Do I Need to Complete a DPIA When Introducing Microsoft Copilot or Google Gemini Across the Organisation?

The short answer: Yes, in almost all cases.

If your organisation operates under the GDPR (General Data Protection Regulation) or the UK GDPR, implementing powerful generative AI tools like Microsoft Copilot or Google Gemini is not a simple IT rollout. It is a new form of data processing that almost certainly triggers the legal requirement to complete a Data Protection Impact Assessment (DPIA) before you "go live."

Integrating these tools without a DPIA is not just a compliance oversight; it's a significant legal and reputational risk. Regulators, like the UK's Information Commissioner's Office (ICO), have been clear: processing that is "likely to result in a high risk to the rights and freedoms of individuals" requires a DPIA. Generative AI, by its very nature, meets this high-risk threshold.

This article is a definitive guide for business leaders, Data Protection Officers (DPOs), and IT departments on why a DPIA is essential for Copilot and Gemini and how to approach it.

? Why Is a DPIA Mandatory for Generative AI?

A DPIA is a structured process to identify, assess, and mitigate data protection risks. Under the GDPR (Article 35), a DPIA is mandatory when processing involves:

  • The use of new technologies.
  • Systematic and large-scale processing of personal data.
  • Evaluation or scoring of individuals (profiling).

Generative AI tools like Copilot and Gemini easily meet these criteria:

  • New Technology: They are the definition of a "new technology," with complex "black box" algorithms whose full impact on personal data is still being understood.
  • Large-Scale Processing: When integrated into an enterprise suite like Microsoft 365 or Google Workspace, these tools will have access to and process a vast and continuous flow of personal data from emails, documents, chats, meetings, and more.
  • Evaluation and Profiling: These tools summarise employee performance (from chats and documents), analyse customer sentiment (from emails), and profile user behaviour to provide personalised responses. This constitutes "evaluation or scoring" even if it's not for a final automated decision.

The ICO's own guidance on AI confirms that in the "vast majority of cases," AI implementation will trigger the need for a DPIA. The potential risks are simply too high to ignore.

⚠️ The Unique Risks of Copilot and Gemini

A DPIA for generative AI is not a standard, tick-box exercise. You must assess a new class of risks that these powerful tools introduce to your organisation's data ecosystem.

Key Risks to Assess in Your DPIA

  • Massive Data Exposure:
    • The Problem: These tools connect to your organisation's "data graph" (e.g., Microsoft Graph). If your internal permissions are weak, a simple prompt from a junior employee could surface confidential HR files, sensitive client contracts, or executive-level financial planning.
    • Your DPIA Must Ask: How robust are our current access controls? How will we prevent data "leaking" across departments?

Data Minimisation vs. Model Needs:

  • The Problem: The GDPR's data minimisation principle (processing only the data that is necessary) is in direct conflict with AI models, which crave massive datasets to be effective.
  • Your DPIA Must Ask: What is our lawful basis for processing all this data? How can we limit the scope of data the AI can access (e.g., to specific SharePoint sites or user groups) and still achieve our purpose?

Transparency and the "Black Box":

  • The Problem: It is incredibly difficult to explain how an AI reached a specific conclusion. This challenges the right to be informed and the right to object to automated decision-making.
  • Your DPIA Must Ask: How will we explain to an employee or customer how their data was used by the AI? What level of human oversight will we implement to review or challenge the AI's output?

Accuracy and Algorithmic Bias:

  • The Problem: AI models can "hallucinate" (invent facts) and perpetuate or amplify existing biases found in your company's data, leading to discriminatory outcomes in summaries, analysis, or content creation.
  • Your DPIA Must Ask: What is our process for fact-checking AI output? How will we test for and mitigate bias, especially if the tool is used to support decisions about hiring, promotions, or customer service?

Data Transfers and Third-Party Risk:

  • The Problem: Where does your data go when it's processed by the AI? It may be sent to data centres outside your jurisdiction (e.g., to the US), and the vendor's policies on using your data for "model training" must be scrutinised.
  • Your DPIA Must Ask: What are the exact data flows? What international transfer mechanisms are in place (e.s., Adequacy, Standard Contractual Clauses)? Have we configured the tool's admin settings to opt out of our data being used to train the public model?

❓ Q&A: Your Key DPIA Questions Answered

Q: Microsoft and Google are huge companies. Haven't they already done a DPIA?

A: Yes, but they've done it from their perspective as a data processor. You, as the data controller, are solely responsible for conducting your own DPIA for your specific use of the tool.

Microsoft and Google provide resources, security white papers, and admin controls to help you with your DPIA, but they cannot do it for you. Your DPIA must analyse how your data, your employees, and your business processes create unique risks.

Q: We are only starting with a small pilot group. Do we still need a DPIA?

A: Yes. The "high risk" is not just about the number of users; it's about the nature of the technology. A DPIA is a "before you start" requirement. Conducting it during the pilot phase is the perfect time to identify risks and build mitigation measures before a full-scale rollout.

Q: This seems incredibly complex. What does a good DPIA process look like?

A: A robust DPIA for AI involves several key steps:

  • Describe the Processing: Map the data flows. What personal data will be processed (emails, documents, chats)? Who will it affect (employees, customers)? What is your lawful basis?
  • Consult Stakeholders: This is critical. You should consult with your DPO, IT security team, legal department, and representatives from the employee groups who will be using the tool.
  • Assess Necessity and Proportionality: Clearly define the business problem you are solving and justify why using this specific AI tool is a necessary and proportionate way to achieve it.
  • Identify and Assess Risks: Use the risk categories listed above (data exposure, bias, transparency, etc.) to systematically identify the risks to individuals' rights.
  • Identify Mitigation Measures: This is the most important part. For each risk, define a concrete measure.
    • Risk: Data exposure from poor permissions.
    • Mitigation: Conduct a full audit and clean-up of all Microsoft 365 / Google Workspace permissions before activation. Use data sensitivity labels (e.g., Microsoft Purview) to block the AI from accessing "Highly Confidential" data.
  • Risk: Lack of transparency.
  • Mitigation: Update your employee privacy notice and create a clear "Acceptable Use Policy" that explicitly states how the AI will be used, what it "sees," and who to contact with concerns.

Sign-Off: The DPIA must be signed off by your DPO and key project owners. If you identify high risks that you cannot mitigate, you are legally required to consult with your data protection authority (e.g., the ICO) before proceeding.

?️ Don't Navigate High-Risk AI Deployment Alone

Completing a DPIA for generative AI is a complex, high-stakes task that sits at the intersection of law, technology, and ethics. It requires specialised expertise that most organisations do not possess in-house.

This is where a trusted partner becomes invaluable.

At Formiti Data International, we are global data privacy experts. We provide the specialist knowledge and resources you need to deploy cutting-edge tools like Copilot and Gemini confidently and compliantly.

Our services are designed to solve this exact challenge:

  • Outsourced DPO Services: Our expert Data Protection Officers can lead and execute the entire DPIA process for you, working with your internal teams to ensure every risk is identified and mitigated.
  • Expert Data Protection Consultancy: We don't just identify problems; we provide solutions. Our team offers practical, actionable advice on everything from auditing your data permissions to configuring your admin consoles and drafting the policies you need to protect your organisation.
  • Global Expertise, Local Knowledge: With deep expertise in the GDPR, UK GDPR, and over 120 other global privacy laws, we ensure your AI deployment is compliant not just locally, but everywhere you do business.

Rolling out generative AI is a major competitive step. Ensure you do it right. A DPIA is not a barrier to innovation; it is the blueprint for doing it responsibly.

Before you activate a single license, let us help you build that blueprint.

Click here for a free consultation