for structuring an AI management system.
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
- Provenance and permitted use of training and evaluation data
- AI inventory across products, internal tools and agents
- Where governance checks sit in data and ML delivery pipelines
- Carry-through of deletion and consent changes to derived data
- Reuse of evidence across customer, regulatory and internal reviews
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
How can governance keep pace with continuous delivery?
By building checks into pipelines and registries so evidence is produced as teams ship, rather than collected in separate review cycles.
Does this help with enterprise customer reviews?
It can. A single control crosswalk and retained evidence let teams answer customer questionnaires from shared sources instead of starting each one afresh.
Is ISO/IEC 42001 relevant for technology companies?
It is a common reference for structuring an AI management system. aerais supports readiness; certification is issued by independent bodies.
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.