Most privacy laws and regulations do not yet directly address AI and how it can be used or how data can be used in AI models. As a result, AI companies have had enormous freedom to do what they want. This lack of regulation has led to ethical dilemmas like stolen IP,
AI privacy concerns are growing as artificial intelligence becomes more widespread, raising issues about how data is collected, used, and protected. While AI can increase efficiency and improve workflows, it also introduces risks such as data security and illegal use. Many individuals are deeply troubled about having their information harvested to train AI models without their consent.
Understanding how AI uses data is essential for protecting customers and their personal information. This involves learning about common privacy risks, AI data collection methods, and best practices to mitigate these risks.
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The collection and use of personal data for AI has grown significantly. From social media interactions to smart gadgets, AI systems collect massive amounts of data to improve user experiences, personalize content, and drive targeted advertising. Clearly, this vast data collection poses substantial privacy risks. Understanding how AI collects and processes your data can help you navigate the digital landscape more securely and make better decisions about your online activity.
Given that the role of artificial intelligence has grown so rapidly, it’s not surprising that issues like unauthorized incorporation of user data, unclear data policies, and limited regulatory safeguards have created significant issues with AI and privacy.
When users of AI models input their own data in the form of queries, it’s possible that this data will become part of the model’s future training dataset. When this happens, this data can show up as outputs for other users’ queries, which is a particularly concerning issue if users have input sensitive data into the system.
Many personal devices use facial recognition, fingerprints, voice recognition, and other biometric data security instead of more traditional forms of identity verification. Public surveillance devices are also beginning to use AI to scan for biometric data to identify individuals quickly. Many individuals are unaware that their biometric data is actively collected, much less that it is stored and used for other purposes.
When a user interacts with an ad, a TikTok or other social media video, or most any major web property, metadata from that interaction—as well as the person’s search history and interests—can be stored for more precise content targeting in the future. While most user sites have policies that mention these data collection practices and/or require users to opt in, it’s mentioned only briefly and amid other policy texts, so most users don’t realize the guidelines to which they’ve agreed.
While some AI vendors may choose to build baseline cybersecurity features and protections into their models, many AI models do not have native cybersecurity safeguards in place. Even AI technologies with basic safeguards rarely come with comprehensive cybersecurity protections. This is because creating a safer and more secure model can cost AI developers significantly in terms of time to market and overall development budget.
Many AI vendors lack clarity about how long, where, and why they preserve user data, whether they’re keeping it for an extended period or using it in ways that do not prioritize privacy. OpenAI’s privacy policy, for example, permits various vendors, such as cloud and online analytics providers, to access and handle user data as requested. It also includes unidentified parties under a broad “among others” category, raising data treatment concerns. While OpenAI allows users to access, remove, or restrict data processing, privacy restrictions are limited, particularly for Free and Plus plan users, leaving certain data usage practices unclear.
AI models frequently employ training data gathered from across the internet, which may contain copyrighted content, and some AI companies either ignore or neglect copyright and intellectual property rules in the process. AI image generation platforms such as Stability AI, Midjourney, DeviantArt, and Runway AI have faced legal issues for harvesting copyrighted images from the web without the authorization of the original creators. Some providers claim that existing regulations do not expressly prohibit this method for AI training. As AI models improve, prohibiting the use of copyrighted content may become more common, making it increasingly difficult for developers to trace the origins and licenses of the data their models use.
Some countries and regulatory bodies are working on AI regulations and safe use policies, but there are no overarching official standards to hold AI vendors accountable for how they build and use artificial intelligence tools. The most privacy-centric regulation is the EU AI Act, which entered into force on August 1, 2024. Some aspects of the law will take as long as three years to become enforceable. With such limited regulation, some AI vendors have come under fire for IP violations and opaque training and data collection processes, but little has come from these allegations. In most cases, AI vendors decide their own data storage, cybersecurity, and user rules without interference.
Unfortunately, the total number and variety of ways that data is collected all but ensures that it will find its way into some irresponsible uses. From Web scraping to biometric technology to IoT sensors, modern life is essentially lived in the service of data collection efforts.
Because the AI landscape is evolving so rapidly, the emerging trends shaping AI and privacy issues are also changing at a remarkable pace. Among the leading trends are major advances in AI technology itself, the rise of regulations, and the role of public opinion in AI’s growth.
AI software has exploded in terms of technological sophistication, use cases, and public interest and knowledge. This growth has happened with more traditional AI, machine learning technologies, and generative AI. Generative AI’s large language models (LLMs) and other massive-scale AI technologies are trained on incredibly large datasets, including internet data and also more private or proprietary datasets. While the data collection and training methodologies have improved, AI vendors and their models are often not transparent in their training or the algorithmic processes they use to generate answers.
Many generative AI companies have updated their privacy policies and data collection and storage standards to address this issue. Others, such as Anthropic and Google, have worked to develop and release detailed research that illustrates how they are working to incorporate more explainable AI practices into their AI models, which improves transparency and ethical data usage.
Most privacy laws and regulations do not yet directly address AI and how it can be used or how data can be used in AI models. As a result, AI companies have had enormous freedom to do what they want. This lack of regulation has led to ethical dilemmas like stolen IP, deepfakes, sensitive data exposed in breaches or training datasets, and AI models that seem to act on hidden biases or malicious intent.
More regulatory bodies—both governmental and industry-specific—are recognizing the threat AI poses and developing privacy laws and regulations that directly address AI issues. Expect more regional, industry-specific, and company-specific regulations to come into play in the coming months and years, with many of them following the EU AI Act as a blueprint for protecting consumer privacy.
Since ChatGPT was released, the general public has developed a basic knowledge of and interest in AI technologies. Despite the excitement, the general public’s perception of AI technology is fearful, especially as it relates to AI privacy. Many consumers do not trust the motivations of big AI and tech companies and worry that their personal data and privacy will be compromised by the technology. Frequent mergers, acquisitions, and partnerships in this space can lead to emerging monopolies and the fear of the power these organizations have.
According to a survey completed by the International Association of Privacy Professionals in 2023, 57 percent of consumers fear that AI is a significant threat to their privacy, while 27 percent felt neutral about AI and privacy issues. Only 12 percent disagreed that AI would significantly harm their personal privacy.
While several significant and highly publicized security breaches involving AI technology have occurred, many vendors and industries are making important strides toward better data protection. The following examples cover both failures and successes.
The following are some of the most significant breaches and privacy violations that directly involved AI technology over the past several years:
Many AI companies are innovating to create privacy-by-design AI technologies that benefit both businesses and consumers, including the following:
As AI continues to integrate into more facets of our lives, concerns about privacy and consumer rights have grown significantly. The large volumes of data required to train and operate AI systems raise serious concerns about how this information is acquired, used, and secured. Transparency, informed consent, and data protection are necessary to sustain customer trust and privacy standards.
Customers have special rights over their data, such as the ability to access, update, delete, and object to its processing. Global regulatory measures aim to reconcile the revolutionary promise of AI with the necessity to preserve human privacy and consumer rights.
AI systems rely on large volumes of data, including personal information, to perform optimally. These systems’ constant need for more data raises a number of key considerations concerning data collection, use, and protection:
The growth of AI has made consumer rights more urgent. With AI systems using personal data, users must maintain control over their information. Key rights include the following:
While AI presents an array of challenging privacy issues, companies can overcome these concerns by using the following best practices for data governance, establishing appropriate data use policies, and educating all stakeholders:
AI privacy solutions and technologies assist businesses in safeguarding sensitive data from potential vulnerabilities and threats. We recommend three tools that perform penetration testing, enforce policies, monitor, and provide strong data protection: Nightfall AI, Private AI, and Troj.AI.
Nightfall AI provides a complete, AI-native data security platform that protects sensitive data across many settings, including SaaS apps, GenAI tools, email, and endpoints. Its software employs advanced AI algorithms to identify and secure passwords, personally identifiable information (PII), and protected health information (PHI) with high precision. Nightfall is fully integrated with enterprise applications, offering real-time monitoring and policy enforcement to avoid data leaks and demonstrate compliance. Nightfall AI’s pricing depends on how much data a company needs to secure. The company offers a free plan allowing users to scan 30 GB of data monthly.
Private AI focuses on privacy-preserving technology, including its AI-powered redaction service. This service detects and eliminates sensitive material from text, images, and other data types, guaranteeing compliance with privacy laws such as GDPR and CCPA. Its solutions integrate into existing workflows, delivering powerful data protection without compromising usability or efficiency. Private AI offers free API keys for users to test their services, but they are limited to 75 API calls per day. The company’s paid plan pricing is available upon request.
TrojAI specializes in protecting AI and machine learning systems from attacks. Its software offers comprehensive security through transformation, model monitoring, and real-time threat detection. TrojAI provides penetration testing for AI models, a firewall for AI applications, and compliance features to help protect against data poisoning and model evasion assaults. Its technologies are designed to improve the security and dependability of AI systems in enterprise environments. Troj pricing is available upon request; each business is different and will require personalized quotations.
AI tools present businesses and consumers with many new conveniences, from task automation and guided Q&A to product design and programming. While these tools can improve users’ lives, they also risk violating individual privacy in ways that damage vendor reputation and consumer trust, cybersecurity, and regulatory compliance efforts.
Using AI responsibly to protect user privacy requires extra effort, yet it’s essential when you consider how privacy violations can impact a company’s public image. Especially as this technology matures and becomes more pervasive in users’ lives, it’s essential to follow AI regulations and develop specific AI best practices that align with your organization’s culture and customers’ privacy expectations.
For more information on how to address these AI issues, read our article on AI and ethics.
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