While some SaaS threats are obvious and visible, others are hidden in plain sight, both of which pose a significant risk to your organization. Wing research shows that a staggering 99.7% of organizations use applications with built-in AI capabilities. These AI-driven tools are indispensable as they provide a seamless experience from collaboration and communication to work management and decision making. However, beneath these conveniences lies a largely unrealized risk: The AI capabilities in these SaaS tools can compromise sensitive business data and intellectual property (IP).
Wing’s latest findings reveal some startling statistics: 70% of the 10 most used AI programs can use your data to train its models. This practice can go beyond simple training and data storage. This may include having your data reprocessed, reviewed by reviewers, and even shared with third parties.
Often these threats are hidden in the fine print of T&C agreements and privacy policies that describe data access and complex opt-out processes. This stealthy approach creates new risks, leaving security teams struggling to maintain control. This article examines these risks, provides real-world examples, and suggests best practices for protecting your organization with effective SaaS security measures.
Four risks of training artificial intelligence on your data
When AI applications use your data for training, there are several significant risks that can affect your organization’s privacy, security, and compliance:
1. Intellectual Property (IP) and Data Leakage
One of the most critical concerns is the potential exposure of your intellectual property (IP) and sensitive data through artificial intelligence models. If your business data is used to train artificial intelligence, it may inadvertently reveal business information. This can include confidential business strategies, trade secrets, and confidential messages that lead to significant vulnerabilities.
2. Use of data and conflicts of interest
AI programs often use your data to improve their capabilities, which can lead to conflicts of interest. For example, research by Wing found that a popular CRM application uses data from its system, including contact details, interaction history and customer notes, to train its AI models. This data is used to improve product features and develop new features. However, it can also mean that your competitors using the same platform can benefit from the insights gained from your data.
3. Sharing with third parties
Another significant risk includes sharing your data with third parties. Data collected for AI training may be made available to third-party data processors. These collaborations aim to improve AI performance and drive software innovation, but they also raise concerns about data security. Third-party providers may lack robust data protection, increasing the risk of hacking and unauthorized data use.
4. Compliance concerns
Various regulations around the world impose strict rules on the use, storage and sharing of data. Compliance becomes more complex when AI programs are trained on your data. Failure to comply can result in large fines, lawsuits and reputational damage. Managing these rules requires significant effort and expertise, further complicating data management.
What data are they actually training on?
Understanding the data used to train AI models in SaaS applications is critical to assessing potential risks and implementing robust data protection measures. However, the lack of consistency and transparency in these applications creates challenges for Chief Information Security Officers (CISOs) and their security teams when determining the specific data used to train AI. This lack of transparency raises concerns about the inadvertent disclosure of confidential information and intellectual property.
Opt-out navigation challenges on AI-powered platforms
In SaaS applications, data opt-out information is often scattered and inconsistent. Some mention opt-out options in the terms of service, others in the privacy policy, and some require you to email the company to opt out. This inconsistency and lack of transparency complicates the task for security professionals, emphasizing the need for a rational approach to controlling data usage.
For example, one image creation app allows users to opt out of data learning by opting for private image creation options available with paid plans. Another offers opt-out options, although this may affect the performance of the model. Some apps allow individual users to adjust settings to prevent their data from being used for training.
The variability of opt-out mechanisms highlights the need for security teams to understand and manage data usage policies across companies. Centralized SaaS Security Health Management (SSPM) the solution can help by providing alerts and guidance on available opt-out options for each platform, streamlining the process and ensuring compliance with data governance policies and regulations.
After all, understanding how AI uses your data is critical to managing risk and ensuring compliance. Knowing how to opt out of data usage is just as important to maintaining control over your privacy and security. However, the lack of standardized approaches across AI platforms makes these tasks challenging. By prioritizing visibility, compliance and affordable opt-out options, organizations can better protect their data from AI learning models. Using a centralized and automated SSPM solution like Wing empowers users with confidence and control over AI data tasks, ensuring that their sensitive information and intellectual property remains secure.
