Responsible Use of AI Assistants in the Public and Private Sectors

⚡ Quick Summary

This practical guidance document sets out how organisations—especially those in high-impact sectors—can safely and responsibly deploy AI assistants like GPT-style chatbots. The authors focus on frontline use by non-technical staff, proposing guardrails, usage categories, and governance roles to balance flexibility with institutional risk controls. It’s one of the first playbooks tuned specifically to human–AI interaction in day-to-day workflows.

🧩 What’s Covered

1. What Are AI Assistants?

Defined as general-purpose, interactive tools capable of producing text, code, images, or structured outputs
Use cases include summarisation, data formatting, code generation, writing, and process planning
Distinct from automated systems with minimal human control—AI assistants are human-led, context-sensitive, and task-flexible

2. Core Recommendation Areas

The document is structured around six key action areas:

A. Access Control

Classify access by team, seniority, risk profile
Recommend tiered permissions (e.g., read-only, editing, code execution)
Control plugin access and third-party integrations

B. Appropriate Use Guidelines

Categorise AI assistant use into:
- High-risk (e.g. decision-making, policy drafting)
- Medium-risk (e.g. summarisation, communications)
- Low-risk (e.g. grammar, formatting)
Offer example prompts and prohibited scenarios (e.g. data synthesis on regulated topics)

C. Disclosure & Labelling

Users should be required to disclose AI-generated outputs in policy, public, and legal documents
Emphasise transparency for downstream users, reviewers, and the public

D. Training & Enablement

Suggests role-specific training modules for legal, comms, procurement, and compliance teams
Promote prompt hygiene, accuracy checks, and escalation paths
Introduce sandbox environments for experimentation

E. Logging & Monitoring

Maintain usage logs for auditing, incident tracking, and learning
Monitor for hallucinations, misuse, and high-volume or anomalous queries
Build in override and revocation mechanisms

F. Governance Integration

Embed oversight into existing structures: risk committees, digital governance boards
Assign AI assistant owners per team
Create feedback loops from logs, audits, and incidents into policy updates

3. Special Considerations

Notes unique challenges in public sector contexts, such as FOIA, transparency laws, and public trust
Discusses third-party procurement, model switching, and supply chain assurance
Flags risks in internal-facing models with fine-tuning or RAG workflows—requiring additional safeguards

💡 Why it matters?

This guide meets a pressing need: helping organisations translate high-level AI principles into specific do’s and don’ts for frontline users. With generative AI being rapidly adopted by comms teams, policy analysts, and operations staff, this document offers a ready-to-use scaffold for responsible deployment. It’s opinionated, clear, and adaptable across sectors.

❓ What’s Missing

No technical template or access control matrix to guide implementation
Does not cover LLM-specific adversarial risks, jailbreaks, or model updates
Relatively light on legal treatment (e.g. GDPR, IP law, data minimisation)
Leaves questions open around redress, user rights, and AI-caused errors
Governance architecture is described but not deeply modeled—no role maps or escalation diagrams

👥 Best For

Digital transformation and IT leaders rolling out AI assistant tools
Public sector agencies looking to align AI use with democratic accountability
Enterprise risk managers building acceptable use policies for genAI
Compliance and legal teams drafting guardrails for internal chatbots
Procurement officers assessing AI vendors or plugin capabilities