Audit-Ready AI Data Governance Services
Most healthcare AI projects do not fail in the model. They stall when a compliance officer asks where the training data came from, who approved its use and whether patient information reached a vendor, and nobody can answer in writing.
Cabot delivers AI data governance services for health systems, payers and digital health companies. We turn policy into controls that run inside your pipelines, so lineage, access rules and approvals are recorded as the work happens and not reconstructed before an audit. Every engagement starts with one AI use case and the data behind it, because that is the fastest way to a governed system you can show to a regulator.
Lineage · PHI controls · Audit evidence
No obligation. Your details stay private.
60%
of organizations are predicted to fail to govern AI successfully by 2027 because they neglect the culture and ownership side of data governance
Dec 2027
new application date for EU AI Act duties on stand alone high risk AI systems, which reach US firms that serve European patients or partners
15+
years building healthcare software for providers, payers and digital health companies
What Is AI Data Governance, and How Does It Differ From Traditional Data Governance?
AI data governance is the set of policies, controls and evidence that decide which data an AI system may use, prove where that data came from and show who approved its use, from training and retrieval through to the answers the system gives.
Traditional data governance was built for reports and records: who owns a table, how it is defined, who may read it. AI adds data flows that older programs never covered. Training sets, retrieval indexes, prompts, model outputs and clinician feedback all carry patient information, and each one can leave your boundary or drift from its source without anyone noticing. Data governance for AI answers three questions for every use case. Is the data good enough to use? Is it allowed to be used this way? Can you prove both after the fact?
AI governance sits above this layer and covers model risk, ethics and oversight policy. Healthcare data governance sits beside it and covers clinical records. This layer is where they meet, and it is the layer auditors test first. Building and tuning the models themselves sits with our machine learning development services.
Why Do Healthcare AI Projects Stall on Data They Cannot Prove or Protect?
Nobody can trace the data. A model gives an answer and no one can say which records shaped it. Without lineage, a reviewer has nothing to verify and a clinician has no reason to trust it.
Patient data leaks into AI workflows. Records are pasted into prompts, copied into notebooks and sent to outside APIs before privacy has reviewed the path. One careless step creates a reportable event.
Policies live on paper, not in pipelines. A governance document exists, but nothing in the code enforces it. Access, retention and masking depend on people remembering the rules.
Governance arrives as a late brake. Review begins after the build, so every finding means rework. Teams learn to route around governance instead of through it.
Ownership is split and nobody decides. IT owns the platform, compliance owns the rules and clinicians own the meaning. With no named owner per data domain, AI requests wait in queues.
The rules keep moving. HIPAA guidance, state privacy laws and the EU AI Act change on different clocks. A program built for one date breaks on the next unless it is designed to adapt.
What Does an AI Data Governance Engagement Cover?
Data discovery and classification
An inventory of the data each AI use case touches, with patient information, sensitive fields and risk tiers labeled across structured tables and unstructured text.
Lineage and provenance
A recorded path from source system to training set, retrieval index and model output, so any answer can be traced back to the records behind it.
Access and PHI controls
Role based access, masking, de-identification and minimum necessary rules enforced in the pipeline, including the boundary around any outside model. Related work includes our healthcare integration services and health data analysis.
Data quality for AI
Automated checks for completeness, freshness, duplicates and drift, with results that feed the analytics built through our health data analysis work.
Policy and approval workflows
A review path for each AI use case that records who asked, who approved and what was allowed, with owners named for every data domain.
Monitoring and audit evidence
Logs, dashboards and exportable evidence packs that answer an auditor in hours, plus alerts when data or usage moves outside policy. Running models in production sits with our LLMOps consulting services.
What Will AI Data Governance Cost?
Cost follows the number of AI use cases, the systems the data passes through and how much enforcement sits in code, far more than it follows the number of policies. Estimate a program in a few minutes, or put a specific question to an engineer.
Where Does AI Fit in a Data Governance Build?
Three commitments apply to every engagement. We stay model neutral, so we choose the model for the task and your policy and not the other way round. An engineer reads and approves every change before it ships. And we never use your code or your data to train a model.
Which Stack and Tools Do We Use to Govern Data for AI?
We work with the catalog, cloud and quality tools you already run, and add only what a use case needs. Every stage produces something you can review, and the table shows where a person steps in.
Governance stack
Catalog and lineage
Quality and transformation
Security and cloud
Where AI helps in governance, and where a person decides
How Do You Choose Among AI Data Governance Services and Tools?
Which Healthcare Organizations Does Cabot Build Governance For?
Health systems
Your risk is an AI pilot that touches patient records before privacy has approved the path. We govern the data first so clinical teams can adopt AI with confidence. For the AI builds themselves, see our AI development services practice.
Digital health companies
Your customers ask for proof in every security review. We build governance that produces the evidence, so a questionnaire becomes an export and not a project.
Payers and life sciences organizations
Member and trial data move across many parties. We trace and control that movement so AI on claims, outcomes or cohorts stays defensible.
Which Standards and Security Controls Does Governed AI Data Have to Meet?
Security comes first. Access is least privilege and role based, data is encrypted in transit and at rest, credentials stay in a managed vault, patient data never lands in application logs and every access is written to an audit trail.
Regulatory expectations follow the market. In the US the HIPAA Security Rule and the minimum necessary standard shape every design, and business associate agreements cover each vendor that touches patient data. We align controls to frameworks such as the NIST AI Risk Management Framework and ISO 42001, and your counsel confirms which obligations apply to you.
How Does an AI Data Governance Engagement Move From Inventory to Audit Evidence?
The process runs in six steps, and each one ends with a decision that is yours.
1. Inventory the AI data
Cabot lists the data each AI use case touches, where it lives and who uses it. You decide which use case and which data sources go first.
2. Classify and tier the risk
Cabot labels patient information and sensitive fields and assigns a risk tier to each use. You decide which data is off limits and which needs extra controls.
3. Set policies and owners
Cabot turns your rules into written policies and names an owner for every data domain. You decide who approves each use.
4. Build controls into the pipeline
Cabot codes access, masking and retention rules into the pipelines in short reviewable increments. You decide which rules run automatically and which need a person.
5. Prove it with lineage and tests
Cabot records lineage, tests every control and assembles the evidence pack. You decide what your auditors and customers will see.
6. Operate and review
Cabot monitors data use, alerts on drift and reviews policies on a schedule. You decide the review cadence and how new AI use cases join the program.
Who Builds and Owns Your Governance Program?
The team has one accountable owner per risk. A governance architect owns the policy model and the control design. A data engineer owns pipelines, lineage and quality checks. A security and privacy engineer owns access, masking and the model boundary. A QA engineer owns test evidence. A delivery lead owns the plan and your questions.
Our team is deepest in four areas: finding patient identifiers in structured data and unstructured text, lineage across EHR sources, warehouses and AI pipelines, policy enforced as code for access, masking and retention, and evidence packs shaped for HIPAA and customer security reviews.
You work with the team directly. Demos run on your real data in a protected environment, the backlog is shared, engineers join your channels and every policy decision is logged with its reason.
Why Healthcare Leaders Choose Cabot for Audit-Ready AI Data Governance
Fast delivery
A first governed use case reaches production quickly, using the approach behind our MVP development services.
Customization
Controls fit your systems and your risk appetite, backed by our wider healthcare software development services.
Security and compliance
Access rules, audit trails and evidence are part of the build, supported by our healthcare compliance services.
Healthcare data experience
We know how patient data sits in EHRs, claims feeds and warehouses, and where it escapes.
Governance that ships with the AI
Controls are built beside the use case, not after it, so review never becomes a late brake.
Engineering built for review
AI speeds the repeatable work and engineers own every decision.
Client Success Stories
AI-Accelerated Discovery to MVP for a HIPAA-Compliant Patient and Physician Marketplace
See how Cabot took a HIPAA-compliant, four-portal telehealth marketplace from discovery to a working MVP with licensure-aware booking, video, and payments.
Read the case study
Text2SQL with Streamlit
Learn how Cabot used Python and Azure OpenAI to build a Streamlit app that turns plain-English questions into SQL and delivers real-time answers for faster analysis.
Read the case study
FHIR Server & FHIR Auth Configuration for Secure API Access
See how Cabot built a FHIR-compliant server with Firely Auth (OAuth2/OpenID Connect) and MSSQL for secure, standards-based access to healthcare data.
Read the case study
Automated Patient Summary Generation and Eligibility Assessment
Cabot built an AI system to generate patient summaries, run eligibility checks, prioritize referrals, and deliver dashboards, cutting intake time and errors.
Read the case studyWhat Should You Look at Next?
You need governed data turned into trusted analytics
Governed data feeds dashboards and models people can trust. See healthcare data analytics services.
You want answers grounded in your own records
Clinical and policy documents need an assistant that cites its sources. See RAG development services.
You need to monitor and run models in production
Quality, cost and data use all need watching once a model is live. See LLMOps consulting services.
You want AI that acts on governed data
Agents that complete multi-step work need strict data boundaries. See AI agent development services.
Our Clients
AI data governance is the set of policies, controls and evidence that decide which data an AI system may use, prove where that data came from and show who approved its use. It covers the data used to train, ground and prompt a model, and the outputs the model produces.
Traditional data governance manages data for reports and records, AI governance manages model risk, ethics and policy, and this layer connects the two by controlling the data every AI system touches. A health system usually needs all three, and the data layer is the one auditors ask to see first.
HIPAA requires safeguards for protected health information, use of only the minimum necessary data, and a business associate agreement with any vendor that handles it, including a model provider. Data can also be de-identified by Expert Determination or by removing the 18 Safe Harbor identifiers. Your counsel confirms what applies to each use case.
Only with a business associate agreement in place and only with the data the task needs. Many teams de-identify the text first, or run the model inside their own cloud boundary so records never leave it. We design that boundary with your privacy officer before any prompt is written.
Cost follows the number of AI use cases, the systems the data passes through and how much enforcement you want in code, not the number of policies written. Use the Cost Calculator for a first estimate, then confirm the scope in a scoping call.
A first governed use case can reach production in a short series of sprints, while the full program grows use case by use case. Access to data owners and the speed of privacy and security review set the pace more than engineering does, so we start those conversations in week one.
No. We start with the data behind one AI use case and build only the catalog and lineage that use case needs. If you already run a catalog, we connect to it and fill the gaps instead of replacing it.
One executive owner, usually the chief data officer or chief information officer, with named data stewards from clinical, privacy, security and IT. A small governance council that approves AI use cases keeps decisions fast, and the controls we build make each decision enforceable and visible.