While some SaaS hazards are obvious and transparent, others are hidden in plain sight, and both pose major risks to your firm. According to Wing’s data, 99.7% of enterprises use AI-enabled technologies. These AI-powered solutions are crucial, delivering seamless experiences across collaboration, communication, work management, and decision-making. However, beneath these advantages lies a largely unknown risk: the possibility that AI capabilities in these SaaS products can jeopardize important corporate data and intellectual property (IP).

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Wing’s recent findings reveal an unexpected fact: 70% of the top ten most popular AI applications may utilize your data to train their models. This approach can extend beyond simple data collection and storage. It may include retraining using your data, having human reviewers assess it, and even sharing it with outside companies.

These concerns are often buried in the fine print of Terms & Conditions agreements and privacy policies, which detail data access and convoluted opt-out methods. This stealthy strategy poses new vulnerabilities, making security teams struggle to maintain control. This paper explores these threats, presents real-world examples, and recommends best practices for protecting your firm with effective SaaS security solutions.

Four Risks of AI Training on Your Data

When AI apps use your data for training, several serious dangers arise, potentially impacting your organization’s privacy, security, and compliance.

IP and Data Leakage

One of the most serious issues is the potential disclosure of your intellectual property (IP) and sensitive data via AI models. When your business data is used to train AI, it may unintentionally divulge confidential information. This could include critical corporate plans, trade secrets, and confidential communications, posing significant risks.

Data Utilization and Misaligned Interests

AI programs frequently use your data to enhance their capabilities, which can result in a mismatch of interests. Wing’s research, for example, found that a prominent CRM application trains its AI models using system data such as contact details, interaction histories, and customer notes. This data is utilized to improve product features and add new functionality. However, it is also possible that your competitors, who utilize the same platform, could benefit from insights derived from your data.

Third-Party Sharing

Sharing your data with third parties poses a considerable risk. Data collected for AI training may be available to third-party data processors. These alliances aim to boost AI performance and encourage software innovation, but they raise concerns about data security. Third-party providers may lack adequate data protection procedures, increasing the risk of breaches and unlawful data use.

Inadequate Data Protection Measures

Many third-party providers do not implement sufficient data protection measures, which can lead to data breaches and unauthorized data usage. Ensuring robust data protection protocols is essential for mitigating these risks.

4. Compliance Issues

Global policies restrict data usage, storage, and sharing. Ensuring compliance becomes more difficult when AI applications train on your data. Noncompliance can result in significant fines, legal action, and reputational harm. Navigating these restrictions involves significant work and skill, complicating data management.

What Data Do They Actually Train On?

Understanding the data used to train AI models in SaaS apps is crucial for identifying potential dangers and establishing effective data protection measures. However, the lack of uniformity and transparency across various applications makes it difficult for Chief Information Security Officers (CISOs) and their security teams to identify the specific data being used for AI training. This opacity raises concerns about the unintended disclosure of sensitive data and intellectual property.

Navigating Data Opt-Out Challenges on AI-Powered Platforms

In SaaS services, information on opting out of data usage is sometimes dispersed and inconsistent. Some mention opt-out alternatives in terms of service, others in privacy policies, and some require you to contact the company via email. Inconsistency and a lack of transparency complicate the task for security professionals, emphasizing the need for a more streamlined approach to data control.

For example, one picture generation program allows users to opt out of data training by selecting private image generation options, which are only available with subscription plans. Another provides opt-out options, although this may affect model performance. Some programs allow individual users to change settings to prevent their data from being used for training.

The variation in opt-out options highlights the need for security teams to understand and manage data usage rules across firms. A unified SaaS Security Posture Management (SSPM) solution can assist by alerting and guiding users on available opt-out options for each platform, expediting the process, and ensuring compliance with data management rules and regulations.

Ultimately, understanding how AI uses your data is critical for risk management and compliance. Knowing how to opt out of data usage is also vital for maintaining control over your privacy and security. However, the lack of common methodologies among AI platforms complicates these tasks. Organizations can better secure their data from AI training models by focusing on visibility, compliance, and easily accessible opt-out alternatives. Leveraging a centralized and automated SSPM solution, such as Wing, enables users to navigate AI data issues with confidence and control, protecting the security of sensitive information and intellectual property.