In-house AI generative models as a solution to address AI security concerns
Generative AI models and services, which are often accessible to the general public or external organizations, are typically offered by third-party companies or open-source projects. They provide an exciting avenue for both individuals and organizations, presenting a wealth of opportunities and challenges. On one hand, generative AI represents a transformative force, streamlining tasks, fostering creativity, and boosting efficiency. It has the potential to enhance content generation in a wide array of domains, spanning from automated code writing to creative content production. This technology stands as a valuable asset capable of significantly elevating productivity and the overall user experience.
The generative AI market, as reported by Bloomberg Intelligence, stands on the cusp of substantial growth. Its value has surged from $14 trillion in 2020 to $67 trillion in the present year, with forecasts projecting a remarkable leap to $897 trillion by 2030. This astounding prediction translates into an extraordinary 640% increase in market size over the span of a mere decade.
As generative AI becomes an increasingly attractive prospect, the interest of business leaders in its integration into their operations is mounting at a rapid pace. With the widespread adoption of generative AI, these leaders are eager to leverage its benefits to maintain competitiveness. However, in parallel, they are increasingly vigilant about the potential AI security risks associated with these less regulated AI models, which could potentially jeopardize the integrity of their organizations.
Data Privacy and Confidentiality
The primary concern is the privacy of the data that employees and customers share with AI models like ChatGPT and other similar applications. There’s a worry that sensitive information could be exposed or mishandled. Another significant concern is the storage of data used in AI interactions. Some organizations may choose to retain data for extended periods, believing it is necessary for training and quality assurance purposes. However, the longer data is retained, the higher the risk of a security breach or unauthorized access. Over time, the accumulated data can become a valuable target for malicious actors.
In May, Samsung banned the use of ChatGPT after employees accidentally leaked sensitive data on the platform. Data breaches may lead to legal consequences, including lawsuits, regulatory investigations, and penalties. More importantly, reputational damage is a significant impact that business leaders’ fear and concern the most. Therefore, safeguarding data from cyberattacks, breaches, and leaks is paramount for corporations.
Intellectual Property
Generative AI, particularly in the form of natural language generation (NLG) models like ChatGPT, is increasingly becoming a part of today’s legal landscape. According to the 2023 Thomson Reuters report, 82% of lawyers out of 440 survey respondents from large and midsize law firms across the United States, Canada, and the United Kingdom believe that ChatGPT and generative AI can be applied to legal work. Legal professionals and organizations are exploring its applications, such as drafting legal documents, using legal databases to expedite legal research, and facilitating automated reporting. These various purposes can save a significant amount of time and effort. However, they also raise several concerns related to intellectual property rights and copyright laws.
AI-generated legal documents and reports depend on training from a diverse range of data sources, including copyrighted content and legal research. If the AI-generated content closely resembles or paraphrases copyrighted documents, it may constitute copyright infringement. As a result, legal professionals should carefully review AI-generated documents to ensure they are original and do not infringe on existing copyrighted material.
The 2023 Thomson Reuters report also indicates that half of corporate legal professionals believe that ChatGPT and generative AI should not be applied to legal work due to the increasing awareness of the risks mentioned above. This highlights the importance of addressing these concerns and ensuring that generative AI is used responsibly and ethically in the legal field.
Critical Decisions
CEOs who place their trust in unreliable AI-generated content might overlook significant opportunities. For example, if AI-generated market analysis fails to accurately pinpoint emerging trends or customer preferences, the organization may forego chances for growth and innovation, or even lead to financial losses if relying on inaccurate forecasts. Even if CEOs express interest in developing an in-house AI generative model, the costs associated with ensuring its security can be substantial. Thus, it becomes a critical decision for business leaders to consider the wisdom of investing in this field, as it can ultimately impact the company’s revenue and profitability.
Additionally, if inaccurate AI-generated content is used in decision-making processes that later result in legal and compliance issues, the organization may face lawsuits, fines, or regulatory actions. Professionals may be held accountable for such consequences, which can damage their professional reputation.
In May of this year, a U.S. lawyer faced a court hearing after unknowingly using false legal research. This incident has raised concerns over the potential risks associated with AI-generated content, including the potential spread of misinformation and bias.
Building In-House Generative AI as a Solution
1. Tailoring AI Models to Unique Organizational Needs
When organizations choose to develop their own generative AI capabilities, they unlock numerous advantages. The most significant of these is the control it provides over data privacy, a crucial asset in today’s data-driven world. By creating AI models in-house, these organizations gain an unmatched level of control over their sensitive data. Building an in-house generative model means relying less on external AI services, which can result in cost savings and a reduced risk of service disruptions caused by issues like data breaches, unauthorized access, or unreliable data sources. This independence nurtures a feeling of security and autonomy, which is particularly important when AI plays a vital role in essential business operations.
2. Building and Fine-Tuning AI Models
Creating a customized AI model that meets the organization’s specific needs requires a careful data collection process. This process is especially vital in sectors that primarily deal with textual data, like legal services. For example, a legal firm needs to gather a comprehensive and relevant dataset that includes legal texts, case documents, statutes, and other legal literature that aligns precisely with their particular needs and goals.
3. Data Preparation: The Crucial First Step
To carry out this thorough process, you need a dedicated team of AI experts, data scientists, and engineers. They take on the responsibility of building and refining AI models. After collecting the data, there’s a crucial data preparation phase. This includes tasks like cleaning up the data, organizing it, and making sure it’s of high quality. Once the data is in good shape, the experts and engineers carefully choose the right neural network architecture, often favoring deep learning models. They then begin training the model using the meticulously prepared data.
4. The Training Phase: Where AI Learns
The training phase is where the AI model learns from the training data, understanding the complex patterns and relationships within it. This phase is critical because it shapes the AI model’s ability to create content that makes sense and is contextually relevant. Afterward, the AI model undergoes rigorous testing using separate datasets to evaluate how well it performs. The goal here is to find and fix any issues, biases, or inaccuracies in the AI-generated content. Fine-tuning and validation involve making necessary adjustments to improve the model’s output quality, often requiring multiple rounds of refinement.
5. Deployment and Continuous Monitoring
Once the model is optimized to meet the organization’s strict requirements, it’s ready for practical use. It seamlessly integrates into various systems, applications, or platforms within the organization, becoming a vital part of its operations. Continuous monitoring of the AI model is crucial. AI experts and data scientists keep a close eye on its performance, addressing emerging issues, and regularly updating it with new data and insights.
By following these steps carefully, organizations can confidently create an in-house generative AI model that’s both effective and reliable, perfectly aligned with the organization’s goals.
