DCCEEW Pilots Microsoft 365 Copilot: AI in Government with Careful Governance

The Department of Climate Change, Energy, the Environment and Water (DCCEEW) has quietly kicked off a small-scale pilot of Microsoft 365 Copilot for roughly 100 users — a deliberately cautious step that follows the department’s completion of a multi-year transition away from shared IT services and signals an accelerated push to build an “AI‑enabled” workplace while trying to contain security, governance and operational risks.

Background​

DCCEEW was created during machinery-of-government changes and inherited climate, energy and environment functions from other portfolios; over the last two years the department has been standing up its own ICT estate, moving services out of shared arrangements with the Department of Agriculture and others and going live on a standalone HR/payroll platform. The IT transition and the resulting control of core infrastructure underpin the department’s ability to trial desktop‑integrated generative AI tools now that those infrastructure and identity seams have mostly been closed.
The current Copilot trial is deliberately modest in scope — around a hundred users accessing Copilot features across Outlook, Word, Teams, Excel and PowerPoint — and is explicitly described by the department as a short experiment to “inform our long‑term approach to AI adoption.” The department is also seeking external support to deliver Copilot training, executive capability uplift and to shape longer‑term operating models for AI support, including knowledge articles and ServiceNow integration for IT support staff.

---

Why this pilot matters now​

From shared services to departmental control​

For a government agency, the difference between being a tenant in a large shared services environment and owning your own ICT stack is material. DCCEEW’s move off shared services started in 2023 and the department has progressively stood up independent capabilities — a step that reduces overheads involved in multi‑agency coordination and gives DCCEEW the autonomy to enable new productivity tooling, change identity and access policies, and enforce data governance that aligns with departmental priorities. These operational changes are the proximate enablers for any safe, auditable Copilot rollout.

The Australian public‑sector context​

This pilot comes after a six‑month, multi‑agency Copilot trial coordinated by the Digital Transformation Agency (DTA) in which thousands of public servants from across the Australian Public Service participated. That DTA program was designed to explore practical productivity use cases and governance models for generative AI; DCCEEW’s participation in the DTA trial was minimal — effectively a handful of staff — but the broader APS trial produced templates and lessons that agencies can adapt. DCCEEW’s new pilot intentionally builds on those learnings while focusing on departmental uplift and operational readiness.

---

What DCCEEW is testing — scope and aims​

• Small cohort (≈100 users) across Outlook, Word, Teams, Excel, PowerPoint to validate real day‑to‑day benefits.
• Short, structured pilot designed to inform policy, governance and procurement for wider AI adoption.
• External capability uplift: masterclasses for senior executives, high‑touch VIP support, and ServiceNow knowledge‑base build for IT support.
• Emphasis on measurable targets: productivity gains, time saved on routine tasks, changes to business processes, and the operational impact of Copilot on support functions.

Why Microsoft 365 Copilot?​

Copilot integrates directly into the Microsoft 365 productivity fabric — it can summarise emails, draft and refine documents, extract insights from spreadsheets, produce slide decks from notes, and generate meeting action items from Teams conversations. For knowledge‑worker heavy departments this offers obvious time‑saving possibilities; for an environment like DCCEEW that mixes policy, scientific analysis and regulatory responsibilities, the potential is to reduce administrative burden and speed iterative drafting cycles. However, the same deep integration also concentrates risk if data handling, model routing and retention policies are not tightly controlled.

---

Strengths and immediate benefits​

1. Productivity gains for knowledge work​

Copilot is optimised for routine, time‑consuming tasks:

• Drafting and editing correspondence and briefings;
• Summarising long technical reports and Teams threads;
• Translating complex datasets into executive‑friendly summaries using Excel’s natural‑language analysis;
• Speeding the creation of presentation materials from raw notes.

When used in well‑scoped scenarios, the tool can free time for higher‑value analytical and decision tasks that require human judgement.

2. Better cross‑team consistency and knowledge sharing​

By providing a single AI layer within Microsoft apps, Copilot can help standardise templates, generate consistent messaging across policy teams, and assist in codifying institutional knowledge — particularly valuable in a department that spans science, regulatory and policy functions.

3. Low friction onboarding​

Because Copilot sits inside tools staff already use, adoption friction is lower than with separate point solutions. This makes it practical to pilot with a visible cohort and iterate fast on governance and training.

4. Pathway to broader AI enablement​

DCCEEW already uses AI and ML in environmental monitoring and species identification; the Copilot pilot provides an opportunity to combine productivity gains with operational AI capability, linking desktop copilots to governed data platforms incrementally. This can help accelerate other programs, like analytics and decision‑support systems.

---

Risks, technical pitfalls and governance challenges​

These are not theoretical: they have been repeatedly surfaced by APS trial reports and independent audits. Any successful pilot must treat them as immediate engineering and policy priorities.

Data handling and sovereignty​

Copilot is deeply connected to Microsoft Graph and tenant data flows. This increases the risk of:

• Data exfiltration via prompts containing sensitive content;
• Cross‑cloud model routing (where Copilot may call different model backends, potentially outside a single cloud boundary), complicating data residency and legal compliance;
• Unexpected egress costs and audit gaps.

The DTA trial emphasised that agencies must understand model routing, logging and retention, and implement strong DLP (Data Loss Prevention) rules before scaling.

Hallucinations and auditability​

Generative models can produce plausible but incorrect outputs. For policy and regulatory work, hallucinations create real risks — from drafting inaccurate briefings to mischaracterising scientific evidence. Agencies must require human verification, maintain provenance metadata, and ensure outputs are traceable for audit and FOI contexts.

Vendor and model lock‑in​

The convenience of Copilot’s Microsoft ecosystem can lead to dependence on a single vendor stack — including Azure, Microsoft’s model routing policies and third‑party model providers. Agencies should evaluate portability, contractual model provenance guarantees, and the ability to shift to alternate models if needed.

Operational cost and usage spikes​

Copilot billing models and per‑action costs can produce sudden cost spikes if model usage is not throttled or monitored. Pilot programs must instrument telemetry and set quotas or approval gates. This was a recurring lesson in large pilots: meter everything.

Security and attack surface expansion​

Agentic features and integrations (for example, connectors to SharePoint, SAP or external services) expand the attack surface. Adversaries could craft prompts or supply data that abuses connectors or automations; robust runtime monitoring, red‑teaming and connector allowlists are essential.

---

How DCCEEW should sequence Copilot adoption (recommended roadmap)​

• Baseline: run a focused pilot (as DCCEEW is doing) limited to low‑sensitivity administrative tasks. Instrument for telemetry and user feedback.
• Governance: publish Copilot rules of use, DLP policies, prompt hygiene guidance and a decision matrix for allowable data types.
• Technical controls:
• Restrict model‑backend choices where possible;
• Enable tenant‑wide logging of Copilot calls, model backend, and token metadata;
• Integrate logs with SIEM and retention policies.
• Training & capability uplift: equip executives and ICT support teams with hands‑on masterclasses and ServiceNow knowledge articles so IT can operationalise first‑line support and incident response.
• Scale cautiously: broaden users to higher‑value tasks only after technical and policy controls prove effective under load.
• Continuous oversight: maintain an independent audit cadence, red‑team reviews and a mechanism for rapid rollback.

---

Public sector-specific considerations​

• Records and FOI: outputs used in decision‑making must be treated as official records. Departments should update retention schedules and make processes for capturing Copilot‑assisted drafts explicit.
• Risk profiling: not all Copilot tasks are equal — classify tasks by sensitivity and restrict capabilities for high‑risk classes (e.g., legal, intelligence, or some science outputs).
• Inter‑departmental data flows: where DCCEEW keeps older business applications hosted in other departments, special care is needed to prevent accidental cross‑tenant data leakage.
• Procurement and contracts: include model‑provenance clauses, indemnities for data misuse where possible, and rights to audit vendor compliance.

---

What the DTA trial and independent reviews tell us (lessons DCCEEW should adopt)​

• Early pilots in the APS found measurable time savings for routine tasks but warned that benefits are uneven and contingent on governance frameworks and user training. The DTA emphasised transparency, risk management and clear governance as prerequisites for scaling Copilot across government.
• Participation data from the DTA evaluation shows the breadth of agency engagement and confirms smaller agencies or new departments tended to join with limited cohorts; DCCEEW’s smaller participation in the DTA trial is consistent with the approach being taken now: experiment first, then scale with controls.

---

Cross‑checks and verification of key claims​

• The department’s procurement activity requesting Copilot support, training and ServiceNow knowledge‑base work is publicly visible on tender portals, confirming an active plan to run a short trial and fund capability uplift through external providers. This aligns with the DCCEEW statement about seeking external support for Copilot rollouts.
• Independent oversight and audit materials (AN AO reviews and earlier reporting) document the department’s move away from shared services and its focus on stabilising networks, cyber security and information management — all necessary preconditions for any AI productivity deployment. These sources corroborate the sequence described in the department’s internal communications about standing up its own IT operations.

Important caution: a frequently‑reported headcount figure (the article referenced 5,790 staff) appears in media reports but is not straightforward to verify from a single, current government personnel dataset; official corporate plans and audit documents have cited different staff totals in prior years. Treat specific headcount numbers as directional unless confirmed in a current departmental staffing publication.

---

Practical controls that should be non‑negotiable for any departmental pilot​

• Mandatory DLP rules that block prompts containing classified, personally identifiable, or legally privileged information.
• Model‑backend logging that records which model was used, full input/output hashes (where permissible), timestamps and tenant metadata.
• Quotas and administrative caps by user group to limit cost and blast radius.
• Human‑in‑the‑loop gating for any Copilot outputs used in final policy or regulatory documents.
• A published “AI use register” listing where Copilot is used and for what business purpose.

---

Longer‑term implications and strategic trade‑offs​

Adopting Copilot is not just a technical decision; it’s an organisational change with three strategic trade‑offs:

• Speed vs. safety: rapid productivity wins come with exposures; the right balance is organisationally specific and must be continuously revisited.
• Vendor convenience vs. portability: Microsoft’s tight integration reduces friction but increases dependency; procurement must maintain exit paths and portability clauses where possible.
• Automation vs. human judgement: Copilot amplifies capability but cannot replace domain expertise in regulatory or scientific judgement — the department must preserve and fund critical human review processes.

---

Conclusion​

DCCEEW’s cautious Copilot pilot is exactly the type of evidence‑gathering approach public agencies should pursue: small, measurable, and tied to capability uplift and governance. The department’s recent move to own its IT stack puts it in a stronger position to control risks — but that autonomy brings responsibility. The single greatest determinant of success will not be the tool itself but the quality of the governance, telemetry and operational controls that sit around it.
If DCCEEW uses this pilot to build an auditable, instrumented, and human‑centred operating model — with clear DLP, monitoring, training and a gradual scale plan — the department could realise meaningful productivity gains without sacrificing the data sovereignty, transparency and trust obligations intrinsic to public service work. Conversely, rushing to broad deployment without those guardrails risks costly errors: data exposure, broken records trails and governance gaps that will be far harder to remediate later.

---

Source: iTnews Department of Climate Change dips toes into Microsoft Copilot