Sage AI & Copilot: The 7 Security Risks UK Accounting Firms Can’t Ignore in 2025

Why Sage AI Matters – and Why Governance Has to Catch Up

Sage Copilot was unveiled in early 2024 and is now in use by over 40,000 early adopters across the UK, US and Europe. It’s tightly integrated into Sage’s cloud accounting products, automating tasks like bank reconciliation, invoice processing, cash flow forecasting, and anomaly detection.

That’s good news for productivity. But for partners and practice owners, it raises a bigger question: how do you make sure AI is helping your firm, not quietly increasing cyber risk and GDPR exposure?

At PPCS, we specialise in ISO 42001 AI governance for accountants, ISO 27001 information security and Cyber Essentials for accounting practices. What we’re seeing is simple: the firms that lean into AI with clear governance, policies and controls will move faster, win better clients, and sleep better at night. The ones that switch it on without thinking will inherit a set of risks they don’t yet see.

Quick Recap: What Sage AI and Copilot Actually Do

Sage has embedded AI across its Business Cloud stack. In practice, that means tools like Copilot can:

• Automate bank reconciliation with intelligent matching
• Process invoices and help predict payment behaviour
• Generate cash flow forecasts and flag variances
• Assist with tax calculations and compliance reminders
• Surface anomalies or unusual transactions for review
• Create draft reports and respond to natural-language questions about your data

Technically, Sage is building these services on top of cloud infrastructure such as Amazon Web Services (AWS), using custom models built specifically for accounting terms. Sage’s public commitments state that customer data isn’t used to train third-party foundation models, and that data isn’t shared between customers.

That’s reassuring – but it doesn’t remove your responsibilities under UK GDPR, professional standards, or the need for a proper AI management framework (ISO 42001).

The January 2025 Sage Copilot Incident – What Actually Happened

In January 2025, Sage temporarily suspended access to Copilot after a customer reported an issue: when they asked Copilot to show recent invoices, the AI surfaced information belonging to other businesses as well as their own.

Public reporting described this as a data-isolation failure. Sage characterised it as a “minor issue” affecting a very small number of customers, and stated that while unrelated business information was shown, “at no point were any invoices exposed”. Copilot was in early access at the time, so the number of organisations affected was limited.

However you frame it – “minor glitch” or “data leak” – the message for accounting firms is the same: even reputable vendors can get AI isolation wrong. If you are relying purely on vendor assurances without your own governance and monitoring, you are accepting risk you haven’t formally assessed.

That’s where the seven risks below come in.

The 7 Security & Governance Risks of Sage AI That Firms Can’t Ignore

These risks don’t just apply to Sage; they apply to any AI-powered accounting platform. But because Sage has such a strong footprint in the UK, Copilot is often the first place accountants encounter them.

1. Risk #1Data Residency & Third-Party AI Processing

   When you use Copilot, your client data is being processed by AI models. The first question is: where and under which legal regime is that processing happening?

   Sage’s documentation and AI commitments explain that data is hosted on trusted cloud platforms and is not shared with other customers or with third parties for model training. That’s positive, but as a data controller you still need to understand:

   • Which regions data is stored and processed in (UK, EU, US, elsewhere)
   • Whether data is used for model fine-tuning and, if so, on what basis
   • How long AI-processed data and prompts are retained

   What you should do: review your Data Processing Agreement (DPA) with Sage, document data flows for AI features, and decide whether certain sensitive clients should be excluded from AI processing. This links closely to your ISO 27001-style data mapping and risk assessments.

2. Risk #2Lack of Explainability in AI-Assisted Decisions

   When Copilot suggests a categorisation, flags an anomaly, or presents a forecast, it doesn’t always tell you why. For accountants, that’s a problem. You may need to justify those decisions to:

   • Clients querying their financial statements
   • HMRC during an investigation or enquiry
   • Professional indemnity insurers after a dispute
   • Professional bodies if a complaint is raised

   Professional guidance from bodies like ICAEW is clear: you must maintain professional scepticism, understand the limitations of AI outputs and be able to explain the basis of your advice.

   What you should do: never treat AI output as “final”. Build a documented review process for AI-assisted work, keep audit trails of who approved what, and make sure staff know when to escalate AI suggestions for partner review. These controls fit naturally into an ISO 42001 AI management system.

3. Risk #3Shared Processing & Data Isolation Failures

   The January 2025 incident showed that, under certain circumstances, AI could surface data from more than one customer in the same context. Sage moved quickly to fix the issue, but it underlines a wider point: in multi-tenant cloud environments, isolation isn’t just a database feature – it’s also an AI behaviour challenge.

   Even if your data isn’t being used to train a shared model, there’s still a risk that caching, context windows, or orchestration errors could cause information to bleed between customers.

   What you should do: ask Sage to confirm how they enforce tenant isolation at each layer (storage, APIs, AI context). For very high-sensitivity clients, consider explicitly excluding them from AI workflows until you are satisfied with the controls. Record these decisions in your AI risk register as part of your ISO 42001 work.

4. Risk #4Over-Reliance on Copilot Without Human Oversight

   As Copilot becomes more capable, there’s a natural temptation for teams to “trust it” and move faster. The danger is that junior staff start treating Copilot as an infallible oracle, rather than a tool that still needs checking.

   Over time, that can lead to:

   • Mis-categorised transactions going unnoticed
   • Subtle forecasting errors that compound over months
   • Incorrect client assumptions being baked into AI prompts

   What you should do: define clear boundaries for AI. For example: “Copilot can draft, humans finalise.” Put in place checklists for AI-assisted processes, monitor error rates, and build AI use into your regular file reviews and cold file audits. Your governance should be strong enough that if Copilot disappeared tomorrow, your quality wouldn’t fall off a cliff.

5. Risk #5Fragile Integrations with Legacy Systems and APIs

   Sage doesn’t sit alone in your tech stack. It often connects to:

   • Practice management systems
   • Client document portals
   • Cloud storage (e.g. OneDrive, SharePoint)
   • Bridging software for Making Tax Digital (MTD)
   • Bank feeds and payment services

   Each integration point is another place credentials can be stolen, misconfigured or abused – and AI can happily automate on top of bad data. A small sync issue, or a compromised API key, can turn into a much bigger problem once AI starts making decisions based on it.

   What you should do: map every system that connects into Sage and Copilot. Apply the principle of least privilege to API keys and service accounts. Make sure you have at least Cyber Essentials-level controls in place (see our Cyber Essentials certification for accountants). Test how Copilot behaves when data is incomplete or corrupted, so you aren’t surprised in production.

6. Risk #6Shadow AI Usage by Staff

   Because Copilot is embedded directly inside Sage, it’s easy for staff to experiment with it without thinking about policy. Common patterns we see:

   • Copy-pasting sensitive client information into open-ended prompts
   • Using AI-generated narrative in reports without noting the source
   • Testing “what-if” questions on live production data

   None of this is malicious, but it can create real compliance and security issues if left unmanaged.

   What you should do: create an AI Acceptable Use Policy specifically covering Sage and any other AI in your stack. Train staff on what is and isn’t appropriate to ask Copilot. Where technically possible, enable logging of AI activity. Then, as part of your ISO 27001-style monitoring, periodically review how AI is actually being used in practice.

7. Risk #7Vendor Lock-In and No Exit Strategy

   Once you’ve woven Copilot into your workflows – automated close, anomaly detection, client reporting – it becomes hard to imagine operating without it. That’s the moment you need to ask: what’s our plan if this changes?

   You may be reliant on:

   • AI-generated insights that you can’t easily recreate elsewhere
   • Workflows that only exist inside Sage
   • Staff who are trained in “how Copilot works”, not in underlying principles

   What you should do: document every business process that currently relies on Sage AI. For critical workflows, maintain a manual or non-AI path so you’re not boxed in. Regularly export and archive important AI-generated reports, and keep an eye on alternative AI-enabled accounting tools so you have options if you ever need to move. This kind of planning fits naturally under ISO 42001, which expects you to think about AI lifecycle and change management.

Sage’s AI Trust Label – Helpful, but Only Half the Story

In June 2025, Sage announced its AI Trust Label – a framework to explain how AI is built and used in its products. By November 2025, the label was live for tens of thousands of users in the UK and US, providing more transparency on:

• How customer data is used and protected
• Which models are involved in processing
• What controls exist to reduce bias and harm
• How accuracy and ethical performance are monitored

That’s a step in the right direction and puts Sage ahead of many vendors. But it still only covers vendor-side behaviour. It doesn’t create your AI policies, train your staff, or provide your audit trails.

For that, you need your own AI governance framework – ideally aligned to ISO 42001 and integrated with your existing ISO 27001 and Cyber Essentials work.

Why ISO 42001 Matters for Sage AI Users

ISO 42001 is the first international standard for AI management systems. It’s designed to help organisations:

• Identify and assess AI-specific risks
• Define roles, responsibilities and oversight
• Ensure transparency and explainability where required
• Control how data flows into and out of AI systems
• Monitor AI accuracy, bias and security over time

For UK accounting firms, ISO 42001 sits neatly alongside:

• UK GDPR and data protection requirements
• Professional standards from bodies such as ICAEW and ACCA
• Professional indemnity insurers’ expectations
• HMRC and Making Tax Digital cyber security obligations

At PPCS, we help practices implement ISO 42001 in a pragmatic way: not as a tick-box exercise, but as a way to safely adopt AI across platforms like Sage, Xero and others. If you’ve read our piece on Xero + AI and ISO 42001, you’ll recognise the same patterns here.

Practical Next Steps for Your Firm

If you’re already using Sage AI – or planning to switch it on – here’s how to start bringing your governance up to scratch:

1. Before You Enable Copilot

• Review your DPA and contract with Sage for AI-specific clauses
• Confirm data residency and cross-border data transfer arrangements
• Identify high-sensitivity clients and decide whether to opt them out of AI

2. During Implementation

• Create an AI Acceptable Use Policy covering Sage and other tools
• Train all staff on AI risks, limitations and escalation routes
• Define which decisions must always have human sign-off
• Map and secure all integrations feeding data into Sage

3. Ongoing Monitoring

• Review AI-assisted workflows at least quarterly
• Track errors, anomalies and near-misses involving AI
• Update training as features and risks evolve
• Maintain manual fallback processes for critical tasks