AI Safety, Governance & Data Privacy
Large language models (LLMs) and AI assistants can be powerful tools — but they also introduce new risks around data privacy, accuracy, cost control and unpredictable behaviour. I design AI-enabled features with safety, governance and auditability built in from the start.
This is especially important for systems handling confidential information, regulated processes, internal policies or business-critical decision-making.
Data privacy
Clear control over what data is shared with AI services.
Grounded answers
AI responses based on your own approved content.
Audit & traceability
Logs and evidence for AI-assisted actions and outputs.
Cost control
Usage limits and monitoring to avoid runaway spend.
Principles for safe AI integration
AI features work best when they’re treated like any other critical system component — with clear boundaries, accountability and visibility.
Data protection by design
- Keep sensitive or regulated data out of external AI providers where required
- Redaction, minimisation or summarisation before data reaches an LLM
- Clear rules on what data can and cannot be used for prompts
- Alignment with GDPR and internal data protection policies
Predictable behaviour
- Constrained prompts and system instructions
- Clear scope: what the AI is allowed to answer — and what it must refuse
- Fallback behaviour when confidence is low or data is missing
- Human-in-the-loop where decisions matter
Grounded AI using retrieval-augmented generation (RAG)
For most business use cases, free-form “internet-trained” answers aren’t appropriate. RAG allows AI responses to be grounded in your own approved content.
Your data, your rules
- Answers sourced from your documents, policies, guides or legislation
- No hallucinating beyond the material you’ve approved
- Ability to cite or link back to source content
- Controlled updates when content changes
Scoped use cases
- Internal knowledge assistants
- Policy, handbook or legislation Q&A
- Guided workflows (e.g. “what do I do next?”)
- Staff support tools — not uncontrolled chatbots
Governance, audit & accountability
AI-assisted systems should be explainable and reviewable — especially when they influence decisions, advice or communications.
- Record prompts, sources used and outputs where appropriate
- Trace AI-assisted decisions back to users and context
- Support incident investigation and compliance reviews
- Balance auditability with privacy and data minimisation
- Clear disclaimers and confidence thresholds
- Prevent AI from presenting guesses as facts
- Escalation paths when the AI can’t answer safely
- Regular review of outputs to catch drift or misuse
- Per-user or per-feature usage limits
- Monitoring of token usage and spend trends
- Alerts for unexpected spikes
- Ability to disable or throttle features quickly
AI in Android & field apps
When AI features are exposed through mobile apps, additional care is needed around data exposure, offline behaviour and user expectations.
Safe mobile integration
- Minimal data sent from devices to AI services
- Server-side mediation rather than direct device → LLM calls
- Consistent behaviour across app versions
User clarity
- Clear messaging about what the AI can and can’t do
- Avoiding over-trust in AI outputs
- Escalation to human support where appropriate
Exploring AI in a controlled way
If you’re curious about AI but cautious about risk, I recommend a clearly scoped pilot: a limited audience, a narrow use case, measurable outcomes and a clear “stop/go” decision. If it proves valuable, it can be expanded safely from there.
Talk about AI in your organisation
Whether you’re considering an internal knowledge bot, an AI-assisted workflow, or a carefully governed experiment, I can help you design something that’s useful, safe and defensible.