I Asked the Authority on the Matter: ChatGPT Itself

Hardly a day goes by when a "serial entrepreneur" doesn't sell their AI start-up to a mega corporation. While some governments are pumping the brakes on AI use in the workplace, global regulators are tentatively issuing cautious guidelines on the adoption of generative AI for anything resembling real work. Much of the existing guidance boils down to a small set of principles that are baked into the artificial intelligence platforms' Terms of Service anyway:

  1. Feel free to use the tool, but avoid feeding it sensitive data, as large language models (LLM) don't operate in a vacuum. Even if they don't retain the data you input, they are a large stack of cloud services balancing themselves precariously across the various layers of the computing model, and those layers are often opaquely supplied by third-party companies 'facilitating' the service delivery.
  2. Aim for the enterprise version of the tool, as it's made for 'business' use, but do your due diligence: ensure that storage, retention, sharing and disposal are taken care of securely. How? That's up to you.
  3. Enjoy, but remember that all accountability and liability remains squarely in your court. Under no circumstances should you become dependent on this technology simply because it looks and feels indistinguishable from magic.

While that kind of authoritative guidance is enough to put one on their guard, it brings up even more questions for global decision makers. So armed with knowledge of recent bans and awareness of various cyber breaches that have plagued the exciting new technology, I went to the best source of information I could think of: ChatGPT 4.0.

What you're about to read, is pretty much our conversation exactly as it unfolded. The above principles still apply.

> Before we begin, how recent is the information you have access to?

My training data includes information up to December 2023, and I can access real-time data using the browser tool when needed to provide the most current information available. If you have any specific queries or need the latest updates on a particular topic, feel free to ask, and I can look up the most recent data for you!

> Which countries and organizations have banned Generative AI?

Several countries and organizations have imposed bans or restrictions on the use of AI tools like ChatGPT, primarily driven by concerns over security, compliance, misinformation, and the potential for misuse.

Countries with AI Bans:

A number of countries have implemented bans on AI platforms like ChatGPT:

China, Cuba, Iran, North Korea, Russia, and Syria have enforced nationwide bans, primarily due to concerns about internet censorship, control over information, and the potential for spreading misinformation​ (Tech.co)​​ (Open AI Master)​.

Italy has banned the use of ChatGPT citing data privacy concerns as it seeks to protect the personal information of its citizens​ (Open AI Master)​.

Organizational Bans:

On the organizational front, many are cautiously evaluating or outright banning the use of generative AI tools in the workplace due to various risks:

A survey by BlackBerry noted that a significant number of organizations are either considering or have already implemented bans on generative AI applications like ChatGPT. This is largely due to concerns about security threats to corporate IT environments​ (BlackBerry Blogs)​.

In the banking and finance sector, institutions like Bank of America, Citigroup, and Goldman Sachs have banned their employees from using generative AI platforms, driven by the heavily regulated nature of their industry and the risks associated with unvetted, third-party software​ (Tech.co)​.

According to a survey reported by Blind, sectors such as banking/finance and technology show substantial percentages of companies blocking the use of ChatGPT, with security, compliance risks, and job disruption being major motivating factors​ (33rd Square)​.

These measures reflect widespread caution as countries and organizations navigate the complex implications of rapidly advancing AI technologies. The focus is on balancing the potential benefits of AI with the need for responsible governance and risk management.

> Have Canada and Italy imposed bans on ChatGPT and CoPilot?

Italy has temporarily banned ChatGPT over concerns about privacy, particularly focusing on the mass collection and storage of personal data used to train the AI, and the lack of clarity about whose data is being collected. This action by the Italian Data Protection Authority highlights concerns under the European Union’s General Data Protection Regulation (GDPR). However, OpenAI has stated that they believe their practices align with GDPR and other privacy laws and that they are taking steps to protect users' data and privacy​ (Yahoo News - Latest News & Headlines)​​ (TechXlore)​.

As for Canada, there is no current ban on ChatGPT or GitHub Copilot. However, ChatGPT was under investigation by the Canadian privacy watchdog following concerns similar to those in Italy, focusing on data privacy practices​ (TechXlore)​.

In summary, Italy has imposed a temporary restriction on ChatGPT, but there is no similar ban in Canada, where the focus has been more on investigation rather than prohibition.

> Are ChatGPT and Microsoft's CoPilot banned in the US?

Yes, like GitHub Copilot, ChatGPT is also subject to trade restrictions and economic sanctions imposed by the United States. These restrictions generally affect how companies based in the U.S., such as OpenAI (the creator of ChatGPT), operate in certain regions. The specific countries impacted typically include those under U.S. sanctions, which can limit the availability of services like ChatGPT.

These sanctions aim to prevent businesses from providing services or conducting transactions with individuals, companies, and governments in those sanctioned countries. As a result, residents of countries such as Iran, North Korea, Syria, and others that face comprehensive U.S. sanctions might find access to ChatGPT blocked or severely restricted.

These measures are enforced to comply with U.S. law, and companies like OpenAI must adhere to these regulations to operate legally. The exact list of countries where ChatGPT is unavailable can change based on updates to U.S. trade policy, so it's always a good idea to check the latest information directly from OpenAI or relevant U.S. government resources.

> Have any other countries or regulators expressed concern and issued warnings over AI use in the workplace?

Several organizations, governments, and regulators have expressed concerns and issued warnings about the use of AI tools like ChatGPT and GitHub Copilot in the workplace, highlighting various risks associated with their deployment.

  1. European Union (EU) Regulations: The EU is actively working on comprehensive AI legislation that organizations must monitor to ensure compliance when using AI tools such as ChatGPT and GitHub Copilot. This legislation aims to address the wide-ranging implications of AI, including privacy, data protection, and ethical considerations​ (Tabnine)​.
  2. Specific Country Responses: Italy, for instance, has taken significant steps regarding the use of ChatGPT following concerns about data collection practices and privacy issues. This has included investigations and temporary bans as they assess the broader implications of AI technologies on privacy and data security​ (Brookings)​.
  3. Corporate Policies and Training: Companies are increasingly aware of the potential legal, privacy, and contractual risks associated with the use of generative AI. This has led some to develop specific policies governing the use of AI tools, train employees on their responsible use, and draft guidelines to mitigate risks like intellectual property disputes or breaches of confidential information​ (Debevoise)​.
  4. Risk Assessments by Legal and Compliance Leaders: Organizations are advised to evaluate multiple risks when integrating AI tools like ChatGPT into their operations. These include risks to quality control, potential violations of privacy laws, and the risk of unintentional sharing of sensitive or proprietary information​ (Debevoise)​.

These various warnings and the development of policies reflect a growing recognition of the complex challenges posed by AI tools in the workplace, emphasizing the need for robust oversight and regulation to ensure they are used safely and ethically.

> Given the recent ChatGPT data breach, are these concerns justified?

The recent data breach involving ChatGPT occurred due to a vulnerability in the Redis open-source library, which OpenAI uses to store user data. This breach, detected during a specific nine-hour window on March 20, 2024, led to the accidental exposure of sensitive information from approximately 1.2% of ChatGPT Plus subscribers who were active during that time. The exposed data included names, email addresses, payment addresses, types of credit cards, and the last four digits of credit card numbers. However, full credit card numbers were not exposed.

The vulnerability was identified in the Redis client library, redis-py, used with Asyncio, which created a situation where, under specific circumstances, canceled requests could leak data intended for one user to another. This issue was exacerbated by a change that increased the number of request cancellations, leading to a higher chance of data being exposed inadvertently. OpenAI has since patched the bug, notified affected users, and taken steps to ensure that such a breach does not happen again. They've also strengthened their systems to prevent similar vulnerabilities and launched a bug bounty program to incentivize the discovery and reporting of system weaknesses.

> What about the added risk of generative AI plug-ins?

In the context of ChatGPT, plugins can greatly expand the functionality of the AI by allowing it to interact with external data sources, software, and services. This could include fetching real-time data, accessing specific databases, or integrating with other software tools to enhance its responses. However, integrating plugins with ChatGPT introduces several risks and concerns:

Cybersecurity Risks

  1. Increased Attack Surface: Plugins can increase the number of entry points for attackers, especially if these plugins access external networks or handle data transactions.
  2. Dependency Risks: Relying on third-party plugins can introduce risks if those plugins are not regularly updated or are compromised. The security of the overall system becomes dependent on the security of each plugin.
  3. Integration Flaws: Poorly integrated plugins might create vulnerabilities in the system, such as improper data validations, which could be exploited to inject malicious code or data.

Privacy Concerns

  1. Data Exposure: Plugins that interact with external services or databases might expose sensitive data. Ensuring that all data exchanges comply with privacy laws and organizational policies is crucial.
  2. Compliance Challenges: When plugins are used to handle or store data, they must comply with relevant data protection regulations. Non-compliance due to a plugin’s operations could lead to legal issues and fines.
  3. Consent and Transparency Issues: Users must be aware of what data is being accessed and for what purpose. Plugins that obscure these details can undermine trust and potentially violate user consent agreements.

Best Practices for Secure Plugin Use

  1. Regular Audits and Updates: Ensure that all plugins are regularly reviewed and updated to mitigate vulnerabilities.
  2. Vet Third-Party Providers: Thoroughly evaluate the security practices of third-party plugin providers before integration.
  3. Implement Robust Access Controls: Restrict plugin access to the minimum necessary data and functions to perform its tasks.
  4. Transparency and User Consent: Clearly inform users about what data plugins will access and how it will be used, ensuring all data handling complies with legal requirements.

Using plugins with AI systems like ChatGPT can significantly enhance their capabilities but requires careful consideration of the associated risks and a proactive approach to managing those risks.

> I keep hearing about "closed" implementations of generative AI for organizations that have concerns around confidentiality and the protection of intellectual property. What is it?

A "closed" ChatGPT refers to a version of the ChatGPT AI model that operates within a private, controlled environment rather than being accessible on the public internet. This setup is often used by organizations that require enhanced security, privacy, and customization for their specific needs. Here are key aspects of what makes a ChatGPT "closed":

  1. Privacy and Security: In a closed system, ChatGPT can be configured to ensure that all data remains within the organization’s controlled environment. This is particularly important for handling sensitive or confidential information, complying with data protection regulations, and mitigating risks of data breaches.
  2. Customization: Closed versions of ChatGPT can be tailored to specific organizational needs. This includes training the model on specialized data to improve its relevance and accuracy for particular tasks or industries, and tweaking the model to align with internal policies and ethical guidelines.
  3. Integration and Control: Organizations can integrate a closed ChatGPT with their internal systems and databases without exposing these resources to external threats. They have full control over the AI’s deployment, including when and how it is updated or maintained.
  4. Compliance: A closed system can be specifically designed to comply with industry-specific regulations and standards, making it suitable for sectors like healthcare, finance, and legal, where compliance with stringent laws is necessary.

Organizations might choose a closed ChatGPT to benefit from the capabilities of AI while maintaining strict control over operational and security aspects, thereby enhancing trust and compliance in their use of AI technology.

> Can you tell me more about the risks with such solutions?

 Closed systems that use generative AI, such as individual instances of ChatGPT, present unique risks and challenges across several dimensions:

  1. Data Security and Privacy Risks: Closed AI systems often handle sensitive or proprietary information. If these systems are not properly secured, they could be vulnerable to data breaches. Moreover, the AI model could inadvertently memorize and reproduce sensitive data, potentially leading to privacy violations if the system outputs are not adequately controlled or sanitized.
  2. Bias and Fairness Concerns: Generative AI systems can perpetuate or even amplify biases present in their training data. In closed systems, where the AI might be trained on a specific set of corporate or localized data, the risk of developing biased outputs can be particularly high if the data set is not representative or contains historical biases.
  3.  Lack of Transparency: Closed systems are often black boxes with proprietary algorithms and data sets, making it difficult to audit or understand how decisions are made. This opacity can be problematic, especially in industries requiring compliance with regulatory standards that mandate transparency, such as finance and healthcare.
  4. Dependency and Lock-In: Relying on a proprietary, closed AI system can lead to vendor lock-in, where a user becomes dependent on a single provider for updates and maintenance. This can limit flexibility and increase costs over time, especially if the system does not interoperate well with other technologies.
  5. Error Propagation: AI systems, particularly those based on machine learning, can generate errors that are hard to predict or diagnose. In a closed system, these errors may propagate unchecked if there are not sufficient controls and monitoring in place. This is particularly critical in systems that perform automated decision-making.
  6. Misuse Potential: The powerful capabilities of generative AI can also be misused, such as creating deceptive content or manipulating information. In a closed system, if such capabilities are not strictly governed, it can lead to ethical issues or harm to individuals or groups affected by the AI's output.
  7. Maintenance and Update Challenges: AI models in closed systems require regular updates to stay effective and secure. However, updating these systems can be challenging due to the complexity of the models and the potential for new updates to introduce unexpected behaviors or dependencies.

Addressing these risks involves implementing robust data handling and security measures, maintaining transparency and accountability through thorough documentation and audits, actively mitigating bias, and ensuring that there are clear guidelines and controls over the use of AI outputs. It's also crucial for users of closed AI systems to stay informed about the potential limitations and risks associated with these technologies.

> Sounds like a lot of risks! How safe is "closed" ChatGPT vs regular ChatGPT?

When comparing the safety of a closed version of ChatGPT versus the standard, publicly available ChatGPT, several key factors influence their respective safety profiles:

Data Privacy and Security:

  • Closed ChatGPT: Typically, closed systems are customized for specific organizational use and can be configured to comply with internal data security protocols and privacy standards. These systems often operate within a controlled environment, limiting exposure to external threats. This setup can potentially offer higher levels of data privacy, especially if the system is isolated from the internet or external networks.
  • Regular ChatGPT: As a public-facing service, regular ChatGPT adheres to OpenAI’s privacy and security policies but handles a vast range of inputs from numerous users. While it is designed with security measures in place, the broad and varied use exposes it to more potential security risks and privacy concerns, especially regarding how data inputs and logs are managed.

Transparency and Control:

  • Closed ChatGPT: Organizations can tailor the AI’s functionality, training data, and usage to align with specific business needs and ethical guidelines. This level of customization allows for greater transparency within the organization regarding how the AI operates and decisions are made. However, it might still remain a black box to external stakeholders without specific disclosures.
  • Regular ChatGPT: While users of the standard ChatGPT benefit from OpenAI’s commitment to ethical AI practices, individual users have less control over the model’s training data and less insight into the decision-making process of the AI. OpenAI provides general information on data handling and system training, but detailed operations remain less transparent to end-users.

Compliance and Regulation:

  • Closed ChatGPT: Businesses can configure a closed system to comply with industry-specific regulations such as GDPR, HIPAA, or financial services regulations. This customization can make closed ChatGPT safer for regulated applications where adherence to strict legal and compliance standards is critical.
  • Regular ChatGPT: OpenAI endeavors to comply with general data protection laws and regulations, but users in heavily regulated industries may find the standard version less tailored to specific compliance needs.

Update and Maintenance:

  • Closed ChatGPT: The organization controls updates and maintenance schedules, potentially allowing for quicker response to identified threats or flaws. However, this depends on the organization's resources and commitment to maintaining the system.
  • Regular ChatGPT: OpenAI manages updates, improvements, and security patches, benefiting from a dedicated team focused on these aspects. However, users have to rely on OpenAI for timely updates and may not have immediate remedies for specific security or operational issues.
  • In summary, the safety of closed versus regular ChatGPT depends largely on the specific implementation, management, and operational context of the AI system. Closed systems offer benefits in terms of customization and privacy, tailored specifically to organizational needs, while the regular ChatGPT provided by OpenAI benefits from broad-scale security measures and ongoing updates from a dedicated team, albeit with less customization for individual user needs.

> What about the risks of plugins with the "closed" ChatGPT?

Integrating plugins with a closed version of ChatGPT involves a tailored approach where the plugins are also likely to be either custom developed or heavily customized to ensure they fit within the closed ecosystem's security and privacy frameworks. Here’s how plugins interact with a closed ChatGPT system and the specific considerations involved:

Cybersecurity Risks

  • Custom Integration: In a closed system, plugins need to be specifically designed or adapted to work within a secure environment. This reduces the risk of vulnerabilities common in more general plugins but requires thorough security vetting and testing.
  • Controlled Access: Access to external data or services via plugins in a closed system must be carefully managed and monitored to prevent data leaks and ensure that the integrity of the secure environment is maintained.
  • Secure Data Handling: Any data exchanged through plugins must be encrypted and handled according to the strict data policies that govern the closed system. This is crucial to prevent any unauthorized access or data breaches.

Privacy Concerns

  • Data Minimization: Plugins in a closed system should be designed to use only the data absolutely necessary for their function, adhering to privacy-by-design principles.
  • Clear Data Policies: There must be explicit policies in place regarding what data can be accessed by each plugin, how it is used, and who has the rights to view or modify this data.
  • Audit Trails: Maintaining detailed logs of how and when plugins access data can help monitor compliance with privacy policies and aid in forensic investigations if breaches occur.

Best Practices for Implementing Plugins in Closed Systems

  • Thorough Vetting Process: Before integration, plugins should undergo a rigorous security and compliance review to ensure they do not introduce vulnerabilities into the closed system.
  • Limited Connectivity: Plugins that require external connectivity should be limited to what is essential, and such connections should be secured and monitored continuously.
  • Regular Updates and Patch Management: Even in a closed system, plugins need regular updates to address new security threats. A process should be in place to test and deploy updates without disrupting the secure environment.
  • Training and Awareness: Users and administrators should be trained on the potential risks associated with plugins and the proper procedures for safely managing these tools.

Using plugins with a closed ChatGPT system can enhance functionality while maintaining the system's integrity and compliance. However, it requires careful planning and strict adherence to security and privacy standards to mitigate potential risks effectively.

> Ok, so how do I know what versions of ChatGPT are "closed" or "enterprise" or some other package?

OpenAI, the creator of ChatGPT, offers several packages and deployment options that can be adapted for "closed" implementations, catering to businesses and organizations with specific privacy and security requirements. Here are some of the key offerings:

ChatGPT Enterprise: This is a more secure and scalable version of ChatGPT designed for large businesses. It provides enhanced security features, greater control over data, and customization options that can align with specific business needs and compliance requirements.

ChatGPT API: While not a closed system by default, the API can be used to integrate ChatGPT's capabilities into private applications and systems. Organizations can build their own closed environments around the API to ensure that all interactions with ChatGPT remain within their controlled infrastructure.

On-Premises Solutions: For organizations needing the highest level of data control and security, deploying ChatGPT on-premises is a potential option. This involves setting up the necessary infrastructure within an organization's data center, allowing full control over data handling, security, and compliance. However, OpenAI does not directly advertise an on-premises version as a standard offering, so this would typically require a bespoke arrangement.

Fine-Tuning with Custom Data: Although not a package for closed implementation per se, OpenAI allows enterprises to train the model on custom datasets. This can be part of a more controlled, closed system where data privacy is critical, ensuring that all training and outputs are confined to secure environments.

Each of these options involves varying levels of customization and integration effort. Companies interested in these solutions generally need to work closely with OpenAI's sales and technical teams to design a system that meets their specific needs, especially when dealing with sensitive or regulated data.

For more detailed information on these offerings, contacting OpenAI directly or visiting their official website would provide the most accurate and tailored details according to specific business requirements.

> Given that there are risks regardless of implementation, would it make more sense for organizations to just use a "AI prompt engineer", or does it defeat the purpose of saving money?

The decision to use an "AI prompt engineer" versus fully automated AI systems like ChatGPT in organizations depends on several factors, including the specific needs of the organization, the complexity of tasks involved, and cost considerations.

Pros of Using AI Prompt Engineers

  • Improved Accuracy and Relevance: AI prompt engineers can help tailor AI responses to be more accurate and relevant to specific contexts. They understand how to craft prompts that guide the AI to produce the most useful and accurate outputs, thereby reducing errors and increasing efficiency.
  • Enhanced Data Security and Compliance: With a prompt engineer, there is better control over what data is input into the AI, which can help in adhering to data privacy laws and regulations. This is particularly important for organizations dealing with sensitive information.
  • Customization: AI prompt engineers can customize interactions based on the organization's specific requirements, something that a generalized AI might not handle as effectively without significant retraining or fine-tuning.

Cons of Using AI Prompt Engineers

  • Higher Costs: Employing skilled AI prompt engineers can be more expensive than using a generalized AI system. Salaries, training, and other employee-related expenses need to be considered.
  • Scalability Issues: While AI systems can handle thousands of tasks simultaneously, human prompt engineers have limitations in how much they can handle at once. This could be a bottleneck for organizations needing large-scale operations.
  • Dependence on Human Skill: The effectiveness of using AI prompt engineers depends heavily on their skill and experience. Inconsistent quality and human error are potential risks.

Cost Considerations

Using AI directly aims to reduce labor costs and increase efficiency. While AI prompt engineers can enhance the quality and relevance of AI outputs, they also add to the payroll and may slow down some processes due to the need for human intervention.

Whether it makes sense financially and operationally to employ AI prompt engineers should be evaluated based on:

  • The specific use cases within the organization.
  • The volume of tasks that require AI intervention.
  • The degree of accuracy and customization needed.
  • Compliance and security requirements.

For organizations where precision and customization in AI outputs are critical, and where data sensitivity is high, investing in AI prompt engineers could be worthwhile. However, for more general applications where scale and cost-efficiency are paramount, relying more heavily on automated AI systems might be more beneficial.

Ultimately, a blended approach where AI prompt engineers work in tandem with advanced AI systems could offer a balance between quality, compliance, and efficiency, allowing organizations to leverage the strengths of both human expertise and AI capabilities.

> Thanks for all that information, ChatGPT. Is there anything you would like to add for those conducting due diligence before adopting generative AI in the workplace?

When conducting due diligence before adopting generative AI in the workplace, organizations should consider several key areas to ensure they make informed, strategic decisions:

Understand the Technology: It's crucial for decision-makers to have a clear understanding of what generative AI can and cannot do. Understanding the technology's capabilities and limitations will help set realistic expectations and identify potential areas of impact.

  1. Assess Use Cases: Evaluate where generative AI can add the most value in your organization. This could be enhancing customer service, automating routine tasks, generating content, or aiding in decision-making processes. Identifying specific use cases will guide the implementation strategy and help measure the impact of AI integration.
  2. Legal and Ethical Considerations: Review the legal implications, especially regarding data privacy laws like GDPR, HIPAA, or others applicable in your jurisdiction. Ethical considerations should also be addressed, such as the potential for bias in AI outputs, the impact on employment, and ensuring transparency in AI-driven decisions.
  3. Security Assessment: Analyze the security aspects of implementing AI. This includes understanding data handling, storage, and processing practices of the AI solutions and assessing vulnerability to cyber threats.
  4. Vendor Evaluation: If you are partnering with AI service providers, thoroughly vet their credentials, security standards, compliance with laws, and their reputation in the market. It’s important to ensure that the vendor’s offerings align with your organizational needs and values.
  5. Cost Analysis: Consider the total cost of ownership, which includes not just the initial investment in technology but also ongoing costs for maintenance, updates, and potential scaling. Weigh these costs against the expected benefits and ROI.
  6. Skill and Readiness: Assess the current skill levels within your organization and determine what training or hiring might be necessary to effectively implement and use AI. Organizational readiness for adopting new technologies is critical for successful integration.
  7. Pilot Projects: Start with pilot projects before a full-scale rollout. This allows you to test the AI tools in controlled environments, gauge their effectiveness, and understand potential challenges without widespread disruption.
  8. Stakeholder Engagement: Engage various stakeholders, including IT, legal, operations, and end-users early in the process. Their insights can provide valuable perspectives on the practical aspects of AI deployment and help in smoothing the transition.
  9. Monitoring and Evaluation: Once implemented, continuously monitor the performance and impact of AI systems. Be prepared to make adjustments as needed and respond to new developments in AI technology and regulation.

By meticulously addressing these areas, organizations can better manage the risks and maximize the benefits of introducing generative AI into their operations. Effective due diligence will support a smoother integration of AI technologies and help in achieving strategic business objectives.


There you have it, folks. It all adds up to the two words that have served humanity well for millennia: caveat emptor.