Mitigating Shadow AI Risks by Implementing the Right Policies

[rakhirhif8963]

In conclusion, IDC’s forecasts paint a bright picture of the road ahead for CIOs navigating an ever-changing technology landscape. The opportunities for innovation are vast, from AI to automation to analytics. But it’s not just about implementing shiny new tools; it’s also about building a solid foundation of data management, resilient platforms, and proactive cybersecurity measures.

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 nepal mobile database 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.

IT and security teams are grappling with unauthorized applications that can lead to network intrusions, data leaks, and disruptions. At the same time, organizations must avoid a too-rigid approach that could stifle innovation and prevent breakthrough product development. Enforcing policies that prevent users from experimenting with GenAI applications will negatively impact productivity and lead to further silos.