The Rise of Generative AI: Security Concerns and Recommendations for Securing the Enterprise
As generative AI becomes more prevalent in consumer and business settings, it is essential to address the associated security concerns. As a seasoned IT/IS veteran with over 20 years of experience, I will provide insights on the risks posed by generative AI, particularly in terms of privacy threats, intellectual property theft, and operational reliability issues. Furthermore, I will offer three recommendations for securing the enterprise when adopting these technologies.
Privacy Concerns with Generative AI
One of the primary concerns with generative AI is the potential for privacy threats. With native support for generative AI integrated into operating systems and functioning in the background beyond our control, there is a risk of automatic extraction of intellectual property by AI-driven services. This could result in the exploitation of user data by vendors like Microsoft and Apple, as well as their broader service ecosystems. Furthermore, there are legitimate concerns about IP potentially leaking on these platforms, even though vendors claim they do not sample such data.
Intellectual Property Theft
The integration of generative AI into operating systems has also raised questions about intellectual property theft. As users create their own unique content on devices infused with AI, there is a risk that these vendors may be scraping and leveraging this data to incorporate it into their models. This could lead to legal disputes over copyright ownership, as there are currently no clear guidelines or agreements in place to protect user data.
Operational Reliability Issues
Another concern is operational reliability issues. Few AI vendors, including OpenAI, have published their service level agreements for uptime or provided certification documentation like the trust services criteria documentation provided by SOC2 certification. This means that there is a risk of adopting technologies that fail to meet our own operational requirements, potentially leading to downtime and disruptions.
Recommendations for Securing the Enterprise
To address these concerns, I recommend the following three strategies for securing the enterprise when adopting generative AI:
1. Know How to Disable and Monitor New Applications: IT personnel should prioritize training on how to disable or implement security configurations based on operational requirements. This includes disabling all ads through Windows notification settings, as well as carefully considering and monitoring these features in a test environment before widely adopting operating systems that enable them by default.
2. Leverage Data Leak Protection (DLP) Tools: Implement solutions like Nightfall that employ DLP to intercept and block sensitive information from being accessed or transmitted via AI-driven platforms like chatbots or ChatGPT. Agent-based web content filters can aid in detection with work from home employees, but a broader approach is required to monitor OS-enabled AI solutions.
3. Enhance and Update User Awareness Training: Provide quarterly updates to user awareness training to educate employees on the latest best practices, as threats are constantly changing. This will ensure that your user awareness content remains relevant and effective in preventing attacks.
In conclusion, the rise of generative AI poses significant security concerns for enterprises, particularly in terms of privacy threats, intellectual property theft, and operational reliability issues. By implementing the recommended strategies, organizations can better secure their environments and protect against these risks. As a seasoned IT/IS veteran with over 20 years of experience, I will continue to monitor these developments and provide insights on how to address them.