The hidden dangers of data learning

As Software as a Service (SaaS) applications become ubiquitous in the workplace, a lurking threat has emerged: the use of sensitive business data for AI training. While these AI-powered tools improve productivity and decision-making, they also expose organizations to significant risks, including intellectual property theft, data breaches, and regulatory breaches.

The occurrence of AI in SaaS

Recent research from Wing Security shows that a staggering 99.7% of organizations use applications with AI capabilities. These tools have become essential for collaboration, communication and workflow management. However, convenience has its price. A significant 70% of the top 10 most used AI applications can use your data to train their models.

Threats revealed

The dangers of training AI on sensitive data are manifold. First, it may lead to unintended disclosure of intellectual property (IP) and trade secrets. When proprietary information is fed into AI models, it becomes vulnerable to leaks, which could benefit competitors or malicious actors.

Second, using data to train AI may pose a conflict of interest. For example, the popular Customer Relationship Management (CRM) application was found to be using customer data, including contact details and interaction history, to train its AI models. This raises concerns about whether insights from one company’s data could be used to the advantage of its competitors using the same platform.

Third, sharing data with third-party vendors involved in AI development poses security risks. These providers may not have the same stringent data protection measures as the primary SaaS provider, increasing the risk of data breaches and unauthorized access.

Finally, using data for AI training can lead to compliance issues. Different countries have different regulations regarding the use, storage and sharing of data.

Data opt-out opacity

These risks include a lack of transparency and consistency in how SaaS applications handle data opt-out mechanisms. Opt-out information is often hidden in complex terms of service or privacy policies, making it difficult for organizations to control how their data is used.

Navigating risks

To mitigate this risk, organizations must take proactive steps. They should carefully check the terms and conditions of the SaaS application, paying particular attention to data usage policies. Implementing a centralized SaaS security posture management (SSPM) solution can help identify and manage potential threats, including using data for AI training.

While AI-powered SaaS applications offer undeniable benefits, organizations must remain vigilant about the potential risks associated with data training. By understanding these threats and taking appropriate measures, they can leverage the power of AI while protecting their sensitive information.