Addressing Generative AI Data Privacy Concerns in 2024
Raido Linde
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August 21, 2024
Generative artificial intelligence enables enterprises to understand data in ways traditional technologies cannot. It has created a new era for organizations to reimagine how to access, create, and use information.
For example, you can ask natural language questions from data. This can boost efficiency, save time, and cut costs, among other benefits. Yet, the recent hype around generative AI has also raised data privacy concerns.
In this article, we will explore the main generative AI privacy concerns and challenges, and possible solutions for mitigating these risks and issues.
Generative AI is the newest and fastest way to make sense of data. An important aspect to understand is that machine learning models are the building blocks of Generative AI. These models learn patterns from both structured and unstructured data to generate new information or insights. For example, they can generate new images, text, audio, and, more recently, videos.
Generative AI affects data privacy because machine learning models need access to specific data to process queries that are not contained in the model’s own training data, such as internal corporate information. Poor processing or lack of security can raise data privacy issues with AI. What could have been a better, faster, or smarter way to understand data can become a burden you want to avoid.
New technological advancements bring fresh challenges. With generative AI, the challenges are privacy concerns related to securing data, training data, and legal and compliance issues. A proactive approach to mitigate these concerns is crucial to avoid facing the consequences.
Generative AI privacy issue examples are:
In the past decade, there has been a race to collect as much data as possible. More data is always better, has been the rule. Some even claim that the data is more valuable than gold. In many ways, it is; at least more good data is better. The issue is that organizations struggle to securely and efficiently manage and analyze these extensive datasets. This is especially concerning with an ever-increasing flow of new data generation from sources such as emails, client records, social media, ERP systems, and so on. Taking advantage of this data without facing data privacy concerns is challenging.
Many industries will see a push to move from on-premises to the cloud. This is especially true for generative AI and LLM models. The core problem is that cloud-based solutions may train from users' input or at a very least the information needs to be moved to the cloud for the model to access. That means, if you use their services and enter any confidential information into their systems, that data can potentially leak or be exposed, compromising your privacy. Automotive design secrets, social security numbers, and internal strategies are now at risk. Data once considered secure is now vulnerable.
Many governments are forming new laws to protect data from unlawful processing. The latest EU act, which came into force on 01 August 2024 sets criteria for data training and data governance, especially in high-risk industries in the EU. Furthermore, EU GDPR law says individuals have the right to know where their data is, how it is processed, and that their personal data is to be deleted upon request. If organizations process data with generative AI models, especially cloud-based solutions, protecting the data becomes problematic - adding another layer of complexity. Non-compliance can be costly for both the EU AI Act and GDPR. Fines range from €10 million to €30 million or 2% to 6% of global annual turnover, whichever is higher.
There is a need for the solution that mitigates these privacy issues, and it becomes more crucial each day as the adoption of AI technology increase.
We believe that data privacy, security, and costs are the biggest concerns of current cloud providers. Also, organizations that have been early adopters of generative AI have started to realize this. As a result, there is a transition back to on-premises from the cloud. Here is how confidentialMind helps this transition:
We believe that every organization should take advantage of generative AI in its own terms. They should have full control over their data, applications, and models. This is why we have developed generative AI software infrastructure designed for on-premises and private cloud use cases. It’s the only way to generate real and long-term value that differentiates organizations from competitors.
The ConfidentialMind platform provides authentication mechanisms to ensure secure access to applications deployed on the stack. These mechanisms enable the management of user access to different applications, including configuring access management in selected application settings. Additionally, we ensure a secure environment by offering streamlined software development, AI agent management, and authentication managed by proprietary technology.
Open-source LLMs are the backbone of our beliefs and guaranteed security. Their benefit is that you can run these models in your environment, meaning you have full control over these systems and the data you run in them. This provides high security generative AI environments, keeping your data from ever leaking.
Overall, these features ensure the secure and private operation of the ConfidentialMind stack that enables enterprises to generate new business value, reduce costs, and increase ROI with generative AI.
Having more data is not good if you cannot use it. At the same time, more sensitive data becomes an asset when you can combine it with generative AI. You can only gain these benefits for your organization if you first address generative AI data privacy concerns.
For example, use ConfidentialMind’s services that mitigate these issues. One way or another, generative AI is here and here to stay. So, why not take the step to adopt it before your competitors or wait until it is too late.
