Data Protection in a Shadow AI World

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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.

The Shadow AI Visibility Problem
Shadow IT has created a community of workers who use unauthorized devices to support their workloads. It has also given rise to “citizen developers” who can use no-code or low-code tools to build applications without going through official channels to obtain new software. Today, we have citizen developers using AI to build AI applications or other types of software.

These AI-powered apps drive productivity and speed up pakistan mobile database completion, or show how far LLMs can go in solving a complex DevOps problem. While shadow AI apps are typically not malicious, they can consume cloud storage, increase storage costs, pose network threats, and lead to data leaks.

How can IT departments gain visibility into shadow AI? It makes sense to strengthen the practices used to mitigate shadow IT risks, with the caveat that LLMs can make anyone a citizen developer. At the same time, the volume of applications and data generated is increasing significantly. This means a more complex data protection task for IT teams, who must observe, monitor, learn, and then act.

The output of shadow AI must be discovered, analyzed, and subject to the same security policies that apply to other data workloads in the enterprise. Ensuring that data discovery, monitoring, and policy enforcement tools are operating at peak performance is a critical first step. Analysts can use AI-powered automation tools running 24/7 to flag unusual behavior and help prevent data privacy and compliance breaches.