Risk level
High
EADSS extension
AI Governance & Risk Intelligence for policy-backed decisions.
GovRisk extends EADSS with a dedicated workflow for incident assessment, policy retrieval, risk scoring, and human approval. It is built for cases where explainability and governance controls matter as much as speed.
Policy-aware RAGHistorical recurrence checksHuman approval required
GovRisk output
Policy match
Data Governance §4.2
Human status
Pending approval
Example incidentRepeated data access failure in supplier onboarding
The system finds repeated similar events, links them to control failures, cites policy evidence, and recommends escalation for governance review.
What it adds
A separate governance lane inside EADSS.
GovRisk is designed as its own operational surface. It does not replace the core emotion and review workflows. It extends them with policy intelligence, structured risk context, and governance-grade audit controls.
Incident-first workflowStart from governance events, not support sentiment, and keep the metadata that risk teams need.
Evidence-backed outputEvery risk recommendation is paired with policy excerpts and a clear narrative reason.
Approval and audit trailReviewer actions are stored so the final decision is traceable, editable, and accountable.
Best-fit use cases
Where GovRisk helps most
Data governance incidents
Assess access-control failures, repeated exceptions, and policy breaches with cited evidence.
Supplier and onboarding risk
Track recurring control issues in supply-chain and vendor workflows before they widen.
Internal compliance review
Support governance teams with consistent scoring, recommended actions, and human sign-off.
How it works
From incident text to reviewer-approved action
Step 1
Ingest the incident
Capture the report text alongside sector, department, severity, date, and location so risk decisions start with structured operational context.
Step 2
Retrieve policy evidence
Run semantic retrieval across uploaded policy PDFs to surface the sections most relevant to the current governance issue.
Step 3
Score and explain risk
Blend policy evidence, metadata weighting, and historical recurrence into a risk level, reason, policy match, and recommended action.
Step 4
Keep humans in the loop
Require reviewer approval, edits, or rejection before the recommendation becomes a trusted operational decision.
Input example
What teams provide
Incident
Repeated data access failure in supplier onboarding.
Metadata
Sector: Supply Chain • Severity: Medium • Region: London
Policy set
Uploaded governance and data-control PDFs from the existing EADSS document layer.
Output example
Risk level
High
Reason
Similar incidents have repeated three times and align with policy-controlled data access controls.
Evidence
Data Governance Policy, section near page 4.2.
Recommended action
Escalate for governance review.
Human approval
Pending reviewer approval or edit.
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