Knowledge guide · Responsible AI

Practical controls for delivering AI with accountability

Responsible AI becomes real through product, technical, and operational decisions made throughout discovery, design, testing, launch, and improvement.

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Purpose & people

Define benefit, affected users, and accountability

• State the intended outcome and who should benefit.
• Identify people affected by errors or exclusions.
• Name product, information, security, and operational owners.
• Document tasks the AI must not perform.
• Provide a route for questions, correction, and escalation.

Information & access

Protect data and respect permissions

• Use information with a clear purpose and appropriate authority.
• Minimise sensitive data sent to models or external providers.
• Preserve source-level permissions in retrieval and output.
• Protect credentials and tool permissions.
• Define retention, deletion, and audit requirements.

Quality & oversight

Evaluate the behaviours that matter

• Build representative evaluation examples and failure cases.
• Measure grounding, relevance, refusal, safety, latency, and cost.
• Require human approval for consequential actions.
• Make sources, uncertainty, and system limitations understandable.
• Test misuse, prompt injection, and permission boundaries.

Operations

Monitor, respond, and improve

• Log important actions without exposing unnecessary sensitive data.
• Monitor quality, incidents, usage, performance, and spend.
• Provide rollback, disablement, and manual fallback paths.
• Review model, prompt, content, and integration changes.
• Reassess risk as users and capabilities change.

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