AI agents that act.
Governance that proves it.

When we launched the 30-Day AI Governance Launch Program, the promise was simple: bundle an AI Risk Assessment, a DPIA/AI Impact Assessment, and a Responsible AI Audit into one fixed-scope engagement — and take a regulated organization from AI experimentation to audit-ready AI operations in weeks, not quarters. Here's what the program actually did for our first cohort of clients in healthcare and financial services, and the results they walked away with.

Week 1–2: What the AI Risk Assessment found

The risk assessment inventoried every AI system and shadow AI tool touching enterprise data. Across the cohort, discovery surfaced dozens of unauthorised AI touchpoints per organization — consumer chatbots handling sensitive notes, embedded AI features inside sanctioned SaaS that legal had never reviewed, and agents running without registered identities. Each was scored for operational, security, and regulatory exposure, giving leadership its first complete, defensible AI inventory.

Week 2–3: What the DPIA / AI Impact Assessment delivered

For the highest-risk use cases, we produced the documented Data Protection Impact Assessments and AI impact assessments that regulators, DPOs, and enterprise customers now expect before AI touches personal or sensitive data. For one healthcare client, this became the evidence pack that finally unblocked a clinical AI deployment stuck in compliance committee — mapped to HIPAA, the EU AI Act, and NIST AI RMF controls.

Week 3–4: What the Responsible AI Audit changed

The independent audit reviewed fairness, transparency, accountability, and human oversight across each AI portfolio, closing with a prioritized remediation roadmap. Every client left with governance controls wired into a live control plane: agents registered, identity-bound, policy-enforced, and stoppable in under 50 milliseconds.

The results

The headline outcomes across the first cohort: a sharp drop in audit-preparation hours once immutable agent audit trails replaced manual evidence gathering, full visibility into previously uninventoried AI usage, and — most importantly — AI initiatives that had stalled in review moving into governed production. One compliance lead put it plainly: the 30-day evidence pack answered, in writing, the questions their auditor had been asking for a year.

Want these results in your next 30 days? Start the program →

We're proud to become a member of the Austin Chamber of Commerce, deepening our roots in the city's fast-growing tech ecosystem. As Austin cements itself as a national hub for AI and enterprise innovation, we'll be working alongside fellow members to help local businesses adopt agentic AI safely — with the governance, security, and compliance foundations regulated industries demand.

Expect us at Chamber tech roundtables, AI readiness workshops, and community events throughout the year.

Fellow Chamber member? Let's connect →

Clinical AI agents are now touching PHI at scale — and 2026 is the year regulators expect proof, not policy documents. Health systems that deployed agents in 2024–25 are discovering that "we have an AI policy" no longer satisfies an auditor who asks which agent read which record, when, and under whose authority.

What a defensible audit trail looks like

Every agent action logged immutably, bound to a cryptographic identity, and mapped to the policy that authorised it. If your audit trail can be edited after the fact, it isn't an audit trail — it's a liability.

BAA structure for agentic systems

Business Associate Agreements written for human vendors don't cleanly cover autonomous agents acting across multiple systems. The BAA needs to name the control plane, the data boundaries, and the enforcement mechanism — not just the vendor.

The kill switch is now table stakes

In a live health system, the question isn't whether an agent will misbehave — it's how fast you can stop it when it does. Session-level blocking measured in milliseconds, before data moves, is what "under control" means in 2026.

Running clinical AI? Ask about a HIPAA-aligned governance review →

Most healthcare AI pilots still die in committee. The ones reaching production this year share three traits — and none of them is a better model.

1. Data-to-agent lineage

Compliance teams sign off when they can trace exactly which data an agent touched and under which policy. Lineage isn't a nice-to-have report; it's the artifact that gets a deployment approved.

2. Clinician-in-the-loop escalation paths

Successful deployments define — in writing, before go-live — which decisions the agent makes autonomously, which it recommends, and which it must escalate to a clinician. Ambiguity here is what committees correctly veto.

3. Governance wired in before the first patient record is read

Retrofitting controls onto a live agent is an order of magnitude harder than building on a governed control plane from day one. The pilots that scale are the ones that were governable from their first session.

Stuck between pilot and production? Let's talk →

Clinicians and staff are quietly connecting consumer AI tools to workflows that touch patient data. It's rarely malicious — it's a nurse pasting notes into a chatbot to save time, or a department adopting a transcription tool that never went through procurement.

Why it's invisible to traditional security

These tools ride on approved devices and sanctioned browsers. Nothing "breaches" — data simply walks out through a workflow no one inventoried. Standard DLP and CASB tooling wasn't built to recognise agentic AI traffic.

What health systems find in week one of discovery

Shadow AI discovery consistently surfaces dozens of unauthorised AI touchpoints in the first week — from consumer chatbots to embedded AI features inside sanctioned SaaS that legal never reviewed. The inventory itself usually reshapes the AI governance roadmap.

Containment without a crackdown

Banning AI outright drives usage further underground. The systems getting this right pair discovery with sanctioned, governed alternatives — giving staff a faster, compliant path to the same outcome.

Want to know what's already connected? Request a shadow AI scan →