For Healthcare Organizations Deploying AI
Ambient scribes, CDSS, clinical chatbots, agent workflows — the AI committee approved them, but no one can show what they actually do at runtime. GLACIS runs inside your infrastructure, instruments one deployed workflow with runtime controls, and produces signed evidence receipts your AI committee, HIPAA security officer, and auditors can verify — with zero clinical payload egress.
Clinical AI is already in your environment. In the ambient scribe your clinicians use. In the CDSS your radiology team relies on. In the chatbot your front desk handed to patients. In the agent workflows procurement approved two quarters ago.
Most oversight today is paper — AI committee minutes, vendor questionnaires, quarterly attestations. None of it shows what your deployed workflow actually does on a Tuesday afternoon, or which guardrails fired when a clinician asked the model an off-label question.
The Sprint closes that gap on one deployed clinical AI workflow in three weeks — runtime controls instrumented in your stack, signed evidence receipts, and an evidence pack your AI committee and HIPAA security officer can verify.
GLACIS runs inside your infrastructure on one deployed clinical AI workflow — instrumenting runtime controls, signing each control outcome, and packaging an evidence pack your AI committee and auditors can verify without seeing protected health information.
PHI redaction, consent verification, scope-of-use limits, content filtering — the controls your AI committee specified now execute and emit signed evidence at runtime, beside your existing safety stack.
PHI, clinical notes, prompts, and responses stay inside your environment. Only signed hashes and verification metadata leave — designed to minimize BAA scope; confirm against your specific HIPAA analysis.
Timestamped, third-party witnessed, cryptographically signed receipts assembled into an evidence pack your AI committee, HIPAA security officer, and external auditors can verify on their own.
Stop reconstructing what happened from logs and interviews. Every AI interaction that passes through your controls generates verifiable evidence automatically.
Audit prep becomes report generation, not archaeology.
Answer "how do we know our AI is safe?" with evidence, not assurances. Show them a dashboard of verified control executions, not a policy document.
Confidence backed by cryptographic proof.
No workflow changes. GLACIS observes your existing controls — it doesn’t replace them. Your clinicians keep working exactly as they do today.
Evidence generation is invisible to end users.
Give them what they actually want: proof that your governance isn’t just documented, it’s operational. Evidence they can verify without trusting your word.
Third-party verifiable, not self-attested.
If consent hasn’t been verified, the request doesn’t proceed. If PHI detection fails, the pipeline stops. GLACIS enforces your policies — it doesn’t just report on them.
You choose the failure mode. We enforce it.
Not a policy — an architecture. PHI, clinical notes, prompts, and responses are hashed locally inside your infrastructure. Only signed hashes and verification metadata cross the boundary.
Designed so GLACIS never has access to plaintext protected health information. Whether a BAA is required depends on your deployment configuration and HIPAA analysis.
The regulatory landscape for healthcare AI is shifting fast. The EU AI Act classifies most clinical AI as high-risk. State laws like the Colorado AI Act (June 2026) are proliferating. CMS and ONC are watching.
The common thread: regulators want evidence that governance actually happened, not just documentation that it was planned.
Organizations that can demonstrate operational AI governance — with verifiable evidence — will have a material advantage. Those that can’t will face increasing scrutiny.
The Agent Runtime Security & Evidence Sprint is a fixed-scope $48k engagement — three weeks, one deployed clinical AI workflow, runtime controls instrumented inside your stack, and an evidence pack mapped to HIPAA, the AI committee charter, and emerging state AI laws. No clinical workflow disruption.