Raksha
raksha - protection, the act of guarding
One live console for healthcare compliance, security, and clinical AI oversight.
Raksha is a healthcare governance platform giving hospitals one real-time console for compliance, security, and AI oversight. It tracks HIPAA, SOC 2, ISO 27001, and HITRUST readiness, maps PHI data flows, manages user access and identity risk, detects security threats, monitors clinical AI models for bias and drift, enforces policy, and maintains a live risk register with full audit trails. Compliance failures in healthcare mean fines, breaches, and lost trust, and most teams still track this by hand.
Workflow
Problem
Hospitals track HIPAA, SOC 2, ISO 27001, and HITRUST readiness manually across spreadsheets and email, so posture is a point-in-time claim rather than a monitored state, and clinical AI oversight has no home at all.
Outcome
Continuously monitored compliance, PHI flow mapping, access risk, and clinical model oversight in one console with a live risk register.
01
Map the estate
Systems, data flows, and identities that touch protected health information are inventoried.
02
Monitor the controls
Framework requirements are evaluated continuously against live evidence rather than at audit time.
03
Watch the models
Clinical AI in production is monitored for drift and bias, with policy applied to its use.
04
Hold the register
Risks, owners, and remediation are tracked in a live register with a complete audit trail.
Modules
Framework Readiness
HIPAA, SOC 2, ISO 27001, and HITRUST posture tracked continuously against evidence.
PHI Flow Mapping
Where protected health information moves, and which systems and identities touch it.
Access & Identity Risk
User access reviewed against role, with standing privilege surfaced as risk.
Clinical AI Oversight
Deployed clinical models monitored for drift and bias, with policy enforced on their use.
Representative use cases
Maintaining Continuous HIPAA and HITRUST Readiness Across a Health System
A regional health system with 12 hospitals prepares for HITRUST certification while maintaining HIPAA compliance across a sprawling application estate.
Mapping PHI Flows Across Clinical and Administrative Systems
A hospital network cannot produce an accurate map of where protected health information moves between its EHR, imaging, billing, and analytics systems.
Monitoring Deployed Clinical AI Models for Drift and Bias
A health system has deployed several clinical decision-support models into live care pathways with no ongoing oversight after go-live.
Reducing Standing Privilege Across Clinical System Access
A hospital's access review is an annual manual exercise across thousands of accounts, and access granted for a temporary rotation is rarely revoked.
Holding One Live Risk Register Instead of Departmental Spreadsheets
Clinical risk, IT risk, and privacy risk are tracked in three separate registers owned by three departments, with no shared view for the board.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Four frameworks
monitored continuously, not annually
Live
risk register with full audit trail
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
7 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
3 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
Covered by the platform
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0345
Quality Inspection Reporting Agent
Quality
- AGT-0348
Scrap and Rework Analysis Agent
Quality
- AGT-0349
First-Pass Yield Improvement Agent
Quality
- AGT-0237
CI/CD Build Failure Diagnosis Agent
Software Engineering
- AGT-0250
Code Completion Agent
Software Engineering
- AGT-0251
Code Review Agent
Software Engineering
- AGT-0222
Incident Triage Agent
IT Operations
- AGT-0224
Change Risk Assessment Agent
IT Operations
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
- In-boundary deployment available
- Policy and compliance checks on retrieved context
Capability atoms
- policy-compliance
- document-intelligence
- risk-scoring
- evidence-audit-trail
- alert-routing
- in-boundary-deployment
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- Siemens MES MCP
- SAP S/4HANA
- PI System
- Cognex VisionPro
- ServiceNow ITSM
- Datadog
- PagerDuty
- Kubernetes
Designed for
healthcare, public-sector
Business case
Model a Raksha scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 9% | — | — | — | — |
| Expected | 18% | — | — | — | — |
| Upside | 24% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending4 Agrani candidates in this solution: Incident Triage Agent, Change Risk Assessment Agent, Test Generation Agent, SRE Runbook Generation AgentPairs well with