Cost of Assurance Dashboard

Vendor risk, AI governance, evidence validation, and remediation routing

Demo principle: operational metrics first, dollar estimates second, assumptions visible.

Manual Process Cost

Estimated monthly cost from touches, rework, and questionnaires

Automation Run Cost

Runtime and maintenance effort for the workflow

Net Avoided Effort

Manual cost reduced, less automation operating cost

Evidence Reliability

Evidence valid on first submission

Avg Review Cycle

0 days

Submitted to closed or routed for remediation

Residual Risk

Reviews with open high-risk findings

Assurance Workflow

Vendor / AI Tool Intake

Capture vendor, data type, AI usage, integrations, permissions, and requester.

Risk Classification

Classify by data sensitivity, access level, AI behavior, and business criticality.

Control Mapping

Map findings to SOC 2, ISO 42001 concepts, privacy, security, and common controls.

Evidence Validation

Check whether evidence is complete, current, source-backed, and consistent.

Risk Register Entry

Turn failures into owned risks, exceptions, due dates, and compensating actions.

ServiceNow Routing

Generate remediation payloads and track closure instead of leaving findings in a spreadsheet.

Cost Model Assumptions

Loaded hourly rate

Minutes per manual touch

Minutes per evidence rework

These are demo assumptions, not audited savings claims. In a real environment, the inputs would come from intake records, GRC tool activity, ServiceNow timestamps, questionnaire logs, and evidence validation results.

How the Cost Is Calculated

Manual Labor Cost

manual touches x avg minutes x loaded hourly rate

Evidence Rework Cost

failed evidence x rework minutes x loaded hourly rate

Net Avoided Effort

manual cost avoided - runtime - maintenance

Vendor Review Queue

Vendor	AI / Data Use	Risk	Evidence	Controls	Owner	Remediation

Cost Drivers

AI Vendor Test Cases

Clean Pass

Approved

Vendor uses internal metadata only, evidence is current, DPA matches questionnaire answer, and integrations are least privilege.

Evidence Conflict

Exception

Questionnaire says no training on customer data, but the data processing addendum and integration permissions need follow-up.

Operational Drag

Monitor

Risk is manageable, but review is stuck because evidence sits across email, screenshots, Excel, and ticket comments.

Portfolio positioning: This is not a production GRC platform. It is a proof-of-concept showing how the workflow can be made measurable, how evidence quality can be validated, and how GRC friction can be translated into cost, velocity, and residual risk.

Sample Event Log

Review ID	Event	Actor	System	Status	Signal

Executive Readout

What changed?

Fewer manual touches, faster reviews, cleaner evidence, clearer ownership, and less status reconciliation.

What still matters?

High-risk AI vendors, duplicate evidence locations, stale documentation, and unresolved remediation.

Why fund it?

It reduces the total cost of assurance while improving velocity, evidence reliability, and risk visibility.