Artificial intelligence is forcing us to redefine one of the traditional pillars of data protection. According to a new forecast from Gartner, by 2029, most privacy incidents will no longer be caused by direct leaks of personal information, but by the inferences that AI systems can draw about individuals based on seemingly innocuous data. The consultancy believes that organizations will need to shift from merely protecting data to also governing the inferences generated by their AI models.
The key points of Gartner’s report in 20 seconds
- Gartner predicts that by 2029, most privacy incidents will be related to inferences made by AI.
- Models will be able to deduce sensitive information without direct access to identifiable personal data.
- The consultancy recommends incorporating AI governance into privacy strategies.
- Technologies such as synthetic data and differential privacy will become more important.
- Investment in data integrity will grow to match the funds allocated to confidentiality.
This warning reflects a shift that is starting to concern both technology leaders and compliance teams. For years, protecting privacy meant preventing unauthorized access to databases containing names, addresses, medical histories, or financial information. However, current models are capable of reconstructing personal profiles through correlations across multiple sources, even when the original data has been anonymized.
AI can discover information that was never stored
One of Gartner’s most interesting points is that it focuses on a different problem than traditional data leaks.
AI models no longer need direct access to specific data to obtain sensitive information. By combining behavioral patterns, consumption habits, location, digital activity, or statistical data, they can infer aspects such as potential illnesses, socioeconomic status, personal preferences, or future behaviors.
From a technical perspective, this represents a significant change because many of these conclusions do not exist beforehand in any database. They are generated by the model itself during the analysis process.
For Gartner, this evolution makes privacy a much more complex challenge than mere access control to information.
Privacy is no longer just a data protection issue
The firm argues that many organizations continue to handle privacy from a traditional confidentiality perspective.
Reducing stored data remains a good practice, but it is no longer enough, as AI can reconstruct sensitive information using public, anonymized, or aggregated data.
Therefore, Gartner proposes expanding the concept of privacy to what it calls governance of inferences, meaning controlling not only what data an AI system uses but also what kind of conclusions it can generate and how those conclusions are later used.
This approach becomes increasingly relevant as companies and authorities incorporate generative models, intelligent assistants, and autonomous agents into more critical processes.
Investment priorities will shift
The report also anticipates changes in how organizations will allocate their investments.
According to Gartner, by 2028, spending to protect data integrity will reach levels similar to those historically dedicated to confidentiality.
The reason is that the risks will no longer be limited to data theft. It will also be necessary to prevent incorrect profiles, algorithmic biases, wrong automated decisions, or unauthorized inferences.
In this context, data quality, context, and traceability will become as important as protecting data from unauthorized access.
Technologies to mitigate inference risks
To prepare for this new landscape, Gartner recommends implementing specific privacy protection tools throughout the AI system lifecycle.
Highlights include:
- Differential privacy, which adds statistical noise to hinder individual identification.
- Synthetic data, used for training models without exposing real information.
- Machine learning models with privacy protection, designed to minimize the possibility of reconstructing personal data.
The consultancy also advises integrating privacy by design principles into AI application development, regularly assessing potential biases, and maintaining human oversight for decisions that may affect individuals’ rights or freedoms.
A challenge for companies, developers, and regulators
Gartner’s forecast arrives as AI increasingly faces new regulatory requirements, especially in Europe with the progressive implementation of the AI Act alongside the General Data Protection Regulation (GDPR).
While both regulations already address transparency and personal data processing, the growth of models capable of inferring sensitive information presents new technical and legal challenges.
Gartner’s message is that privacy will no longer solely depend on the data that organizations store; it will also rely on what their algorithms can discover from that data, even if that information was never explicitly collected.
Frequently Asked Questions
What are AI-generated inferences?
They are conclusions derived by an AI model analyzing patterns and relationships in data, even when that information does not explicitly appear in databases.
Why does Gartner see them as an increasing risk?
Because models can deduce sensitive attributes about individuals without directly accessing identifiable personal data, making these incidents harder to detect.
What technologies does Gartner recommend?
The firm highlights the use of differential privacy, synthetic data, privacy-preserving machine learning, and enhanced governance over AI systems.
How will privacy strategies change?
Organizations will need to control not just what data they collect and store, but also what inferences their algorithms generate and how those inferences are used afterward.
Source: Open Security
