How Does AI Impact Data Privacy?

Artificial Intelligence (AI) has become an important part of our digital world. Organisations use AI for many purposes, for example, customer service, recruitment, healthcare, banking, marketing, fraud detection, content creation and business decisions. As generative AI tools become more widely used, AI is now easily available to businesses, employees, and consumers.

However, the increasing use of AI also creates important data privacy and protection concerns. AI systems often need large amounts of data, including personal information, to train models, make predictions, provide personalised services and support automated decisions. Organisations therefore need to be careful about how they collect, use, store, share and analyse personal data through AI systems. AI may also use this data to generate new information or make conclusions about individuals.

The impact of AI on data privacy spans the entire AI lifecycle. This includes collecting and preparing data, developing and training AI models, using AI systems, monitoring their performance, and finally storing or deleting the data.

1. AI Increases the Amount and Complexity of Data Processing.

Traditional computer systems usually process data for specific and limited purposes. AI systems, however, can process large amounts of data and identify patterns and connections between different types of information.

For example, a company may use customer details, purchase history, website activity and customer service records to build an AI-based recommendation system. Each piece of information may seem harmless on its own. However, when AI combines and analyses all this information, it may create a much more detailed picture of a person.

This creates an important privacy concern. The more data an AI system collects and processes, the higher the risk that the data may be misused, exposed, or kept longer than necessary.

The NIST Privacy Framework recognises privacy as an important business risk. It helps organisations identify, assess and manage the privacy risks that can arise when personal data is collected and processed.

2. Personal Data Can Be Used as AI Training Data

One of the main data privacy concerns with AI is how personal information is used to train and improve AI systems.

Organisations often have large amounts of information, such as customer records, employee details, contracts, emails, and other business documents. When this information is used to train, improve or support an AI system, organisations need to consider data protection requirements.

They should ask questions such as:

• Where did the data come from?
• Was the data originally collected for this purpose?
• What legal basis allows the organisation to use it?
• Is the use consistent with the original purpose?
• Is personal data actually needed to train the AI system?
• Can the data be anonymised or reduced to only what is necessary?
• How long should the data be kept?
• What privacy rights do individuals have?

The Information Commissioner’s Office (ICO) explains that data protection laws apply when AI systems process personal data. Organisations should therefore take suitable technical and organisational measures to identify, manage and reduce privacy risks.

3. AI Can Create New Privacy Risks Through Inference

AI does not only use the information that an organisation already has. It can also analyse data to make predictions or draw conclusions about people.

For example, an AI system may look at a person’s purchases, online activities or behaviour and use this information to predict their interests, preferences, financial situation or other personal details.

This creates an important privacy concern because there is a difference between collecting information and generating new information through AI.

An organisation may not have directly collected certain information about a person. However, AI may be able to predict or derive that information by analysing other data. Therefore, organisations need to consider not only what information they collect, but also what AI can learn, predict or infer from that information.

NIST has also highlighted privacy risks related to AI, including the ability of AI systems to estimate personal information, reconstruct information from data and determine whether information about a person is present in a dataset.

4. Generative AI Creates Privacy Risks Through Prompts and Outputs

Generative AI can create privacy risks based on the information users enter into AI tools. Employees may unknowingly share customer details, employee records, contracts, confidential business information or personal data while using AI to summarise documents, analyse information or draft content.

This raises questions such as:

• Where is the information processed and stored?
• Is it used to train or improve the AI model?
• Who can access it?
• Can the information be deleted?
• Does the organisation have an agreement with the AI provider?

The risk is higher when employees use unapproved public AI tools, often referred to as “shadow AI.”

Organisations should reduce these risks through clear AI usage policies, approved AI tools, employee training and technical controls to prevent inappropriate sharing of personal or confidential information.

5. AI Can Affect Individual Privacy Rights

Data protection laws give people several rights over their personal information. These may include the right to access, correct or delete their data, object to its use, and in some cases, request data portability or restrict certain types of processing.

AI can make it harder for organisations to protect and respond to these rights. For example, when personal data is used to train an AI or machine-learning model, it may be difficult to identify where the data is stored or understand how it has influenced the model.

AI can also produce incorrect information or predictions about a person. Organisations should therefore have processes to identify and correct errors and address any harm caused by incorrect AI results.

The ICO’s AI guidance also highlights the importance of protecting individual rights when AI systems process personal data.

6. Automated Decision-Making and Profiling

AI is increasingly being used to analyse information and make or support decisions about people. For example, it may be used for:

• Screening job applicants
• Assessing loan or insurance applications
• Detecting fraud
• Showing personalised advertisements
• Assessing customer risk
• Providing healthcare recommendations

These uses can create privacy and fairness concerns, especially when people do not understand how a decision was made or cannot easily challenge it.

Organisations should therefore focus on transparency, fairness, explainability and human oversight when using AI for decisions that may significantly affect individuals.

Responsible AI governance should protect personal data while also considering how AI decisions can impact people.

7. AI and Data Minimisation

Data minimisation is an important principle of data protection. It means that organisations should collect and use only the personal data they actually need for a specific purpose.

However, collecting too much personal information, especially when it may not be needed, can increase privacy risks. Therefore, organisations should carefully consider what data is necessary before using it in AI systems.

Organisations should therefore ask:

What data does the AI system actually need?

Where possible, organisations can reduce privacy risks by using methods such as anonymisation, pseudonymisation, synthetic data and data aggregation. These methods help reduce the amount of personal information that needs to be collected or used.

The goal should be to move away from collecting as much data as possible and instead focus on using only the data that is necessary, relevant and appropriate for a specific purpose.

8. Privacy by Design Becomes Essential

Consider privacy from the start of an AI project, not only through policies or legal agreements.

Organisations should manage privacy at every stage:

Measures such as access controls, encryption, data masking, anonymisation and data retention limits can help protect personal data.

The NIST AI Risk Management Framework also recommends identifying and managing privacy risks throughout the AI lifecycle, rather than after deployment.

9. AI Vendor and Third-Party Risk

Many organisations use external AI providers, cloud services and AI-based software instead of building their own systems. This can create privacy risks when personal or confidential data is shared with third parties.

Before using an AI vendor, organisations should check:

• Whether customer data is used to train AI models.
• Who can access prompts and outputs.
• Whether data is shared with other providers.
• Whether data is transferred to other countries.
• What security measures are in place.
• Whether data can be deleted when required.

Contracts should clearly define how data can be used, security requirements, retention periods and each party’s responsibilities.

10. AI Can Also Improve Data Privacy

While AI can create data privacy risks, it can also help organisations improve their privacy practices.

Organisations can use AI to:

• Find and identify personal data.
• Classify and organise documents.
• Identify potential privacy risks.
• Review contracts and privacy clauses.
• Support Data Subject Access Requests (DSARs).
• Identify unnecessary data collection.
• Track changes in privacy laws and regulations.

Conclusion

AI is changing the way organisations manage data privacy. The focus is no longer only on protecting stored data, but also on understanding how AI collects, uses, processes and makes decisions using data.

Organisations should take a risk-based approach by using data minimisation, privacy by design, strong security, AI governance, vendor checks and regular monitoring. Frameworks such as the NIST AI Risk Management Framework and Privacy Framework can help manage these risks.

The goal is not to stop AI innovation, but to make sure AI is used responsibly and safely, with privacy built in from the start. As AI becomes more widely used, privacy will be an important part of AI governance, trust and compliance.