{"id":2724,"date":"2026-06-01T10:36:02","date_gmt":"2026-06-01T10:36:02","guid":{"rendered":"https:\/\/sawaryn.com\/uslugi\/gdpr-for-ai\/"},"modified":"2026-07-14T16:41:16","modified_gmt":"2026-07-14T16:41:16","slug":"gdpr-for-ai","status":"publish","type":"service","link":"https:\/\/sawaryn.com\/en\/uslugi\/gdpr-for-ai\/","title":{"rendered":"GDPR for AI"},"excerpt":{"rendered":"<p>During the workshop, we\u2019ll assess whether your AI use case involves personal data, identify the risks, and determine what needs to be set up (roles, policies, documentation) to implement AI in a secure and predictable manner.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"specialization":[1215],"practice_area":[],"class_list":["post-2724","service","type-service","status-publish","hentry","specialization-gdpr"],"acf":{"short_description":"During the workshop, we\u2019ll assess whether your AI use case involves personal data, identify the risks, and determine what needs to be set up (roles, policies, documentation) to implement AI in a secure and predictable manner.","full_description":"<strong>GDPR for AI in Practice<\/strong>\n\n<strong>GDPR for AI, which streamlines implementation-rather than halting it.<\/strong>\n\nDuring the workshop, we\u2019ll assess whether your AI use case involves personal data, identify the risks, and determine what needs to be set up (roles, policies, documentation) to implement AI in a secure and predictable manner.\n\nA snapshot from a working meeting on AI implementation: a screen showing a data flow map, with the client and supplier roles highlighted, and representatives from business, IT, and compliance on both sides.\n<h2>CUSTOMER'S PROBLEM<\/h2>\nAI is already in use at the organisation. The rules, however, usually\u2026 aren\u2019t yet. \n\nMost often, it\u2019s not the technology that\u2019s the issue-it\u2019s the lack of clear guidelines. The team is testing AI, but the organisation still isn\u2019t clear on what data goes into the process, who is responsible for what, and how it can be scaled safely (and quickly). \n\n\"It's not about telling AI 'no.' It's about determining where personal data is, what the real risks are, and what needs to be sorted out so the project can move forward.\" \n\nIn practice, companies want to avoid two extremes: regulatory chaos and a \u201cstandstill\u201d during the implementation phase. They need decisions, order, and an operational model that they can defend before the board, the team, and-if necessary-the regulator. \n<h2>MOST COMMON PROBLEMS \/ CHALLENGES<\/h2>\n<strong>AI works faster than rules<\/strong>\n\nTeams are testing tools, vendors are demonstrating new capabilities, and the organisation lacks a single, unified framework for evaluating data, risks, and accountability.\n\nWe establish the guidelines: use cases, roles, and the scope of documentation needed to safely manage the project.\n\nWe analyse the AI's operating model and identify what needs to be determined at the outset and what can be refined in subsequent steps.\n\n<strong>Business benefit:<\/strong> Less chaos, faster decisions, and easier scaling of future implementations.\n\n<strong>Risk of inaction:<\/strong> Every new project starts from scratch, and the same mistakes recur in subsequent use cases.\n\n<strong>It is unclear whether AI even falls within the scope of personal data<\/strong>\n\nThe team uses AI, but there is still uncertainty about what data is fed into the tool, where it comes from, and what happens to it afterward.\n\nWe map the data and determine whether the project falls under the GDPR.\n\nWe determine what data is involved in the process, what the purpose of its use is, and what the boundaries of a secure operating model are.\n\n<strong>Business benefit:<\/strong> No more guesswork-a quick decision on whether to proceed with the implementation (and how).\n\n<strong>Risk of inaction:<\/strong> The project is based on assumptions that could fall apart in the event of an audit, an incident, or scaling.\n\n<strong>The roles of the customer and the supplier are unclear<\/strong>\n\nA company purchases an AI solution but is unsure who is the controller, processor, or joint controller-and what obligations this entails.\n\nWe are clarifying roles, responsibilities, and the model for collaboration with the supplier.\n\nWe review the relationships between the client, the supplier, the integrator, and internal teams, and identify the necessary safeguards (both contractual and procedural).\n\n<strong>Business benefit:<\/strong> More control over the contract and fewer hidden risks in the partnership model.\n\n<strong>Risk of inaction:<\/strong> The organisation assumes a liability that it does not understand-or that it has not addressed in the contract.\n\n<strong>AI supports decisions about people, but no one has assessed the consequences<\/strong>\n\nIn HR, sales, marketing, and customer service, practices such as segmentation, scoring, recommendations, and candidate preselection are becoming common-yet the legal and operational consequences have not been fully assessed.\n\nWe are assessing whether your activities fall under the scope of profiling or automated decision-making.\n\nWe review risk areas, disclosure requirements, and the level of safeguards needed for a given process.\n\n<strong>Business benefit:<\/strong> You can use AI in a process that is defensible both operationally and legally-without the risk of \u201csurprises.\u201d\n\n<strong>Risk of inaction:<\/strong> The tool works, but the organisation cannot explain how it affects people or on what basis.\n\n<strong>Each subsequent use case must be analysed anew<\/strong>\n\nThere are no guidelines for using AI, no escalation procedures, and no standardised documentation model-so every issue has to be \u201creinvented\u201d from scratch.\n\nWe are laying the groundwork for operations that can be applied to future implementations.\n\nWe are developing rules for using AI, documentation guidelines, and decision-making logic.\n\n<strong>Business benefit:<\/strong> The organisation gains order, not just a one-time opinion.\n\n<strong>The risk of inaction:<\/strong> AI is scaling faster than our ability to control the process.\n<h2>HOW OUR SOLUTION WORKS<\/h2>\nWhat exactly do you get with the GDPR service for AI?\n\nThis isn't just a \"just-in-case\" analysis. We're working on a specific AI use case and delivering: decisions, recommendations, and a package of materials that truly facilitate implementation. \n\n<strong>AI Use Case Analysis and Data Mapping<\/strong>\n<ul>\n \t<li>Business Objective of the Solution<\/li>\n \t<li>types of data in the process<\/li>\n \t<li>Data Sources and Information Flow<\/li>\n \t<li>users, providers, and implementation context<\/li>\n<\/ul>\n<strong>Result:<\/strong> a use case map and clarity on whether and where personal data appears.\n\n<strong>Assessment of Roles and Responsibilities<\/strong>\n<ul>\n \t<li>the role of the customer, supplier, and other parties<\/li>\n \t<li>Division of Responsibilities in the Operational Model<\/li>\n \t<li>matters requiring contractual or procedural safeguards<\/li>\n<\/ul>\n<strong>Result:<\/strong> a well-organised model of roles and responsibilities.\n\n<strong>Verifying the Legality of Data Use in AI<\/strong>\n<ul>\n \t<li>training, testing, and validation<\/li>\n \t<li>providing tools with data on customers, employees, or candidates<\/li>\n \t<li>Consistency between the purpose of processing and the planned use case<\/li>\n<\/ul>\n<strong>Result:<\/strong> recommendations on what is permissible, under what conditions, and what needs to be changed.\n\n<strong>Assessment of GDPR Risks Specific to AI<\/strong>\n<ul>\n \t<li>profiling and decision automation<\/li>\n \t<li>Data minimisation and retention<\/li>\n \t<li>transparency and accountability<\/li>\n \t<li>Disclosure requirements and areas requiring further analysis<\/li>\n<\/ul>\n<strong>Result:<\/strong> a list of realistic risks and priorities for action.\n\n<strong>An Overview of the Model for Collaboration with AI Providers<\/strong>\n<ul>\n \t<li>the need for a data processing agreement or other provisions<\/li>\n \t<li>Restrictions on the provider's use of data<\/li>\n \t<li>Required organisational and contractual safeguards<\/li>\n<\/ul>\n<strong>Result:<\/strong> recommendations for working with the supplier and a list of items to be addressed.\n\n<strong>Guidelines for Using AI in an Organization<\/strong>\n<ul>\n \t<li>What's allowed, what's not allowed<\/li>\n \t<li>What data is excluded?<\/li>\n \t<li>When an issue needs to be escalated<\/li>\n \t<li>How to Standardise Team Practices<\/li>\n<\/ul>\n<strong>Result:<\/strong> Working guidelines for employees and teams on the use of AI.\n\n<strong>Documentation and Implementation Support<\/strong>\n<ul>\n \t<li>DPIA and Risk Analysis<\/li>\n \t<li>Information Recommendations<\/li>\n \t<li>project documentation<\/li>\n \t<li>embedding requirements within a real-world business process<\/li>\n<\/ul>\n<strong>Result:<\/strong> readiness to implement the recommendations without postponing the documentation.\n<h2>WHO IS THIS SERVICE FOR?<\/h2>\nWho most often needs this support?\n\nThis service is for organisations that want to use AI in a practical way but don't want to base their project on guesswork.\n<ul>\n \t<li>Compliance Manager - needs to quickly assess the AI use case and translate the topic into specific recommendations for the teams.<\/li>\n \t<li>Chief Digital Transformation Officer - wants to implement AI more widely, but needs guidelines to streamline collaboration between the business, IT, and vendors. <\/li>\n \t<li>Board Member \/ COO - Seeks a model that allows for scaling AI while maintaining control over risk, accountability, and decision-making.<\/li>\n<\/ul>\n<h2>STAGES OF WORK<\/h2>\nWhat Does the Collaboration Look Like?\n\nWe work to move from \u201cwe don\u2019t know\u201d to a decision in a structured way. Without passing the buck and without documents that look nice but don\u2019t help the project. \n\n<strong>Step 1. Defining the business context and mapping the use case <\/strong>\n\nWe'll explore the tool, the purpose of its implementation, the types of data, the users, the providers, and how it works.\n\n<strong>What the client gains:<\/strong> a shared understanding of the project and an end to the guesswork about what we're really talking about.\n\n<strong>Phase 2. Legal and Operational Assessment and Risk Identification <\/strong>\n\nWe examine where the processing of personal data actually takes place, as well as the roles, responsibilities, and risk areas involved.\n\n<strong>What the client gains:<\/strong> clarity on what is critical, what needs to be addressed, and what does not require excessive action.\n\n<strong>Step 3. Recommendations and Security Design <\/strong>\n\nWe translate these findings into specific recommendations regarding contracts, organisation, information, documentation, and processes.\n\n<strong>What the client gets:<\/strong> a practical action plan instead of advice that\u2019s hard to put into practice.\n\n<strong>Step 4. Implementation and Organization of Decisions <\/strong>\n\nWe provide support with DPIA, documentation, AI usage policies, and integrating recommendations into business processes.\n\n<strong>What the customer gains:<\/strong> the ability to continue implementing AI in a more predictable and controlled manner.","icon":"","order_within_specialization":0,"flexible_content":[{"acf_fc_layout":"hero","section_header":{"section_header":{"eyebrow":"","heading":"GDPR for AI in practice","heading_highlight_mode":"none","heading_split":{"small_part_text":"","strong_part_text":"","":null,"small_part_size":"","strong_part_size":"","tag":"h2","style":"inherit","alignment":"inherit"},"description":"During the workshop, we assess whether your AI use case involves personal data, where the risks lie and what needs to be put in place (roles, rules and documentation) so that AI can be implemented safely and predictably.","":null,"heading_tag":"h1","heading_style":"hero","alignment":"left"}},"actions":{"button_cluster":{"intro":"","buttons":[{"button":{"link":{"title":"Book a call about your AI use case","url":"#contact","target":""},"variant":"primary","icon":"arrow","icon_size":"inherit","size":"default","aria_label":""}}],"alignment":"left"}},"media":{"media":{"image":"","mobile_image":"","object_position":"center","object_position_custom":""}},"section_meta":{"anchor_id":"","is_enabled":true},"variant":"split","mobile_body_card":false},{"acf_fc_layout":"person_cta","person":[2654],"section_header":{"section_header":{"eyebrow":"","heading":"AI is already in use within the organisation. The rules usually are not\u2026 yet.","heading_highlight_mode":"none","heading_split":{"small_part_text":"","strong_part_text":"","":null,"small_part_size":"","strong_part_size":"","tag":"h2","style":"inherit","alignment":"inherit"},"description":"It is usually not the technology that causes problems, but the lack of clear arrangements. The team is testing AI, while the organisation still lacks clarity about what data enters the process, who is responsible for what and on what basis the solution can be scaled safely (and quickly).\n\nThe point is not to say 'no' to AI. 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