To prevent such risks, you need to follow four rules

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Four Ways to Prevent Data Leaks with Shadow AI
One of the biggest concerns for organizations in the GenAI world is data security. When feeding confidential company information, source code, or financial data into AI tools, there are concerns about the exposure of sensitive data and whether that information will be used to train underlying models. Some industries, such as healthcare or financial services, are particularly sensitive to data breaches.


Strengthen all key security measures governing data flows.
Add AI-specific security enhancements.
Improve solutions for remote workers to new zealand mobile database risks associated with shadow AI.
Accept that employees strive for productivity, and AI can be useful if properly managed.
These methods will ensure a secure data environment.
Mitigating Shadow AI Risks by Implementing the Right Policies
20.03.2024
With the rise of artificial intelligence, security is becoming increasingly important in every organization, which creates a new problem - "shadow AI" applications that need to be checked for compliance with security policies, writes Kamal Srinivasan, senior vice president of product and program management at Parallels (part of Alludo), on the Network Computing portal.

The shadow IT problem of recent years has evolved into the shadow AI problem. The growing popularity of large language models (LLM) and multimodal language models has led to product teams within organizations using these models to create use cases that improve productivity. Numerous tools and cloud services have emerged that make it easier for marketing, sales, engineering, legal, HR, and other teams to create and deploy generative AI (GenAI) applications. However, despite the rapid adoption of GenAI, security teams have yet to figure out the implications and policies. Meanwhile, product teams building applications are not waiting for security teams to catch up, creating potential security issues.