In an era when Software as a Service (SaaS) applications are becoming more prevalent in the workplace, a potential threat has emerged: using sensitive business data to train AI. While these AI-powered tools improve productivity and decision-making, they also expose organizations to significant risks, including intellectual property theft, data leaks, and compliance violations.
The Pervasiveness of AI in SaaS
A staggering 99.7% of organizations use applications with built-in AI capabilities, according to a recent survey by Wing Security. These tools have become essential for collaboration, communication, and workflow management. But that convenience comes at a price: a staggering 70% of the top 10 most commonly used AI applications may use user data to train the models.
The risks revealed
The dangers of training AI with sensitive data are manifold. First, intellectual property (IP) and trade secrets can be inadvertently leaked. If proprietary information is fed into an AI model, it is at risk of being leaked, potentially providing an advantage to competitors or malicious actors.
Second, using data for AI training can create conflicts of interest. For example, a popular customer relationship management (CRM) application was found to be using customer data, such as contact details and interaction history, to train AI models. This raises concerns that insights gained from one company's data could be used to benefit competitors using the same platform.
Third, sharing data with third-party vendors involved in AI development creates security risks: these vendors may not have the same rigorous data protection measures in place as major SaaS providers, increasing the likelihood of data breaches and unauthorized access.
Finally, using data for AI training can raise compliance issues: countries have different regulations regarding the use, storage, and sharing of data.
No transparency about data opt-outs
Further exacerbating these risks is a lack of transparency and consistency in how SaaS applications handle data opt-out mechanisms. Information about opt-outs is often buried within complex terms of use and privacy policies, making it difficult for organizations to control how their data is used.
Overcoming risks
To mitigate these risks, organizations must take proactive measures, carefully scrutinizing SaaS application terms of use and paying close attention to data usage policies. Implementing a centralized SaaS security posture management (SSPM) solution can help identify and manage potential risks, such as data usage for AI training.
While AI-powered SaaS applications offer undeniable benefits, organizations must remain vigilant about the potential risks associated with training data. Understanding these risks and taking appropriate measures can help organizations harness the power of AI while protecting sensitive information.
