It’s one of the difficult but true facts of innovation that as technology advances, so do the risks of using it.

Enhancing data collection and analysis, these tools also increase the possibility that personal data and sensitive information will appear what it doesn’t belong to.

The privacy risk, as a particular risk, is especially prevalent in the age of artificial intelligence (AI) for sensitive information is attained and utilized to create and refine AI and machine learning systems. With policymakers rushing to tackle the issue with privacy regulations around the use of AI, they create new compliance challenges for businesses that utilize AI technologies for decision-making.

Companies go on deploying AI models to improve productivity and unlock value. Let’s take a closer look at the AI privacy risks and safeguards impacting society and commerce nowadays.

What is AI privacy?

AI privacy is the practice of safeguarding personal or sensitive information obtained, applied, shared or saved by AI.

AI privacy is closely connected with data privacy which , also famous as information privacy, serves as the principle that a person should have control over their personal data. This control involves the ability to decide the way organizations collect, store and use their data. But the concept of data privacy predates AI and what people think of data privacy has changed with the advent of AI.

Understanding the privacy risks of AI

We can frequently trace AI privacy concerns to issues involving data collection, cybersecurity, model design and governance.       

Collection of sensitive data

One reason why AI arguably results in a greater data privacy risk than earlier technological advancements is the sheer volume of information in play. Terabytes or petabytes of text, images or video are routinely contained as training data, and inevitably some of that data is sensitive: healthcare information, personal data from social media sites, personal finance data, biometric data used for facial recognition and more. With more sensitive data gathered, saved and transmitted than ever before, greater chances are that at least some of it will be exposed or deployed in ways infringing on privacy rights.

Collection of data without consent

Controversy may ensue when data is procured for AI  development without being agreed by the people from whom it’s being collected. Under the circumstances of websites and platforms, users increasingly expect more autonomy over their own data and more transparency about data collection.

Use of data without permission

Even when data is collected with individuals’ agreements, privacy risks occur if the data is used for purposes beyond those initially disclosed. For instance, a former surgical patient reportedly discovered that photos related to her medical treatment had been used in an AI training dataset. The patient said that she had signed a consent form for her doctor to take the photos, but not for them to be included in a dataset.

Unchecked surveillance and bias

Privacy concerns linked to widespread and unchecked surveillance—whether through security cameras on public streets or tracking cookies on personal computers—surfaced well before the proliferation of AI. But AI can make these privacy concerns even more serious because AI models are used for the analysis of surveillance data. From time to time, the outcomes of such analysis can be destructive particularly when they demonstrate bias. For example, in the domains of law enforcement, a number of wrongful arrests of people of color have been linked to AI-powered decision-making.

Data exfiltration

AI models include a trove of sensitive data that can prove irresistible to attackers.  Bad actors can conduct such data exfiltration, namely data theft, from AI applications through various strategies. For instance, in prompt injection attacks, hackers conceal malicious inputs as legitimate prompts, manipulating generative AI systems into exposing sensitive data. For example, a hacker using the right prompt might trick an LLM-powered virtual assistant into forwarding private  documents.

Data leakage

As the accidental exposure of sensitive data, data leakage is exactly harmful to AI models. Risks exist for small, proprietary AI models as well. For example, consider a healthcare company building an in-house, AI-powered diagnostic app on the basis of its customers’ data. That app might unintentionally leak customers’ private information to other customers happening to utilize a particular prompt. Even such unintentional data sharing can lead to severe privacy breaches.