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The emergence of Generative AI has ushered in a new era of innovation in the ever-evolving technological landscape that pushes the boundaries of what machines can achieve by learning about content or objects from their input data and using it to generate brand-new, entirely original data.
McKinsey's latest research estimates that Generative AI’s impact on productivity could add $2.6 trillion to $4.4 trillion annually in value to the global economy. This phenomenal value represents industries harnessing the power of Generative AI across the board.
All this advancement is fueled by data, where organizations are accumulating massive amounts of data in the cloud to power hyperscale, cloud-native applications. By 2025, Gartner expects Generative AI to account for 10% of all data produced, up from less than 1% today.
As data grows in volume and Generative AI transforms how we approach innovation and problem-solving, it's essential to address a crucial aspect often overshadowed in the midst of marveling possibilities – data privacy and data privacy protection.
This guide explores the fascinating intersection of Generative AI and privacy protection, its challenges, and the safeguarding tips that can help organizations responsibly navigate these uncharted territories.
Although Generative AI promises remarkable advancements, it's not without its challenges. Privacy is one of the most significant concerns. When models are not trained with privacy-preserving algorithms, they are vulnerable to numerous privacy risks and attacks.
Generative AI generates new data, which is contextually similar to the training data, making it important to ensure that the training data does not contain sensitive information. However, the potential of inadvertently generating content that violates an individual’s personal information, particularly sensitive data, prevails as AI models learn from training data - enormous databases obtained from multiple sources containing personal data, often without the individual's explicit consent.
Large language models (LLMs), a subset of Generative AI, are trained on trillions of words across many natural-language tasks. Despite their success, studies suggest that these large models pose privacy risks by memorizing vast volumes of training data, including sensitive data, which may be exposed accidentally and used by attackers for malicious purposes.
The ability of LLMs to memorize and associate makes them produce results with near accuracy but a huge blow to privacy when sensitive data is exposed. The ability of LLMs to memorize personal data is referred to as memorization, and linking an individual’s personal data to its owner is referred to as association.
The uniqueness of Generative AI is resulting in new attack vectors that target sensitive data. Generative AI apps, including ChatGPT, and their increased acceptance have introduced several privacy concerns when certain prompts respond with information that includes sensitive data as a part of the responses.
Exfiltration attacks make matters worse. Research highlights how exfiltration attacks can be used to steal training data. For example, an unauthorized individual accesses the training dataset and steals, moves, or transfers data. Additionally, as models become more predictable, certain prompts can result in disclosing more data than originally intended, such as sensitive data.
Additionally, by integrating unvetted apps that use generative AI into critical business systems, organizations run the risk of compliance violations and data breaches, necessitating the need for periodic risk assessments, effective privacy protection measures, obtaining informed consent, and implementing data anonymization measures.
The rise of Generative AI has prompted an increased focus on the ethical and legal implications of using AI. Personal data handling must adhere to strict guidelines set forth by data privacy laws such as the General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) and AI-specific laws such as the EU’s Artificial Intelligence Act (EU AI Act).
Generative AI risks exposing an individual's identity through produced data, making it difficult to comply with laws governing the use of AI. Striking a balance between technological advancement and compliance begs the question: Will generative AI be a disruptive innovation benefiting users or be a cause of concern moving forward?
It’s no secret that we live in a post-GDPR era where countries worldwide are racing to enact their own data privacy legislation similar to obligations outlined in the EU’s GDPR. As such, consent is by far the most crucial aspect where models must obtain informed and explicit consent, ensure transparency of data processing activities, and honor data subject rights.
Additionally, AI-generated material can easily traverse national borders, creating disputes between various legal systems, intellectual property rules, and jurisdictional challenges. This would require SCCs and BCRs when AI content travels across borders. In addition, determining ownership and rights for AI-generated content can be confusing when the barrier between human and machine creation is blurred, causing a conflict of interest.
AI regulations and data protection regulations are growing globally. Here’s a list of AI-specific laws and regulations governing the safe use of Generative AI models:
The immense potential of Generative AI comes accompanied by complex implications, particularly regarding data privacy, ethics, and legal frameworks. Failure to ensure the privacy of sensitive data can have far-reaching effects. Apps that use Generative AI must abide by all applicable laws and regulations, especially in sectors such as healthcare, where a vast volume of sensitive data is involved.
Data breaches are increasing in both frequency and complexity, necessitating organizations to have a proactive approach to handling data securely. Such risks can have catastrophic consequences, ranging from financial loss and reputational damage to regulatory fines.
On July 27, South Korea’s Personal Information Protection Commission (PIPC) imposed a fine of 3.6 million won on OpenAI, the operator of ChatGPT, for exposing the personal data of 687 citizens of South Korea. Additionally, on March 20, 2023, ChatGPT encountered a glitch that enabled certain users to view brief descriptions of other users' conversations from the chat history sidebar, prompting the company to shut down the chatbot temporarily. The glitch potentially revealed the payment-related data of 1.2% of the ChatGPT Plus subscribers.
Protecting data in the era of Generative AI requires a multifaceted approach that balances innovation with privacy.
As Generative AI continues to evolve, privacy protection challenges will persist. The future of Generative AI will be defined by striking an effective balance between advancing technological limits and ensuring privacy protection.
It’s important to realize that data is a key input to Generative AI. Once sensitive data has been fed into the training model, the model can not unlearn it and allows malicious actors to employ various exfiltration techniques to expose that data.
Securiti Data Command Center can help implement a data controls strategy that enables you to ensure that model training data doesn't violate privacy requirements. It helps with:
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