Cabonne Council GenAI Policy: human review and privacy safeguards

Cabonne Council’s draft guidelines for generative AI mark a cautious, pragmatic step: allow staff to use tools such as ChatGPT, Microsoft Copilot and Google Gemini for productivity gains — but only with human sign-off, disclosure and strict privacy controls in place.

Background​

Local governments worldwide are rapidly confronting a simple operational question: how can councils harness generative artificial intelligence (GenAI) to reduce routine work while preserving public trust, privacy and legal defensibility? Cabonne Council’s publicly-notified draft policy — put on exhibition for community consultation — is the latest example of a small council attempting to answer that question with a risk-managed approach. The draft emphasises three core points: GenAI can speed document production and data insights; its outputs are fallible and must be verified; and personally identifiable information must be protected. Attempts to fetch the draft directly from Cabonne’s public-notices page encountered access restrictions during verification, so the council’s published notice and local reporting form the basis of the summary below.
This article places Cabonne’s proposal in context. It summarises the key elements of the proposed local policy, explains where practical gains usually appear, outlines the real-world technical and contractual boundaries councils must understand, and provides an actionable governance checklist for councils that want responsible AI adoption while safeguarding democratic processes and resident data.

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Why Cabonne’s direction matters​

Councils are information-heavy organisations. Planning consultations, meeting minutes, customer service enquiries and regulatory casework generate high volumes of repetitive text and data that are costly to process manually. Generative AI promises time savings in:

• drafting routine letters and reports,
• summarising long public submissions or meeting transcripts,
• extracting themes from thousands of consultation responses,
• producing initial research or briefing notes.

These productivity claims are realistic in small administrations where staff capacity is constrained. Other local governments that have piloted enterprise-grade assistants report measurable time savings when governance is applied responsibly. Examples include targeted pilots that kept humans in the loop and ring‑fenced licences within the organisation’s IT tenant.
But the operational value comes with direct risks: hallucination (confident but incorrect outputs), privacy leakage (sending PII to models that may retain or log data), and procedural opacity (auditors and citizens can’t see what the AI actually did unless there’s an audit trail).

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What Cabonne’s draft seeks to achieve​

From the council report summarised in local media, the draft policy aims to strike a compromise: allow productive use where it is low-risk, while locking down high-risk workflows. Key elements reported include:

• Mandatory disclosure — staff must record and disclose when GenAI has been used to prepare a document or analysis.
• Human verification — any AI-generated output must be checked and approved by a staff member before it is used in decision-making or released to the public.
• Data currency and accuracy checks — outputs must be verified against up-to-date internal data sources.
• PII protections — personally identifiable information must not be supplied to consumer GenAI services; data classification rules determine what can be used.
• Governance by a cross-functional working group — the policy was drafted by senior officers across innovation, governance, strategy and corporate performance.

These controls mirror practical governance approaches other councils have adopted when piloting AI: keep the model in an enterprise tenant where contractual terms are explicit, require human-in-the-loop review, and publish or make available an audit trail for transparency.
Caveat: the council’s draft text on the public site could not be fully retrieved by automated verification at the time of reporting, so direct inspection of the cabinet or committee papers on Cabonne’s website is recommended for precise wording.

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Vendor realities: what councils must verify before sending data into a model​

One of the most consequential technical and contractual decisions is which vendor product the council uses and under what contractual terms. Public sector IT teams need to verify vendor commitments in three areas:

• Training and reuse commitments. Many enterprise offerings promise that organisation data will not be used to train public models by default — but the exact contractual language matters. OpenAI’s enterprise pages state that business data from ChatGPT Enterprise and Team is not used to train models by default. Anthropic and Microsoft make similar distinctions between consumer and enterprise products; enterprise plans commonly provide non‑training assurances for customer content. These vendor claims are public but must be confirmed in signed procurement documents.
• Data residency, retention and deletion rights. Councils should require contractual guarantees about where prompt logs, attachments and metadata are stored, how long they are retained, and the process for deletion or export for audit. The vendor’s help pages are informative, but contractual addenda or Data Processing Agreements (DPAs) are the enforceable instrument.
• Telemetry and monitoring. Vendors may collect telemetry for product improvement or abuse detection. Confirm whether telemetry is anonymised, whether administrators can disable telemetry, and whether the vendor will provide logs on request for FOI, audit or incident response. If a vendor reserves the right to use telemetry for model improvement unless a customer opts out, that is a red flag for council use on resident data.

Practical validation: do not accept vendor marketing or FAQ pages as a substitute for explicit contractual clauses covering non‑training, deletion and audit rights. Councils must make these procurement terms explicit in the contract.

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Lessons from other councils: what has actually worked​

Practical case studies from small councils illustrate a common pattern: focus on narrowly scoped, high-volume, low-risk tasks and pair them with clear governance.

• A district council that ring‑fenced Copilot licences inside its Microsoft 365 tenant used the assistant for meeting recaps and plain‑language conversion; human editors always reviewed and published the final text. That approach retained the efficiency gains while maintaining human ownership.
• In another example, Copilot was used to thematically analyse Long Term Plan submissions, with staff sampling AI outputs to ensure minority technical submissions were not down‑weighted. The council preserved the audit trail and published short assurance statements explaining the AI’s role. This both sped the process and kept the decision-making defensible under challenge.
• The Bath planning case demonstrates the political sensitivity: when thousands of public comments were summarised with AI for a contentious stadium application, journalists and campaigners demanded the audit trail and sample checks so councillors’ deliberations could be verified. That episode shows how an otherwise operational AI use becomes a democratic issue when stakes are high.

These municipal experiences converge on a few principles: start small, document everything, require named human sign-off, and publish a plain‑English note when AI is used in public-facing processes.

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Legal and ethical guardrails — Australian government context​

Cabonne sits within an Australian policy landscape that has moved quickly to require ethical AI assurance in government. The Commonwealth’s Policy for Responsible Use of AI in Government and the National Framework for AI assurance set expectations for:

• human-centred design,
• explicit accountability and contestability,
• privacy protection,
• transparency and auditability.

At the NSW state level, agencies are expected to apply the NSW Artificial Intelligence Assessment Framework and the Digital Assurance Framework for higher-risk projects, and new state capability bodies are being established to coordinate safe adoption. These frameworks make human oversight, DPIAs (Data Protection Impact Assessments) and documented assurance mechanisms a material requirement when agencies use AI. Councils and local government entities should align local policies with these national/state frameworks.

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A practical governance checklist for councils​

Cabonne’s draft touches the obvious controls; the checklist below converts those controls into concrete, auditable actions councils should adopt before scaling GenAI usage.

• Define permitted use-cases by risk tier:
• Low risk: drafting non‑decision-facing communications, internal note-taking, templating.
• Medium risk: drafting officer reports that summarise factual material (requires sampling and enhanced logging).
• High risk: any analysis that informs regulatory decisions, enforcement, or where PII is involved (disallow or require full DPA and explicit ministerial/CEO sign-off).
• Require an AI Governance Group (cross-functional: IT/security, legal, records, communications, service leads).
• Procurement must include:
• Explicit non‑training clause for council data unless explicitly agreed,
• Audit and deletion rights,
• Data residency and export guarantees,
• Defined retention windows for logs and prompts.
• Technical controls:
• Enforce sensitivity labels and DLP rules in Microsoft 365 (or equivalent) that prevent high-risk content from being sent to web-grounded prompts.
• Configure tenant settings to disable any automatic telemetry that would expose prompts to vendors for training.
• Central logging of prompts and responses with tamper-evident storage linked to records retention policy.
• Human-in-the-loop workflows:
• AI drafts a summary or artefact.
• A named officer reviews, annotates and signs off in the document version history.
• The final, human‑approved text is the only version published or used for decision-making.
• Transparency and auditability:
• For any AI-derived material that informs decisions publish an assurance statement: what the AI did, who reviewed it, and how checks were performed.
• Maintain an auditable appendix (prompts, raw AI output and final human edits) available on request or appended to committee papers for high-stakes items.
• Training and competence:
• Mandatory staff training on GenAI risks, vendor differences, prompt hygiene and PII handling.
• Role-based access and licence issuance (don’t give Copilot to every user by default).
• Contingency planning:
• Incident response scenarios where a hallucination or privacy breach impacts a resident or a decision, including communication templates and legal notification steps.

Many councils that have reported positive outcomes followed this incremental checklist, pairing enterprise-grade tools with strict administrative and technical controls.

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Operational pitfalls and how to avoid them​

• Hallucination that enters the public record. Mitigation: never publish AI text without a human reviewer who certifies factual accuracy. Log the verification step as part of records management.
• “Frequency bias” in theming public comments. Mitigation: ensure AI‑assisted theming is supplemented with flagging rules that prioritise submissions from statutory consultees and technical experts, and present representative verbatim comments in committee papers.
• Assuming vendor FAQs equal contractual guarantees. Mitigation: demand enforceable contract clauses and DPIAs; treat vendor marketing statements as background, not law.
• Uncontrolled consumer tool use. Mitigation: issue only enterprise licences and block or limit the use of consumer chatbots on council-managed devices and networks; include this in acceptable-use policies.
• Ignoring records, FOI and legal discoverability. Mitigation: capture prompts and responses in secure records systems when AI contributed to decision-critical outputs; be prepared to disclose under FOI or legal processes.

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Implementing Cabonne’s policy — a pragmatic rollout plan​

• Immediate (0–90 days)
• Publish a short plain‑English statement for residents explaining that a draft GenAI policy is on exhibition and what it covers.
• Issue a temporary moratorium on using consumer-grade chatbots for council business until tenant-level controls and DLP rules are in place.
• Run a DPIA and map the top five use-cases by risk (communications, records summaries, customer service templating, planning submission theming, back-office drafting).
• Medium term (90–180 days)
• Pilot enterprise Copilot/assistant for one low-risk service area with strict logging and human sign-off. Track time-saved and error rates.
• Negotiate contractual clauses with the chosen vendor that explicitly prohibit training on council data and provide deletion/audit rights.
• Train a group of “AI champions” and publish an internal playbook for prompt-hygiene and PII handling.
• Longer term (6–12 months)
• Scale to additional service areas where pilot metrics show clear benefits and risks remain manageable.
• Publish an annual report on AI usage, including incidents, time-saved metrics and DPIA outcomes.
• Maintain alignment with state/national AI assurance frameworks and adjust governance as legal or vendor conditions change.

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Final assessment: strengths and risks of Cabonne’s approach​

Strengths

• Pragmatic balance — Cabonne’s draft acknowledges operational benefits while insisting on human control; this is the right tenor for small councils where staff time is scarce.
• Cross-functional drafting — involving governance, IT, strategy and executive offices increases the chance the policy will be operationally enforceable.
• Explicit staff obligations — disclosure, verification and up-to-date data checks are practical measures that reduce the risk of AI-driven errors entering public records.

Risks and open questions

• Contract enforcement — vendor assurances in marketing materials are not a substitute for contract language; the policy must mandate procurement clauses.
• Audit trail publication — the policy should clarify when and how AI audit trails become public or are made available for FOI and appeals; silence here creates political risk, especially for planning decisions.
• Tech drift — vendor policies and default data practices can change. A council that signs a one‑year licence must still monitor vendor privacy changes and be ready to adapt or exit contracts. Don’t assume vendor FAQs are immutable.
• Staff capability — the best policy fails without practical training and role-based controls. Allocate resources for training and monitoring from day one.

Where claims could not be independently verified

• The full text of Cabonne’s draft policy was not retrievable via automated verification at the time of research; local reports summarise the policy’s intent. Readers and councillors should examine the council’s public notice and committee papers to confirm specific wording and obligations.

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Conclusion​

Cabonne Council’s draft GenAI policy reflects an emerging municipal consensus: generative AI can amplify staff productivity when used in narrowly defined, governed ways — but it must never substitute for human judgement, especially where residents’ rights, privacy or planning outcomes are on the line. The right path for councils is not blanket prohibition nor ungoverned adoption, but a phased, auditable rollout that binds vendor commitments into enforceable contracts, keeps human sign‑off mandatory and publishes plain‑English assurance statements whenever AI contributes to public-facing decisions.
If Cabonne publishes the draft policy text and associated DPIA in an accessible form, it can become a useful template for similarly sized councils wanting to harness GenAI without risking the procedural and legal harms that arise when automation goes unchecked. The practical checklist in this article converts the draft’s principles into concrete steps: classify risk, require contract guarantees, log and human‑verify outputs, train staff, and keep transparency central to any AI-enabled workflow. With those elements in place, AI can help scale human judgement — not replace it.

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Source: Young Witness Should AI have a place at council? Cabonne weighs in - Young Witness