AI governance
Governing AI isn't about ticking a regulatory box. It means knowing, at any moment, which systems you run, what they decide, who answers for them, and when to shut them down. Compliance comes next — and you prove it with what this governance produces.
Most companies discover they need to govern their AI the day someone asks who approved something. By then, there are live models nobody reviews and generative tools running on personal accounts.
Three layers, each built on the one below
You can't trust an agent built on ungoverned models, or models built on ungoverned data. This is the order we build in.
- 01
Data governance
Can I trust what I have?
Quality, lineage, ownership, and access. Who owns each dataset, where it comes from, and what state it's in. This is the foundation: whatever goes ungoverned here, every layer above inherits.
- 02
Machine learning governance
Are my models' conclusions still valid?
A model inventory, pre-deployment validation agreed with the business, drift monitoring, retraining with rollback, and a named owner for every model. Models don't crash — they drift silently while they keep producing numbers.
- 03
Generative AI and agent governance
What is it saying and doing on my behalf?
What data can leave and where it can go, what an agent can execute without human sign-off, spending limits, audit trails, and transparency toward the people it deals with. This is the layer that can almost never wait, because the risk isn't accuracy — it's exposure.
Questions you should answer without hesitation
- How many AI systems are in production, and what each one decides.
- Who is accountable for each one, by name and job title.
- What data goes in, where it comes from, and how far it can travel.
- How you know they're still working, and what threshold triggers a review.
- What shuts down automatically and what requires human sign-off.
- What each one costs and who owns that budget.
If any of these questions has no answer, that's where the work starts — and it's usually the most uncomfortable finding of an AI audit.
Governance and compliance aren't the same thing
Compliance answers to a regulator: risk classification, technical documentation, record-keeping, and human oversight under the EU AI Act and the GDPR. Governance answers to the business: systems that do what they should, someone who answers for them, and the ability to shut them down.
The order matters. With all three layers in place, much of the evidence the law requires already exists, and compliance becomes a documentation exercise. Without them, you have to scramble to produce that evidence — at a much higher cost.
Where it fits in DALIA
Governance is the final phase of DALIA — A · Adoption: governance, measurement, and scaling — and it decides whether what you implemented survives its first year. The four earlier phases choose and launch; this one sustains.
Start by knowing what you have
The starting point isn't a written policy — it's a real inventory of systems, data, and owners. From there, we decide what to govern first.