Combating IP leaks in AI applications with free detection and risk reduction automation

Free Discovery And Risk Reduction Automation

Wing Security announced today that it is now offering free discovery and a paid tier for automated monitoring across thousands of AI and AI-powered SaaS applications. This allows companies to better protect their intellectual property (IP) and data against the growing and evolving risks of AI use.

Combating IP leaks in AI applications with free detection and

SaaS applications seem to be multiplying by the day, and so does the integration of AI capabilities. According to Wing Security, a SaaS security company that has researched more than 320 companies, as many as 83.2% use GenAI applications. While this statistic may not come as a surprise, the research showed that 99.7% of organizations use SaaS applications that leverage AI capabilities to deliver their services. This use of GenAI in SaaS applications that are not “pure” AI often goes unnoticed by security teams and users alike.

70% of the most popular GenAI applications can use your data to train their models, and in many cases it’s completely up to you to configure them differently.

When eBy examining hundreds of AI-using SaaS applications, Wing Security was able to categorize the different ways these applications use organizational data and provide a solution for this new threat:

Data storage: In some cases, data is stored by the AI ​​for very long periods of time; in other cases it can only be kept for short periods. By storing data, AI learning models and future models can continuously train on it. That said, the biggest concern is considering the many different types of attacks on SaaS applications. When an application is compromised, the data stored in it can also be compromised.

Model training: By processing massive amounts of information, AI systems can identify patterns, trends and insights that can elude human analysis. Through machine learning algorithms, AI models learn from data and adapt over time, fine-tuning their performance and accuracy, resulting in better service to their end users. The downside is that letting these models learn your code, patents, sales and marketing knowledge gives AI-using applications the potential means to commoditize your organization’s competitive advantage. Some consider these knowledge leaks to be more important than data leaks

The human element: Certain AI applications use human validation to ensure the accuracy and reliability of the data they collect. This collaborative approach, often called human-in-the-loop or human-assisted AI, involves the integration of human expertise into the algorithmic decision-making process. This results in higher accuracy of the AI ​​model, but also means that a human working for the GenAI application is exposed to potentially sensitive data and know-how.

1705504794 650 Combating IP leaks in AI applications with free detection and

Leveraging automation to combat AI SaaS risks

Wing’s recently released AI solution guarantees that security teams will better adapt and control the ever-growing and virtually unstoppable AI use in their organizations. Their solution follows three basic steps: know, assess, control.

Know: As with many security threats, the first step is discovering them all. In the case of AI, it is not enough to simply highlight the usual suspects or the pure GenAI applications such as ChatGPT or Bard. With thousands of SaaS applications using AI to improve their service, discovery must include every application that uses customer data to improve their models. Like theirs previous solutionsWing offers this first and foundational step as a free, self-service solution that allows users to onboard themselves and discover the breadth of AI-powered applications used by their employees.

To estimate: Once AI-using SaaS is discovered, Wing automatically provides a security score and details the ways in which corporate data is used by the AI: How long is it retained? Is there a human factor? And perhaps most importantly: is it configurable? Provides a detailed overview of the application’s users, permissions, and security information. This automatic analysis allows security teams to make more informed decisions.

Check: Wing’s discovery and analysis uncovers the most critical issues that need to be addressed, allowing security teams to easily understand the level of risk and the types of actions needed. For example, deciding whether or not to allow the use of a particular application or simply configure the AI ​​elements to better suit their security policies.

The secret: automating all of the above

By automating Discovery, Assessment, and Control, security teams save time figuring out where to focus their efforts, rather than spreading themselves out trying to solve a vast and evolving attack surface. This then significantly reduces the risk.

Wing’s automated workflows also enable a unique multi-organization solution: by allowing users to communicate directly with the administrator or users of the application, Wing delivers more informed security solutions in addition to a stronger security culture of integration rather than a simple black or white list.

1705504794 481 Combating IP leaks in AI applications with free detection and

In an era where SaaS applications are ubiquitous, integrating them with artificial intelligence presents a new kind of challenge. On the one hand, using AI has become a great tool to increase productivity, and employees should be able to use it because of its many benefits. On the other hand, as the reliance on AI in SaaS applications continues to increase, the potential risks associated with data usage are increasing.

Wing Security has responded to this challenge by introducing a new approach aimed at enabling organizations to navigate and manage the escalating use of AI within their operations, while engaging end users and ensuring they understand the AI -Be able to use the SaaS they need. safe. Their automated control platform provides a comprehensive understanding of how AI applications use organizational data and know-how, addressing issues such as data storage, model training, and the human element in the AI ​​loop. Security teams can save valuable time through clear risk prioritization and user engagement.

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