Operational AI Governance — Not Slide-Deck Compliance
AI governance isn't a document. It's a set of operational controls embedded in your systems — defining what AI can do, when humans must intervene, and what happens when things go wrong.
We build governance that's real: model intake checklists that actually get used, logging that actually captures decisions, and fallback paths that actually route to humans.
Governance framework components.
Each component is operational — built into workflows, not filed in a drawer.
Model Intake Protocol
Before any AI model enters your operations, it passes through a structured review: what it does, what data it uses, who owns it, and what happens when it fails.
Vendor Questionnaires
Standardized evaluation criteria for AI vendors — covering data handling, model transparency, training data provenance, and liability boundaries.
Logging & Audit Trails
Every AI decision is logged with context: what was the input, what was the output, what confidence level triggered the action, and who reviewed it.
Human Fallback Paths
Critical decisions always route to a human. We define explicit confidence thresholds, escalation triggers, and override procedures for every automated step.
NIST AI RMF Alignment
Governance controls mapped to the NIST AI Risk Management Framework — Govern, Map, Measure, Manage — so your AI practices align with federal guidance.
Incident Response
When an AI system produces unexpected results, what happens? We build documented response procedures — not hoping someone figures it out.
What we don't do.
- Write compliance policies that sit in binders
- Create slide decks about "responsible AI"
- Provide theoretical risk assessments
- Sell governance software or platforms
If your governance doesn't affect what happens when a model runs at 2am on a Saturday — it's not governance. It's marketing.
The admission ticket to deploying AI in healthcare.
HIPAA Security Rule strengthening and rising cyber threats mean "secure AI workflows" is not a buzzword — it's the prerequisite for deploying LLMs inside real operations.
- BAA alignment — Vendor agreements that explicitly cover AI model access to ePHI
- Access controls — Role-based access, audit logging, and session management for AI-mediated workflows
- ePHI logging — Every AI interaction with protected health information is logged with context and reviewable
- Safe prompt patterns — Data handling protocols that prevent PHI leakage in model inputs and outputs
- Recognized security practices — Controls aligned to NIST SP 800-66 Rev. 2 and HIPAA Security Rule NPRM expectations
Healthcare is one example — not the only one.
AI governance applies everywhere organizations deploy AI into operations.
Healthcare
HIPAA Security Rule-aligned AI controls for clinical documentation, PA processing, and patient data handling — with ePHI logging and access governance.
Learn more →Service Businesses
Governance for AI-driven scheduling, client communication, and automated marketing — ensuring customer data stays protected.
Learn more →Operations-Heavy Orgs
Controls for AI triage, extraction, and summarization in organizations with complex exception-handling requirements.
Learn more →Every governance engagement begins with the assessment.
We don't build governance in a vacuum. The Workflow Stability Assessment reveals where AI is currently deployed, what controls exist (if any), and what's needed to bring operations into alignment.
From there, we build the governance framework that matches your actual risk profile — not a generic template.
Govern what's real.
The Workflow Stability Assessment is the first step toward AI governance that actually works.
Get a Stability Verdict