The situation: chaos
- Multiple AI tools simultaneously (ChatGPT, Claude, Gemini, Copilot).
- Unknown what data goes in and what output comes out.
- IT has limited control, management lacks visibility.
The framework: 5 phases
This framework works because it builds control step by step: first visibility, then policy, then implementation, and only then strict control and optimisation.
Phase 1 — Visibility (week 1–2)
- Inventory tools and use cases (surveys + interviews + monitoring).
- Identify risky data flows and processes.
- Document: teams, tooling, purpose, data, risk.
Phase 2 — Policy (week 3–4)
- Which tools are permitted (and why)?
- Which data is and isn't allowed in prompts?
- Escalation + incident protocol (what to do when a mistake happens).
- Communication: short, clear, repeatable.
Phase 3 — Implementation (week 5–8)
- Roll out safe tooling (enterprise settings).
- Train employees on safe use and role-specific risks.
- Deploy monitoring (DLP, logging, alerts) and organise support.
Phase 4 — Control (week 9–12)
- Monitor compliance: tooling, data breach indicators, exceptions.
- Respond to incidents: triage, mitigation, additional training.
- Report to management: trends, risks, actions.
TipImportant: control only works when people also have a good alternative. Otherwise the problem shifts out of your sight.
Phase 5 — Optimisation (week 13+)
- Gather feedback and update policy.
- Update training for new tools and regulations.
- Continuous monitoring and monthly/quarterly reporting.
Conclusion: control is a system
AI governance is not a one-time project. It's an ongoing system that learns, measures, and adjusts.
Qrio helps with training, assessment, and reporting so you demonstrably build control over AI usage.