Data Protection in a Shadow AI World
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.
AI insights also require innovative approaches due to the huge volume of data being processed and generated, which if left unchecked could leave an organization at risk of breaching data privacy regulations. So-called “confidential computing” is one approach that some companies are taking. Essentially, it involves encrypting data as it is being processed, so that sensitive and private data cannot be exposed. It is a way to ensure that the data used and/or generated by shadow AI applications is not at risk.
Current market statistics suggest that remote work will remain a viable option for the foreseeable future. Various research forecasts show that a significant portion of the IT workforce, especially those with application development and AI skills, are driving this trend. Other fields such as medicine, healthcare, accounting, finance, and marketing also have a significant presence of remote work. All of these professions have the opportunity to become shadow AI practitioners, as generative AI is readily available.
Organizations need to carefully design cayman islands mobile database actively implement various remote application security measures to help IT better control unauthorized and incompletely vetted shadow applications. Remote application solutions, for example, can help organizations that are already in the process of cloud transformation to deploy a Zero Trust Architecture (ZTA). This is achieved by implementing a remote browser isolation solution that evaluates requests based on the company’s access policy and security measures. This will allow IT to begin implementing ZTA at the cloud level for all users, regardless of where they are located or distributed around the world. Another benefit is that there is no need for expensive edge hardware.
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.
Remote Shadow AI Adds Complexity
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