Using AI for DSAR responses: What every organisation should know

What makes DSARs so challenging?

DSARs are a fundamental right under the EU and UK General Data Protection RegulationRegulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation). (GDPR) that grant individuals certain rights, including the ability to obtain a copy of the personal information that an organisation holds about them. However, responding to DSARs can be resource-intensive and stressful, putting pressure on teams whilst increasing the risk of compliance missteps. 

The biggest DSAR challenges include: 

• Rising volumes: DSARs are becoming more common, particularly from employees, customers, or individuals involved in disputes 
• Manual processes: Locating and collating data across different systems is time-consuming and prone to error 
• Compliance risk: Missing data, disclosing too much, or breaching statutory deadlines exposes organisations to regulatory action 
• Time sensitivity: Organisations typically have one month to respond, with limited room for extension 

Can AI help with DSARs?

AI tools are promoted as a way to cut costs and streamline repetitive tasks. But when it comes to DSARs, can AI truly ease the burden, or is this an overstated promise? 

AI can help with efficiency, but DSARs are not simply about retrieving data. They demand context, understanding of regulatory nuance, and human judgement — qualities AI can support but not replace.  

The hidden risks of relying on AI in DSAR responses

In the rush to adopt new time-saving technologies, it is easy to overlook the risks that come with them. When applied to DSAR responses, shortcuts can quickly create bigger problems. Organisations that lean too heavily on AI without the right safeguards in place risk compliance gaps and reputational damage. They also open the door to new challenges, including: 

• Incomplete or over-redacted responses
  AI may miss certain personal dataInformation which relates to an identified or identifiable natural person. or remove too much, leading to inaccurate or non-compliant disclosures
   

• Context limits
  Natural language processing tools can struggle with nuance, particularly in unstructured formats like emails, PDFs, or chat logs
  
  

• Accountability
  Responsibility for errors lies with the organisation, not the AI provider. Relying too heavily on AI does not shift liability
  
  

• Privacy and security concerns
  Feeding personal data into AI systems creates additional processing risks, especially if the tool involves cloud storage or third-party access
  
  

• Transparency
  Organisations must update privacy notices and sub-processor lists to include any changes to how they processA series of actions or steps taken in order to achieve a particular end. DSARs. Fully automated DSAR processing triggers additional duties of explainability

Learn more about AI Explainability and AI Governance.  

Where AI can add value in DSAR handling

The hidden risks of using AI in DSAR responses don’t rule out its use but rather set the conditions for how it can add value. The next step is to understand where AI can genuinely support the process. AI won’t replace human expertise, but it can lighten the load by streamlining repetitive, resource-intensive tasks.  

Here’s where AI can potentially provide useful support: 

Data discovery

AI can scan for personal data across multiple systems, from structured databases to unstructured formats like emails and PDFs. It can also help impose order on unstructured data through tagging, categorisation, and linking information for easier review and extraction. By efficiently identifying and organising relevant information, AI can help to reduce delays at this early stage.

Redaction

Responding to DSARs often requires the removal of sensitive or third-party data, which can be a time-consuming and error-prone task when done manually. AI-powered redaction tools can automate some of this process, reducing the risk of human error while leaving space for human reviewers to validate the output.  

Identity verification

Before any data is disclosed, organisations must verify that the individual making the request is who they claim to be. AI can assist here by supporting document verification, biometric matching, or fraud detection tools. However, although AI can add a layer of assurance, they work best when supported by clear policies and human review. 

Workflow automationA process or a system that operates automatically.

Beyond data tasks, AI-enabled platforms can also support administrative tasks like generating standard response letters, setting reminders, tracking deadlines, and automating internal task management. This can reduce certain repetitive tasks, freeing up staff capacity. However, automation does not remove responsibility, and human oversight remains essential to ensure that every action stays aligned with data protection requirements.

How can organisations use AI in DSARs responsibly?

Strong governance is critical when introducing AI into DSAR handling. The following guidance can help organisations adopt AI safely and effectively: 

• Conduct DPIAs
  Assess risks and  safeguards before deploying AI tools into DSAR workflows, documenting how you will mitigate potential issues.
  

• Maintain audit trails
  Keep clear records of decisions, processes, and system outputs to demonstrate accountability.
  

• Train staff
  Ensure employees understand both DSAR requirements and how to use AI responsibly.
  

• Choose vendors carefully
  Ask questions about accuracyIn data protection terms, the concept of ensuring data is not incorrect or misleading., transparency, and how the tool processes personal data.
  

• Use AI as an assistant, not a replacement
  Keep human oversight central to DSAR handling, retaining validation before disclosing responses.
  Expert DPO input ensures these principles are applied consistently and defensibly.

Key takeaways

Data Subject Access Requests (DSARs) continue to grow in both volume and complexity, placing organisations under pressure to meet strict regulatory deadlines whilst maintaining compliance with data protection lawAny law, statute, declaration, decree, directive, legislative enactment, order, ordinance, regulation, rule or other binding restriction (as amended, consolidated or re-enacted from time to time) that relates to the protection of individuals with regards to the Processing of personal data.. AI can support these efforts by streamlining resource-intensive tasks, but it is not a complete answer and can come with risks. 

Organisations must carefully assess how these tools operate, understand their limitations, and choose vendors that can demonstrate transparency and reliability. AI works best as a support tool, with human oversight and judgement remaining essential to every stage of the DSAR process. 

By embedding strong governance and drawing on data protection expertise, organisations can leverage the benefits of AI whilst maintaining compliance and trust. 

If your organisation would benefit from support, our DSAR Response Service provides dedicated assistance to help you handle requests accurately, securely, and in line with data protection law. Contact us to find out how we can help.  

Register for our DSAR webinar mini-series

In case you missed it…

• AI Impact Assessments: What are they and why do you need one?
• Compliance with the AI Act Part 3: Who must comply and what are the obligations?
• ICO DSAR guidance: Preventing misunderstandings