Ground AI in authorised information
Retrieval, metadata filters, structured context and source references are used where appropriate so generated output remains connected to approved data rather than unsupported assumptions.
Keep people responsible for consequential decisions
AI can prepare, compare, classify, recommend or draft. Material actions—such as sending communications, changing records, filing documents, issuing refunds or modifying commercial data—should use validation and human approval appropriate to the risk.
Protect data and access boundaries
Implementations should minimise personal and confidential data, apply role-based access, scope retrieval to authorised sources, keep credentials server-side and define retention and logging policies.
Evaluate before and after deployment
Relevant evaluation can include retrieval quality, factual grounding, task accuracy, safety, latency, accessibility, cost and business outcomes. High-value workflows should be tested against maintained examples and monitored after release.
Be clear about generated content
Interfaces should distinguish source facts, retrieved evidence, generated suggestions and completed system actions. Users should understand when review is required and where supporting information can be inspected.
Design for correction and oversight
Useful controls can include activity logs, version history, approval records, feedback, rollback, access reviews and escalation paths.
Contact info@feendalus.com to discuss data boundaries, approval design, evaluation and deployment controls.