Generative AI: Fundamental Security Risks

When new technologies emerge, security often lags behind, taking time to catch up. This is particularly true for Generative AI, which inherently encompasses several fundamental security challenges within its design. Here are a few of the challenges with Generative AI

No Delete Button:?

The lack of a "delete button" in Generative AI technologies represents a grave security threat. Once personal or sensitive data is absorbed into the training set of these models, eradicating it becomes a daunting, almost insurmountable task. Any data leak into an AI model is not just a breach but a permanent imprint, making the protection of data against such irreversible exposure more critical than ever.

No Access Control:

The absence of access control capabilities of Generative AI presents a significant security threat in business settings. Once information is transformed into embeddings, it can only be fully accessed or not at all, leaving no room for customized permissions. This lack of Role-Based Access Control (RBAC) means that all data is vulnerable, creating a potential security hazard in environments where restricted, role-based access is crucial.

No Control Plane:?

The failure of Generative AI technology to separate its control and data planes, a widely recognized security precaution from the 1990s in networking, is a significant oversight with concerning implications. This newer technology blurs the boundaries between different types of data – Foundation Model Data, App Training Data, and User Prompts – treating them all as a single entity. This merging makes the entire system vulnerable to potential attacks from users, as there is no clear distinction between the planes. As a result, the AI's core is easily compromised, turning it into a potential danger zone where any user interaction could trigger a security breach with severe consequences.

Chat Interface:?

The integration of chat interfaces has greatly propelled the growth of Generative AI, making it more accessible and user-friendly. As a result, many companies have adopted chat interfaces to improve customer interaction. However, this shift also presents its own challenges. In the past, controlled interfaces with limited Natural Language Processing capabilities, such as dashboards and filters restricted enterprises. The introduction of chat interfaces has opened the door to unlimited user inputs, which can include harmful content, potential attacks, or misuse of resources. For example, a Chevrolet dealership experienced unexpected responses from their chat interface, emphasizing the importance of careful management and supervision in this rapidly evolving field.

Silent Gen AI Enablement:

In the business In the business world, organizations usually have three options for incorporating AI: creating their own solution, purchasing a new product, or relying on existing vendors who have already integrated AI into their offerings. However, this last option can present issues as these applications are already authorized. It is often unclear what happens to the data processed by these tools. This concern has been present for some time with general AI, but has become more prominent with the emergence of Generative AI, which poses higher risks. Recent events, such as the controversy surrounding Zoom's use of AI and concerns about applications like Grammarly that have extensive access to data and may secretly use Generative AI, highlight the need for transparency and control in implementing AI in business settings.

Lack of Transparency:?

Lack of The absence of transparency in the training data for AI models poses a major security threat. If the data sources are not well understood, there is a possibility of hidden biases influencing the model's outputs, causing false information or unintended outcomes. Moreover, the lack of transparency in data sources can also put user privacy at risk, as they may not be aware of how their data is being used or exposed. The delicate balance between security, privacy, and openness remains a difficult aspect in the advancement of AI.

Supply Chain Poisoning:

Using Gen AI in code generation poses significant dangers, particularly if the training data contains susceptible code or if the AI model is compromised. This creates considerable risks in the supply chain, particularly when AI is used in vital tasks such as autopilot systems or automating code production. The possibility of AI unintentionally duplicating vulnerabilities or introducing new ones could have significant consequences for the reliability and safety of technological systems. Currently, there are no built-in features in Gen AI to prevent this.

Lack of Watermarking:?

The lack of established watermarking guidelines in Generative AI poses a significant danger for the security of deepfake production. As a result, it is becoming more challenging to distinguish between real and artificially produced material, increasing the likelihood of spreading false