Clinical action ontology
Translate tool calls into meaningful categories—chart read, order write, patient export, care-plan change—so rules reflect clinical impact.
THE INDEPENDENT AI SAFETY LAYER
Clinical-grade containment for AI agents.
Your AI agents act in milliseconds — your controls should too. Sovarch is being designed to watch every consequential action, enforce institutional boundaries before an action executes, and preserve human authority when it matters most.
◇ Vendor-agnostic by design◇ Clinically aware policies◇ Evidence by default
SOVARCH / DECISION CONTROLILLUSTRATIVE DEMOH-01
CLINICAL OVERSIGHT / COMMAND CENTER
Agent decision monitor
● SIMULATION ACTIVE SAMPLE DATA ONLY
EVALUATED ACTIONS
1,284ILLUSTRATIVEPOLICY INTERVENTIONS
37ILLUSTRATIVECRITICAL CONTAINMENTS
04ILLUSTRATIVEDECISION EVENT STREAM
TIMEAGENT / REQUESTED ACTIONDECISION
09:42:18Medication workflow agentWrite medication order · EHRBLOCK
09:42:16Clinical summary agentRead approved chart · FHIRALLOW
09:42:13Care navigation agentExport patient list · APIREVIEW
09:42:10Administrative agentAccess scheduling resourceALLOW
L3 / ACTION BLOCKED · CASE SVR-0421
Medication order write blocked: agent lacks approved prescriber authority. Human review required.
HMAC AUDIT CHAIN ◉ VERIFIED [DEMO]NO PATIENT DATA · SYNTHETIC EVENTS
Independent by architecture
Human authority retained
Action-level accountability
01 / THE CHALLENGE
More agents. More vendors. No independent watchdog.
Hospitals are adopting AI across clinical, administrative, and operational workflows. Each tool has its own permissions, logs, and controls. But who guards the actions across all of them?
The question is no longer only “What did the model say?”
It’s “What was the agent about to do?”
It’s “What was the agent about to do?”
01 /
Fragmented visibility
No single independent inventory of agent identities, credentials, tools, and destinations across vendors.
02 /
Too late to intervene
After-the-fact alerts cannot undo an unsafe chart modification, data export, or unauthorized order.
03 /
Missing clinical context
Traditional security controls may see an API call without understanding its clinical consequence or blast radius.
04 /
Evidence scattered everywhere
Security, clinical governance, risk, and audit teams piece together incidents from disconnected records.
02 / HOW SOVARCH WORKS
Five moves between an agent and an incident.
A proposed independent control plane around AI execution — observe what can act, determine what is allowed, and intervene before an unsafe boundary is crossed. Deterministic rules enforce the boundary; AI helps interpret, but it never holds the keys.
01 / DISCOVER
Inventory
Register agents, models, credentials, permissions, tools, and approved destinations.
02 / DEFINE
Enforce
Apply deterministic institutional policy to clinically meaningful actions.
03 / APPROVE
Gate
Route high-impact actions through human approval when the risk demands it.
04 / RESPOND
Contain
Restrict permissions, block actions, revoke access, or isolate compromised agents.
05 / ATTEST
Prove
Preserve tamper-evident decision records and export review-ready evidence.
RISK-BASED INTERVENTION
From observation to isolation.
Four configurable response levels give institutions explicit authority over what happens next. AI may assist with interpretation; deterministic rules decide enforcement.
EXPLORE RESPONSE LEVELS →
L1 — LOG. Preserve the action, identity, context, and decision in an audit record. No interruption is required.
One SaaS platform. Five connected layers.
PLANNED SYSTEM ARCHITECTURE / V1–V201 / EXPERIENCEConsole
Customer-facing security command center, reviews, and approvals.
02 / EXECUTIONBackend
Always-on policy evaluation, gates, incidents, and containment.
03 / EVIDENCEDatabase
Inventory, policy versions, and planned hash-chained audit records.
04 / INPUTSConnectors
EHR, cloud audit, SIEM, agent APIs, and optional private collectors.
05 / TRUSTWebsite
Public presence, partner access, and a future Trust Center.
ROADMAP LAYER: SANDBOX TESTING · ROLE-BASED ACCESS · INTAKE WORKFLOWS · FRAMEWORK EVIDENCE EXPORTS
03 / THE SOVARCH ADVANTAGE
Security that understands what an AI action means.
Governance platforms don’t contain agents. Agent-security tools don’t speak healthcare. Sovarch is designed to be the intersection — six capabilities that complement, not replace, existing security, EHR, and clinical governance systems.
Blast-radius mapping
Estimate which patients, systems, roles, data stores, and downstream workflows a single agent action could affect.
Canary workflows
Use decoy resources and safe test actions to detect overreach, misrouting, and unintended agent behavior before harm.
MCP policy gateway
Enforce identity, tool allowlists, purpose and destination constraints at supported Model Context Protocol boundaries.
Ephemeral credentials
Favor short-lived, least-privilege credentials that expire or revoke as agent tasks and risk conditions change.
One-click evidence
Package policy decisions, approvals, cryptographic record links, incident timelines, and framework mappings for review.
04 / BUILT FOR HEALTHCARE
Clinical systems demand more than generic guardrails.
Sovarch is being architected for healthcare's interoperability constraints, access-control obligations, and accountability requirements—with deployment and feature availability validated in each design-partner environment.
FHIR / HL7EHR integration patternsSIEM + audit logsRBACPrivate collectors
HIPAA-conscious by design
Minimum-necessary ingestion, access controls, encryption, and customer-specific security review are design requirements—not a claim of certification or current compliance attestation.
Integration without invented partnerships
Proposed interoperability through FHIR/HL7, APIs, log pipelines, MCP, and on-premise collectors, subject to vendor capabilities, access rights, and health-system approvals.
Human-in-command governance
High-impact clinical workflows can require named human approvers, explicit review queues, escalation paths, and complete decision trails.
DESIGNED FOR EVIDENCE ALIGNMENT
Framework & trust mapping
CHAI Applied Model CardModel inventory · intended use · transparency
DESIGN ALIGNMENT ↗NIST AI RMFGovern · Map · Measure · Manage
DESIGN ALIGNMENT ↗ISO/IEC 42001AI management system evidence
DESIGN ALIGNMENT ↗HIPAA / BAA postureSecurity and contracting workflow
UNDER DEVELOPMENT ↗Evidence exportsPolicy, approvals, incidents, cryptographic links
PLANNED ↗IMPORTANT: References describe intended alignment and product architecture. They do not indicate certification, CHAI membership, endorsement, completed BAAs, regulatory approval, or current production readiness. No PHI should be sent through this demo.
05 / PUBLIC SECTOR
Mission-focused. Accountability-led.
Veteran-founded with a mission to bring measurable oversight and controlled adoption to high-consequence AI environments, including public healthcare and government operations.
Architecture decisions are made with federal security authorization in mind from day one, and Sovarch is preparing for public-sector procurement conversations, architecture reviews, and future security requirements. Eligibility, certifications, and contracting representations will be made only after formal verification.
06 / DESIGN-PARTNER PILOTS
Help define the standard before it becomes one.
Sovarch is pre-launch. We are inviting a limited group of healthcare leaders, AI platform teams, clinical informaticists, and security operators to help validate the control plane in scoped, non-production workflows. Design partners get direct influence over the roadmap, founder-level attention, and preferred terms at launch.
- ↗ Map agent inventory, workflows, and critical actions
- ↗ Co-design risk policies and human approval gates
- ↗ Validate detection and evidence flows using synthetic data
- ↗ Establish technical and privacy readiness milestones
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