Navigating Generative AI Privacy : Challenges & Safeguarding Tips

Introduction

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.

Privacy Concerns in the Age of Generative AI

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.

Legal and Ethical Considerations

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 Rising Call for Data Privacy in Generative AI

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.

Safeguarding Tips for Data Privacy Protection

Protecting data in the era of Generative AI requires a multifaceted approach that balances innovation with privacy.

• Ensuring Regulatory Compliance: Generative AI’s regulatory landscape varies on jurisdiction. In the EU, the GDPR establishes stringent regulations on the handling of personal data, including information produced or handled by AI systems. Organizations  utilizing generative AI in the EU must follow the GDPR's guiding principles, which include data minimization, consent, and the right to explanation. Additionally, Article 22 of the GDPR states that the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her. In the US, the CPRA grants individuals the right to opt-out of automated decision-making where Californians can refuse to have their personal data and sensitive personal data used to make automated conclusions, such as profiling for targeted behavioral advertising. In addition, Californians also have the right to know about automated decision-making, where they can ask for information on how automated decision technologies work and their likely outcomes. Under Canada’s Artificial Intelligence and Data Act (AIDA), if a person is responsible for a high-impact system, then they must, in accordance with the regulations, establish measures to identify, assess, and mitigate risks of harm or biased output that could result from the use of the system.
• User Consent and Transparency: Where necessary, obtain the user's explicit consent before using their data for generative AI purposes. Provide data subjects the right to opt-out of their personal data being used by AI systems (or to opt-in or withdraw consent) when collecting their personal data. Ensure transparency by informing users of the intended use of their data and the security measures in place to ensure the privacy and security of their data, along with the source of the training data.
• Data Minimization: Only obtain and retain the minimum data absolutely necessary for AI training purposes. Limiting the amount of sensitive data reduces the potential risks associated with data breaches or inadvertent sensitive data exposure.
• Classify AI Systems and Assess Risks: Discover and make an inventory of all AI models in use. Assess the risks of your AI model at the pre-development, development, and post-development phases and document mitigations to the risks. You must also classify your AI system, do bias analysis, etc.
• Anonymization and De-Identification: Apply strong anonymization techniques to eliminate personal identifiers from the data before feeding it to generative models. Differential privacy is a well-established notion of privacy that offers strong guarantees of the privacy of individual records in the training dataset. This stops AI-generated material from exposing sensitive data about specific people.
• Secure Data Storage and Transfer: Ensure to employ encryption techniques and proper safeguards to store the data needed to train and improve generative models. Use encrypted channels to move data across systems to prevent unauthorized access.
• Access Control: Implement strict access controls and enforce a least privileged access model to limit who can access and utilize generative AI models and the data they generate. Role-based access ensures that only authorized individuals can interact with sensitive data.
• Ethical Review: Establish an ethical review procedure to evaluate the potential impacts of content produced by AI. This assessment should concentrate on privacy concerns to ensure that the material complies with ethical standards and data protection laws.
• Publish Privacy Notices: Develop and publish comprehensive data governance policies that outline how data is collected, used, stored, and disposed of, along with explanations of what factors will be used in automated decision-making, the logic involved, and the rights available to data subjects.
• Transparent AI Algorithms: Utilize transparent and comprehensible generative AI algorithms. This enables discovering how the model produces material and locating any potential privacy issues. Introduce a module to detect the presence of sensitive data in the output text. If detected, the model should decline to answer or mask any sensitive data that has been detected.
• Regular Auditing: Conduct regular audits to monitor AI-generated content for privacy risks. Implement mechanisms to identify and address any instances where sensitive data might be exposed.

Generative AI Privacy Requires DataCommand Center

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:

• A comprehensive inventory of data that exists;
• Contextual data classification to identify sensitive data/confidential data;
• Compliance with regulations that apply to the data fed to the training model, including meeting data consent, residency, and retention requirements;
• Inventory of all AI models to which data is being fed via various data pipelines;
• Governance of entitlements to data through granular access controls, dynamic masking, or differential privacy techniques; and
• Enabling data security posture management to ensure data stays secure at all times.

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