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AI Data Privacy Deloitte Generative AI GPT Technology

Ethics and Tech: Data Privacy Concerns Around Generative AI

Ethics and Tech: Data Privacy Concerns Around Generative AI

The tech industry is embracing Generative AI, but the conversation around data privacy has become increasingly important. The recent “State of Ethics and Trust in Technology” report by Deloitte highlights the pressing ethical considerations that accompany the rapid adoption of these technologies. 30% of organizations have adjusted new AI projects, and 25% have modified existing ones in response to the AI Act, the report mentions.

The Rise of Generative AI

54% of professionals believe that generative AI poses the highest ethical risk among emerging technologies. Additionally, 40% of respondents identified data privacy as their top concern. 

Generative AI, which includes technologies like GPT-4, DALL-E, and other advanced machine learning models, has shown immense potential in creating content, automating tasks, and enhancing decision-making processes. 

These technologies can generate human-like text, create realistic images, and even compose music, making them valuable tools across industries such as healthcare, finance, marketing, and entertainment.

However, the capabilities of generative AI also raise significant data privacy concerns. As these models require vast amounts of data to train and improve, the risk of mishandling sensitive information increases. This has led to heightened scrutiny from both regulatory bodies and the public.

Key Data Privacy Concerns

Data Collection and Usage: Generative AI systems often rely on large datasets that may include personal and sensitive information. The collection, storage, and usage of this data must comply with stringent privacy regulations such as GDPR and CCPA. Organizations must ensure that data is anonymized and used ethically to prevent misuse.

Transparency and Accountability: One of the major concerns is the lack of transparency in how generative AI models operate. Users and stakeholders need to understand how their data is being used and the decisions being made by these systems. Establishing clear accountability mechanisms is crucial to build trust and ensure ethical use.

Bias and Discrimination: Generative AI models can inadvertently perpetuate biases present in the training data. This can lead to discriminatory outcomes, particularly in sensitive areas like hiring, lending, and law enforcement. Addressing these biases requires continuous monitoring and updating of the models to ensure fairness and equity.

Security Risks: The integration of generative AI into various systems can introduce new security vulnerabilities. Cyberattacks targeting AI systems can lead to data breaches, exposing sensitive information. Robust security measures and regular audits are essential to safeguard against such threats.

Ethical Considerations and Trust

80% of respondents are required to complete mandatory technology ethics training, marking a 7% increase since 2022.  Nearly three-quarters of IT and business professionals rank data privacy among their top three ethical concerns related to generative AI:

  • Developing and implementing ethical frameworks for AI usage is crucial. These frameworks should outline principles for data privacy, transparency, and accountability, guiding organizations in the responsible deployment of generative AI.
  • Engaging with stakeholders, including employees, customers, and regulatory bodies, is essential to build trust. Open dialogues about the benefits and risks of generative AI can help in addressing concerns and fostering a culture of transparency.
  • The dynamic nature of AI technologies necessitates continuous monitoring and improvement. Regular assessments of AI systems for biases, security vulnerabilities, and compliance with privacy regulations are vital to ensure ethical use.

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OpenAI
AI Community Custom GPT Cyber Security Generative Pre-trained Transformers GPT OpenAI

Custom GPTs Might Coarse Users into Giving up Their Data


In a recent study by Northwestern University, researchers uncovered a startling vulnerability in customized Generative Pre-trained Transformers (GPTs). While these GPTs can be tailored for a wide range of applications, they are also vulnerable to rapid injection attacks, which can divulge confidential data.

GPTs are advanced AI chatbots that can be customized by OpenAI’s ChatGPT users. They utilize the Large Language Model (LLM) at the heart of ChatGPT, GPT-4 Turbo, but are augmented with more, special components that impact their user interface, such as customized datasets, prompts, and processing instructions, enabling them to perform a variety of specialized tasks.

However, the parameters and sensitive data that a user might use to customize the GPT could be left vulnerable to a third party. 

For instance, Decrypt used a simple prompt hacking technique—asking for the "initial prompt" of a custom, publicly shared GPT— to access the entire prompt and confidential data of a custom.

In their study, the researchers tested over 200 custom GPTs wherein the high risk of such attacks was revealed. These jailbreaks might also result in the extraction of initial prompts and unauthorized access to uploaded files.

The researchers further highlighted the risks of these assaults since they jeopardize both user privacy and the integrity of intellectual property. 

“The study revealed that for file leakage, the act of asking for GPT’s instructions could lead to file disclosure,” the researchers found. 

Moreover, the researchers revealed that attackers can cause two types of disclosures: “system prompt extraction” and “file leakage.” While the first tricks the model into sharing basic configuration and prompts, the second coerces the model into revealing its confidential training datasets. 

The researchers further note that the existing defences, like defensive prompts, prove insufficient in front of the sophisticated adversarial prompts. The team said that this will require a more ‘robust and comprehensive approach’ to protect the new AI models. 

“Attackers with sufficient determination and creativity are very likely to find and exploit vulnerabilities, suggesting that current defensive strategies may be insufficient,” the report further read.  "To address these issues, additional safeguards, beyond the scope of simple defensive prompts, are required to bolster the security of custom GPTs against such exploitation techniques." The study prompted the broader AI community to opt for more robust security measures.

Although there is much potential for customization of GPTs, this study is an important reminder of the security risks involved. AI developments must not jeopardize user privacy and security. For now, it is advisable for users to keep the most important or sensitive GPTs to themselves, or at least not train them with their sensitive data.

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