AI GOVERNANCE
Close the gap between
Most enterprises govern AI through policies, committees, and reviews. The risk lives in what AI actually does in production. A3R helps regulated enterprises close that gap and show the evidence to examiners, auditors, and boards.
The Enforcement Gap
There is a gap between governance policy as written and AI behavior in production. In most enterprises it shows up as:
Policy on paper
Policies live in documents that no system enforces.
An inventory that drifts
The AI inventory is out of date soon after it’s finished.
Evidence by hand
Evidence is assembled when an examiner asks, instead of produced as AI runs.
Definitional drift
The same business term is defined differently across systems, so no one can explain an AI output with confidence.
Where are you today?
The four stages of AI governance maturity
1. Reactive
Governance happens after an incident or an audit finding.
2. Procedural
Policies, committees, and review gates exist, but they sit outside the systems.
3. Tooling-Augmented
Inventory, registry, and monitoring tools help, but they are disconnected and reconciled by hand.
4. Runtime-Enforced
Policy is enforced where AI acts, and evidence is produced as it runs.
What stage four looks like: visibility by default · lineage by design · policy enforced at the point of interaction · accountability mapped to action
How we work
Foundation: AI Governance Assessment (about 4 weeks)
- An inventory of the AI in use: LLM models, ML models, and AI built into vendor software
- Risk tiering for each use case
- A maturity baseline against the four stages
- A gap report against the frameworks you answer to
- A prioritized 90-day roadmap
Framework: Governance operating model
- Policies, risk tiers, a control library, and an approval lifecycle
- Three-lines-of-defense roles that fit your existing risk and model risk programs
- Documentation standards: model cards for the ML models you build, and vendor documentation intake for the LLM models you buy
Implementation: Moving controls into execution
- Policy enforced where AI and data interact
- Lineage from source data to AI output
- Continuous monitoring, with examiner-ready evidence produced as part of normal operations
- Built on the platforms you already run, with new tooling only where a gap requires it
LLM models and ML models need different governance
ML models
Classification, regression, forecasting, recommendation, computer vision. You own the training data and the model, so you govern them through validation, performance and drift monitoring, and documentation.
LLM models
GPT, Claude, Llama, and others. The vendor owns the training, so you govern what you control: the use cases, data sources, prompts, and outputs. You also test what the vendor’s documentation doesn’t cover.
Frameworks we align to
- NIST AI Risk Management Framework
- ISO/IEC 42001
- Interagency model risk management guidance (SR 26-2 / OCC Bulletin 2026-13)
- EU AI Act, where it applies
- Sector rules, including HIPAA, GLBA, and state privacy laws
Industries
- Banking and financial services: model inventory, fair lending, exam readiness
- Insurance: underwriting and claims models that regulators can understand
- Healthcare: clinical and operational AI, with protected health information controlled
Who you'll work with
A3R is founder-led. Our team brings 125+ collective years in enterprise transformation and governance across six industries, including Big 4 consulting leadership.
Start with the assessment.
In about four weeks, you’ll know what AI you’re running, where you stand on the maturity curve, and what to fix first.