Protecting Data Privacy with OpenAI

Introduction

A recent study by GVR, estimates a global AI market of $196.6 billion in 2023, expected to grow at a “compound annual growth rate of 37.3% from 2023 to 2030 to reach USD 1,811.8 billion by 2030.” Valued at $29B after an investment from Microsoft, ChatGPT is a Large Language Model (LLM) that comes in both free and paid versions.

As artificial intelligence (AI) becomes increasingly sophisticated, businesses are looking for ways to leverage its power to speed up processes and gain a competitive edge without sacrificing service or privacy. This article will discuss the data privacy problem and how to solve it.

Data Privacy Problem

One of the main concerns with LLMs such as ChatGPT is the potential for the tool to access and misuse confidential data. Case in point, if a business uploads customer emails or chat logs for insights and analysis, the tool has access personal information such as names, addresses, emails, and phone numbers. While some of this information is already public, sensitive data like social security numbers, compensation, or health information could be easily misused if it falls into the wrong hands.

The problem is compounded for companies dealing with a variety of consultants who have access to protected data. For example, LLMs can already scan large batches of resumes and instantly identify key skills, qualifications, tenure, and experience. They easily identify gaps in experience that humans can miss and they make recommendations for follow-up steps.

As powerful as this technology is, it creates serious privacy concerns for businesses. Inputting resumes into a free LLM saves time, but after leaving the protected walls of an organization's server it could theoretically go anywhere, violating client and customer confidentiality and trust.

There is also no substitute for human intelligence. An unchecked algorithm is even more likely to suffer from confirmation bias. In their current form, they tend to amplify and confirm pre-existing beliefs (or data), rather than seeking out new information. Thus, in the hands of an organization with skewed data they can actually amplify bias. Humans can address this by carefully curating training data that encourage the model to consider alternative viewpoints and challenge its own assumptions.

Solution

Data privacy is of paramount concern, but there are already solutions to mitigate these risks. The most obvious is to use an API to access OpenAI and create your own gated GPT to serve the exact needs of your organization.

When using an API, communication happens through a secure connection that is encrypted using SSL or TLS cryptographic protocols. This ensures that any sensitive data is encrypted and cannot be intercepted by third parties.

Further, a company has total control over security measures, as well as customer and client information. Developers can ensure data is only accessed by authorized users, and that access to that data is logged and audited. In contrast, 'freeware' cloud-based platforms do not have the same level of security.

OpenAI API Resource Guide:

If your company is interested in OpenAI but concerned about privacy, here is a guide to support your journey:?

1. Sign up for an OpenAI API key:

• To access the GPT models, you need to sign up for an API key from OpenAI.
• You can do this by visiting the OpenAI Pricing page (https://openai.com/pricing) and choosing the plan that fits your budget and usage needs.
• The plans range from a free trial to enterprise-level options, with costs varying depending on the number of requests and the GPT model you choose.
• Note: It can take anywhere from days to weeks to approve your API request. Move on this first.

2. Follow the QuickStart guide to build your application:

• Once you have your API key, you can start building using the QuickStart guide?(https://platform.openai.com/docs/quickstart/build-your-application) or rely on your own internal IT team.
• These step-by-step instructions for integrating OpenAI into your applications are a helpful resource. The knowledgebase has additional guides and relevant support.

3. Choose the appropriate GPT model for your needs:

• OpenAI provides several GPT models with varying capabilities. Pricing can help you determine the right fit for your needs.
• Pricing is dependent on A) the number of requests made to the system per month and B) the number of tokens used per request.
• Tokens are purchased in quantities of 1,000.

Breakdown of Models and Prices:

Ada: This model is the fastest option and is priced at $0.0004 per 1,000 tokens.

• 750 words = $0.30.
• Ideal for: individuals, small businesses, or startups with limited budgets and a need for quick and simple language processing.

Babbage: This model is slightly more expensive than Ada but offers more capabilities. Babbage is priced at $0.0005 per 1,000 tokens.

• 750 words = $0.38. Ideal for: small to medium-sized businesses or research teams with more complex language processing needs and a slightly larger budget.
• This model is well-suited for businesses that need to generate text for marketing campaigns, customer support, or content creation.

Curie: This model is more expensive than both Ada and Babbage but offers even more advanced language processing capabilities. Curie is priced at $0.0020 per 1,000 tokens.

• 750 words = $1.50.
• Ideal for: larger businesses or research teams with more complex language processing needs and a larger budget.
• This model is well-suited for businesses that need to generate text for more technical applications, such as medical research, legal documentation, or financial analysis.

ChatGPT3.5 Turbo: This is a more recent addition to OpenAI's suite of language models and offers improved speed and accuracy over the earlier versions.

• It is priced at $0.0060 per 1,000 tokens. 750 words = $4.50.
• Ideal for: businesses or research teams that require a balance between speed and accuracy for language processing tasks, such as chatbots, customer support, or content creation.

ChatGPT4.0: This is the most advanced language model offered by OpenAI and provides state-of-the-art natural language processing capabilities.

• It is priced at $0.0600 per 1,000 tokens. 750 words = $45.00.
• Ideal for: businesses or research teams that require the highest level of accuracy and sophistication in their language processing tasks.
• Examples: language translation, sentiment analysis, or complex document generation. This model is well-suited for enterprises with significant resources and a need for the most advanced language processing capabilities available.

Conclusion: Protecting Data Privacy

LLMs have unique advantages for businesses, including improved productivity, efficiency, and accuracy. Those that find ways to utilize this technology will have advantages over their competitors. Despite that drive for competitive advantage, fears surrounding privacy are a current barrier to implementing AI.

To address these concerns, we highlighted the benefits of using an API to gain control over your data. We also provided a resource guide for businesses interested in implementing this solution and outlined the steps for signing up and choosing the appropriate model for your needs.

It is important to reiterate that data privacy and security are paramount. As more companies turn to AI, they must also ensure that their valuable data is protected. However, that should not be a barrier to use. The paid version offers a solution that protects privacy while forging ahead with new technology. The future is coming fast. Let us know your thoughts about the usefulness of this article and reach out to our sales team if we can support your needs.

Copyright ? 2023 The Kansas City AI Company. All rights reserved.