Data boundaries
Define what data the workflow may access, where it is stored, and what must never be sent to external systems.
Security & AI governance
AI workflows should be useful, controlled, and reviewable.
For compliance-sensitive B2B teams, the problem is rarely “can AI do this?” The real question is whether the workflow has the right data boundaries, review points, audit trail, and ownership.
Governance model
Controls we define before a workflow ships.
These are practical delivery controls, not policy theatre. The exact shape depends on your data, tools, risk level, and people who own the process.
Human review
Route uncertain, sensitive, or externally visible outputs to accountable people before action is taken.
Audit trail
Log decisions, extracted fields, confidence, prompts, owners, and review notes where the process needs traceability.
Operating rules
Document fallback paths, approval thresholds, escalation rules, and what happens when the system is wrong.
- AI agent connected broadly to private systems
- No clear owner for bad outputs or edge cases
- Outputs accepted without confidence checks
- No record of what changed and why
- Limited tool access and documented permissions
- Human review for consequence-heavy decisions
- Test cases based on real exceptions
- Logs and dashboards for operational review
Need a safer AI workflow?
Start with the process and the risk, not the model.
Share the workflow you want to improve and we will identify the safest useful starting point.
Start a governed AI enquiry