AI for Data Privacy and Compliance: Prompt Engineering for DPOs
- 10/09/2025
While the work of a Data Protection Officer (DPO) cannot yet be fully automated, AI can accelerate the completion of routine tasks and improve idea generation. However, to obtain accurate and relevant results, knowledge about the proper use of AI tools is essential. In this article, we explore the key elements of effective prompts that help you achieve compliance, examine various approaches to prompt building, and provide guidance on using AI safely without creating risks for users.
What is Prompting?
Prompt engineering is a relatively new discipline focused on developing and optimizing prompts to effectively use large language models (LLMs). This skill is critical for DPOs looking to maximize AI’s potential. By mastering prompt engineering, DPOs gain better insight into these models’ capabilities and limitations.
How effective prompts are built? Best practices of prompt engineering
While a prompt doesn’t require a specific structure, it becomes much more effective when you provide clear, detailed instructions to the model.
Remember: LLMs are not experts. They’re more like interns who need thorough explanation for each task and step. The more detailed your instructions, the better the results.
Elements of an effective prompt typically include:
📎 Instruction or question: The main query to AI, a clear formulation of the task. For example, “Identify potential data protection risks in the rollout of a new telemedicine platform.”
📎 Input data and context: Information that helps AI understand the request. This could be a description of a business process, relevant data protection legislation, or a specific task. Example: “Include specific reference to GDPR requirements for health data, system architecture involving third-party cloud services, and expected data retention policies.”
📎 Format: Indicates the format of the desired result (e.g., table, list, paragraph of text). Example: “Provide the result as a risk matrix with columns for risk description, likelihood, impact, and mitigation measures.”
📎 Examples: Providing AI with examples of desired output improves accuracy and quality of results. Example: “In similar DPIAs, we evaluate risks like biometric authentication data breaches, profiling in employee monitoring tools, or location tracking in ride-sharing apps.”
📎 Persona: Instruction for AI to act in a certain role, which helps AI understand the context and use appropriate terminology. Example: “Respond as a senior data protection officer with 15 years of experience in healthcare technology compliance.”
This approach may not yield perfect results. But it will significantly accelerate your drafting and ideation process, providing a solid foundation to build upon.
Also learning fundamental prompting techniques can help you reach better results. While creating a prompt you can use:
📎 Zero-shot prompting: Direct task formulation, where LLM uses its background knowledge. You can use this prompt for quick drafts and simple questions, but the result can be superficial.
💡 Example: Review the AI and privacy market in Europe: trends, key players, regulation.
📎 Few-shot prompting: Including 2-3 examples in the query, which activates the in-context learning mechanism. The model imitates given patterns, which is useful for unifying style and format.
💡 Example:
Example 1:
Fintech in Europe:
- Players: Revolut, Klarna
- Regulation: PSD2
- Trends: growth of BNPL
- Risks: pressure from regulators
Example 2:
E-commerce:
- Players: Zalando, Allegro
- Regulation: Digital Services Act
- Trends: cross-border trade
- Risks: logistics, returns
Do the same review for the AI and privacy market in Europe.
📎 Chain-of-Thought (CoT) prompting: Step-by-step thinking, where the model is explicitly asked to show internal reasoning. This is based on cognitive decomposition and improves accuracy in complex tasks, but can lead to long answers and risk of “hallucinations.”
💡 Example:
Think step by step:
Describe the current state of the AI and privacy market in Europe.
Add the role of GDPR and the AI Act.
Identify key players (startups and corporations).
Formulate three predictions for the next two to three years.
Describe the risks and opportunities this creates for consulting.
📎 Reflection prompting: The model checks and improves its own response based on metacognition. Implemented through iterations using a checklist to improve quality and eliminate errors.
💡 Example:
Review your previous answer and rewrite it following these steps:
- Completeness: Check that the overview contains all key sections —
Regulation (GDPR, AI Act, Digital Services Act, etc.)
Players (corporations, startups, regulators)
Market trends
Forecasts for 2–3 years
- Accuracy:
- Remove general statements (“the market is developing rapidly”),
- Replace them with specifics (figures, examples, specific companies/initiatives).
- Applicability:
Add a separate section titled “What this means for consulting and our services.”
Highlight 3–4 business opportunities.
Output format:
- Brief summary (≤200 words)
- Table: [Section | Key facts | Significance for consulting]
Through these examples we can define the main advanced prompting techniques that can empower your AI usage:
📎Role prompting: Setting a specific role for AI (e.g., “You are a GDPR consultant”), which affects the style and depth of the response.
📎 Prompt chaining: Breaking down a complex task into several sequential prompts.
📎 Retrieval-Augmented Generation (RAG): Connecting external knowledge bases to provide AI with additional information, which increases the accuracy and relevance of generated text and reduces the risk of “fabrication.” For DPOs, this is especially useful for adapting AI output to organization-specific needs, for example, when creating a privacy policy. The knowledge base can include a record of processing activities (ROPA), data retention policy, and a list of data processors.
📎 Self-consistency: Generating multiple solutions, comparing them, and selecting the best one based on given criteria.
📎 Emotion prompting: Adding motivation (e.g., “The task is critically important for the company strategy”), which can increase response accuracy.
It is important to avoid anti-patterns, such as overly general requests (e.g., “Do everything”), examples without structure, attempts to solve a multi-component task with a single prompt, or verification requests without clear criteria.
You can find more ideas on how to make prompts work effectively for your desired results here in the Prompt Engineering Guide. If you want dive deeper into the world of automation the work with various automations and AI-based tools, join our practical AI Tools for DPO course. There you’ll get a full package of materials and templates to boost effectiveness of data protection practices.
How AI helps Reach Compliance?
AI is a powerful tool that enhances DPO work by automating routine tasks, generating text, analyzing documents, and helping DPOs stay current with the latest data protection legislation.
Main benefits of using AI:
📎 Semantic search: AI understands the meaning of words and phrases, not just matching keywords. This helps DPOs find information in large datasets and formulate more relevant queries.
📎 Text generation: AI can create drafts of documents such as privacy policies, data processing agreements, and records of processing activities (RoPA).
📎 Legitimate interest and risk assessment: AI can assist in conducting these assessments.
📎 Legislative updates: AI can track changes in legislation and regulatory guidelines, helping DPOs stay up-to-date with the latest developments.
Examples of AI use for various processes in data protection:
📎 Development of key documents: AI can generate initial drafts of privacy policies, data processing agreements, internal policies, and data breach response plans. This saves time and provides a starting point, though it doesn’t replace the expert opinion of the DPO.
📎 Creating a Record of Processing Activities (ROPA): This is one of the most labor-intensive tasks for DPOs. AI can analyze business processes and automatically fill in ROPA sections, such as data categories, processing purposes, and retention periods. Our team has developed a prompt for LLMs that allows creation of a RoPA. It includes a persona (experienced business process expert), context (DPO needs a general understanding of the process for ROPA), instruction (describe the process in points), and format (Markdown, tables).
📎 Results that DPOs can get with just a few clicks:
- Description of the business process.
- Variations of data processing purpose formulations.
- Process stages, including hints about additional processing, data categories, and processors.
- Categories of personal data with processing purposes (in tabular form).
- Hints about possible processing periods.
- Types of data processors and examples (e.g., Mailchimp for Email Service Providers).
- Understanding which department or division to contact for information about the process, and who the process owner is.
- A collection of privacy risks, serving as a starting point for legitimate interest assessment (LIA) or data protection impact assessment (DPIA).
- Examples of fines and regulatory measures that can help convince businesses of the need for ethical and secure practices.
📎 Generating ideas for potential risks for Data Privacy Impact Assessment (DPIA): AI can analyze business processes, identify data categories, and highlight potential risk scenarios, such as security vulnerabilities or possible impacts on the rights of data subjects.
Ready to Use Prompts for Compliance
Our team has been curious and enthusiastic about AI from the very beginning of its popularity. We believed that AI could be a powerful incentive for small businesses and specialists to reduce the number of routine tasks and focus on strategic privacy issues. And while we were testing the capabilities of different models, we developed a list of prompts that can really simplify some of the tasks of a DPO. You can get them for free at this link. We hope they will be a useful tool for your work and a good example for building your own effective prompts to stay compliant.
User Privacy and AI: How to Avoid Privacy Risks
To safely and effectively use AI in their work, DPOs must consider a number of important aspects. AI does not replace expert opinion and requires a careful and critical approach.
Here’s what a DPO should do:
📎 Be fully aware of their actions: DPOs need to deeply understand how AI works, what capabilities it offers, and what risks it carries.
📎 Acquire necessary skills: This includes prompt engineering skills and critical evaluation of results to effectively interact with AI and obtain accurate data.
📎 Continuously weigh all “pros” and “cons”: It is important to continuously assess the potential benefits and drawbacks of using AI for specific tasks.
📎 Consider ethical barriers: Using AI raises questions about intellectual property rights, as models are trained on content created by humans and may be accused of “stealing” it. DPOs should be aware that even their own input may be used to train neural networks.
📎 Critically verify AI results:
- Beware of “hallucinations” and superficial answers: Neural networks can provide inaccurate or fabricated data, especially on narrow and rarely covered topics in data protection. AI is, at minimum, prone to “banalization” of everything it touches.
- Correct terminological errors: AI may replace data protection-specific terms (e.g., “privacy” with “confidentiality,” “legitimate interest” with “lawful interest,” “anonymization” with “depersonalization”), which have different meanings. This requires additional instructions in prompts and careful verification.
📎 Maintain their own expertise and prevent stagnation: DPOs should maintain critical thinking and not allow AI to “push the profession toward obvious errors, inaccuracies, and stagnation,” as AI often offers the most common, rather than the most accurate or correct thoughts.
📎 Be extremely cautious when working with personal data: Applying AI to personal data is extremely dangerous due to high risks of de-anonymization of people, biased and erroneous decisions, and discrimination. This aspect is particularly well-known to data protection specialists and requires maximum attention.
We’re confident you don’t want ChatGPT or other AI models to replace you. Therefore, maintain your authority by not allowing AI to make your final decisions.
Conclusion
AI is not an inevitable enemy, but rather a powerful tool that can not only provide effective data management but also help protect it. Understanding and managing risks associated with AI use becomes a key aspect of successful integration of this technology. Final decisions should remain within the responsibility of the DPO.
Description: In this article we share how to build AI prompts that help you reach compliance and don’t violate data privacy.
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