Responsible AI
Security and Responsible AI
AI implementation should protect business information, keep important decisions reviewable, and make error handling clear before launch.
Data minimization
Use only the information required for the workflow. Avoid sending sensitive or unnecessary data into tools, prompts, integrations, or test environments.
Least-privilege access
Access should be limited to the systems, records, and users required for the workflow. Staff permissions and vendor access should be reviewed before launch.
Testing and production separation
Use sample or approved test data where possible, define test cases, and confirm behaviour before production data or customer-facing workflows are involved.
Vendor and model assessment
Review the vendors, models, integration methods, data handling, retention options, and operational limitations before choosing an implementation path.
Human approval and exception handling
Consequential actions should include human approval, escalation for low-confidence outputs, and clear handling for errors, unusual inputs, and untrusted documents.
Monitoring and handover
Projects should include logs where supported, operating documentation, staff training, issue escalation, and a plan for retention, maintenance, and improvement.
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