1. Inventory
Map trigger, AI step, tools, branches, human approvals, data classes, and final system of record.
Method
Governance becomes useful when it produces evidence.
The scanner turns abstract AI governance into workflow-specific findings an operator can fix: data exposure, autonomy, approvals, audit trail, provider posture, and operational resilience.
2. Score
Rate workflow risk by autonomy, data sensitivity, provider posture, downstream action, auditability, and exception handling.
3. Remediate
Prioritize practical fixes: scoped keys, approval thresholds, validation, redaction, logging, rollback, and change review.
Evidence pack
What the customer can hand to a client or owner.
The output is not a generic checklist. It is a record of what was reviewed, what is risky, what should change, and what proof exists.
Risk report
- Workflow map and risk score
- Critical, high, medium, and low findings
- Plain-English business impact
Remediation checklist
- Owner and due-date fields
- Recommended control changes
- Retest criteria for each fix
Evidence index
- Reviewed artifacts and dates
- Provider settings and audit proof
- Known gaps and scan assumptions
Boundaries
This is an operational risk scan, not a compliance certification.
The service helps teams find and fix risky AI workflow patterns. It does not replace legal advice, security testing, regulated compliance assessment, or vendor due diligence.
What it does well
- Catch unsafe automation patterns before production damage
- Turn messy workflow facts into a useful control record
- Identify whether recurring monitoring is worth building
What it avoids in v1
- Direct access to customer systems by default
- Broad enterprise AI governance claims
- Too many integrations before paid demand is proven