As new technologies emerge, security measures often trail behind, requiring time to
catch up. This is particularly true for Generative AI, which presents several inherent security challenges. Here are some of the key risks related to AI that organizations need to bear in mind.
1. No Delete Button
The absence of a “delete button” in Generative AI technologies poses a serious security threat. Once personal or sensitive data is used in prompts or incorporated into the training set of these models, recovering or removing it becomes a daunting task. A data leak into an AI model is not just a breach; it leaves a permanent imprint. Therefore, protecting data against such irreversible exposure is more critical than ever.
2. No Access Control
The lack of access control in Generative AI presents significant security risks in business environments. Not only is it wise to control unsanctioned AI apps but also control access and usage based on who is using AI and how. This is because once information is transformed into embeddings (numerical representations showing relationships between data points), those can only be accessed in their entirety or not at all. This absence of Role-Based Access Control (RBAC) makes all data vulnerable, given there are no guardrails for who can access data, creating hazards in settings where restricted, role-based access is essential.
3. No Control Plane
Generative AI technology often fails to separate its control and data planes, a fundamental security practice established in the 1990s. This oversight blurs the lines between different types of data—such as foundation model data, app training data, and user prompts—treating them all as a single entity. This merging increases AI’s vulnerability, as malicious user interactions like prompt injections or data poisoning can compromise the AI’s core, creating a potential danger zone for security breaches.
4. Chat Interface Challenges
The integration of chat interfaces has made Generative AI more accessible and user-friendly, prompting many companies to adopt them for improved customer interaction. However, this shift introduces challenges. Unlike controlled interfaces with limited Natural Language Processing capabilities, chat interfaces allow unlimited user inputs, which can include harmful content or misuse of resources. For instance, a Chevrolet dealership experienced unexpected responses from their chat interface when abused by web visitors, underscoring the need for careful management and oversight.
5. Silent Gen AI Enablement
Organizations typically have three options for incorporating AI: creating their own solutions, purchasing new products, or relying on existing vendors with integrated AI. However, the latter can lead to issues, as the data processed by these authorized tools often remains unclear. This concern, already prevalent with general AI, has intensified with the rise of Generative AI, which poses higher risks. Recent controversies, such as those surrounding Zoom’s use of AI that could access and store sensitive information shared during Zoom sessions, or concerns about applications like Grammarly, highlight the need for transparency and control in how AI implements data privacy in business settings.
6. Lack of Transparency
The absence of transparency in training data for AI models poses a major security risk. If data sources are not well understood, hidden biases may influence the model’s outputs, leading to false information or unintended outcomes. Moreover, a lack of transparency can jeopardize user privacy, as individuals may be unaware of how their data is being used or exposed. Balancing security, privacy, and openness remains a challenging aspect of AI advancement.
7. Supply Chain Poisoning
Using Generative AI in code generation carries significant risks, especially if the training data contains vulnerable code or if the AI model is compromised. This can create considerable threats in the supply chain, particularly in critical tasks like autopilot systems or automated code production. The risk of duplicating vulnerabilities or introducing new ones can have serious consequences for the reliability and safety of technological systems, especially since current Generative AI models lack built-in safeguards against this.
8. Lack of Watermarking
The absence of established watermarking guidelines in Generative AI poses a severe security risk, particularly regarding deepfake production. Without effective watermarking, distinguishing between real and artificially generated content becomes increasingly difficult, raising the likelihood of spreading false information.
Zscaler is protecting enterprises from Gen AI Threats
While Generative AI offers transformative potential, it also brings fundamental security risks that must be addressed to ensure safety and reliability in its application. Zscaler is a prime example of an advanced security vendor that approaches securing Generative AI from the lens of having strong data protection capabilities, implementing strict access controls, delivering advanced threat detection, and a true Zero Trust security architecture designed to minimize risks by assuming no user or device is inherently trusted.
To learn more, visit us here.
