Greetings,
I am Kakeya, the representative of Scuti Jsc.
At Scuti, we specialize in offshore and lab-based development in Vietnam, leveraging the power of generative AI. Our services include not only development but also comprehensive generative AI consulting. Recently, we have been privileged to receive numerous requests for system development integrated with generative AI, reflecting the growing demand for innovative AI-driven solutions
How does your company perceive the security risks associated with generative AI?
With the rise of generative AI, many companies are discovering new business opportunities. However, this innovative technology also harbors the potential for security risks and problems. This article focuses on the security risks of generative AI and the countermeasures companies can take. By referring to actual cases, it explores the risks and issues faced by companies and provides effective guidelines and measures. This article will provide a detailed explanation of the security risks of generative AI and the measures against them.
The Importance of Security in Generative AI
Security Risks Faced by Companies
As the use of generative AI progresses within companies, it is imperative for them to properly manage the associated security risks. This is because, while generative AI contributes to the automation of business processes and the creation of new services, it also has the potential to cause security issues such as data leaks and unauthorized access.
For example, the automatic generation of content using generative AI carries the risk of being exploited by malicious third parties to spread misinformation or conduct phishing attacks. Additionally, if the data handled by generative AI is highly confidential, any leakage of this data could result in significant damage to the company.
To mitigate these risks, companies should establish security guidelines for the use of generative AI, enhance employee training, and conduct regular system audits. In conclusion, understanding these security risks and implementing appropriate measures is essential for the safe utilization of generative AI.
For those who want to learn more about generative AI, please refer to our other article, “Introduction to Generative AI: A Clear Explanation of Text and Image Generation“.
Learning Security Challenges from Case Studies
Understanding the security risks associated with generative AI can be significantly enhanced by studying actual cases. Below are several reported incidents.
Corporate Users Entering Source Code into ChatGPT
According to Netskope Threat Labs, out of 10,000 corporate users, 22 individuals per month are posting source code, resulting in an average of 158 incidents. This surpasses the posting of regulated data (average 18 incidents), intellectual property (average 4 incidents), and passwords and keys (average 4 incidents), making source code the most frequently leaked sensitive information.
Reference URL: Infosecurity Magazine
Samsung Employees Entering Confidential Source Code into ChatGPT
Samsung employees input confidential source code into ChatGPT, leading to a violation of the company’s confidential information management policy and resulting in information leakage.
Reference URL: Springer Link
Samsung Bans Use of Generative AI Apps after Data Leak
After an incident where some employees accidentally leaked confidential data via ChatGPT, Samsung banned the use of generative AI applications by employees from May 2023 and decided to develop its own AI applications.
Reference URL: Infosecurity Magazine
Data Leak Due to ChatGPT Bug
At the end of March 2023, OpenAI disclosed a data leak caused by a bug in an open-source library, necessitating the temporary offline status of its generative AI application. This data leak exposed payment-related information of some customers and allowed viewing of chat history titles for some active users.
Reference URL: Infosecurity Magazine
Microsoft AI Team Accidentally Publishes 38TB of Data
Microsoft’s AI research team mistakenly published 38TB of private training data, which included highly sensitive information.
Reference URL: Springer Link
Risks of Data Poisoning and Manipulation in Generative AI Models, Especially ChatGPT
Generative AI models, particularly ChatGPT, face risks of data poisoning, leading to the generation of false results. This can result in the dissemination of misleading information, potentially impacting business decisions.
Reference URL: Springer Link
Learning Security Challenges from Examples
To deepen the understanding of security risks associated with generative AI, learning from actual cases is highly effective. For instance, there was a case where a company leaked confidential information due to inadequate security measures while analyzing customer data using generative AI. The root cause of this problem was that the security measures for the generative AI system did not sufficiently consider the confidentiality of the data. Additionally, there have been reports of cases where fake documents and images created by generative AI caused social turmoil. The lesson to be learned from these cases is how important it is to manage risks and strengthen security for generative AI technology. As measures, companies need to establish security guidelines for generative AI and thoroughly educate employees about handling data. Furthermore, regular security audits are required to promptly discover and address vulnerabilities in the system. In conclusion, referring to actual cases to enhance the awareness of security risks of generative AI and taking specific measures are crucial for companies.
Security Measures During the Use of Generative AI
Practical Risk Management and Response
Security measures when utilizing generative AI require practical risk management and response. This necessity arises from the need to safely exploit generative AI technology by identifying potential risks in advance and taking appropriate measures. For instance, to prevent the misuse of AI-generated content, the implementation of digital watermarking and content tracking technologies can be suggested. Furthermore, to counter the risks of unauthorized access and data breaches, employing robust authentication systems and encryption technologies is effective. Additionally, it is crucial for companies to conduct regular security training to enhance the security awareness of their employees. As a concrete example, there have been cases where service-providing companies using AI have combined these measures to protect customer data and maintain system security. In conclusion, companies utilizing generative AI can maximize the technology’s potential while minimizing risks by practically engaging in risk management and security measures.
Specific Steps for Accident Prevention
When utilizing generative AI, it is important to follow specific steps for accident prevention. First, conducting a security risk assessment for all AI projects is fundamental. This allows for the identification of project-specific risks and the planning of countermeasures. Next, implementing strict access control and data encryption to protect data and reduce the risk of confidential information leakage is crucial. Additionally, considering security from the design phase of the AI system and enhancing resistance to unauthorized inputs and operations are necessary. Furthermore, conducting regular security education and training for employees to improve security awareness is essential. As a practical example, there have been cases where companies providing customer services using generative AI have implemented these steps to prevent accidents proactively. In conclusion, for accident prevention, it is essential to follow specific steps from risk assessment to implementation and employee education to ensure the safe use of generative AI.
Constructing Effective Security Guidelines
Constructing effective security guidelines is a crucial step for companies utilizing generative AI. In essence, these guidelines provide a foundation for companies to manage security risks and ensure the safe use of AI technology. The reason is that security guidelines set clear standards and procedures, enabling companies to effectively respond to security challenges such as data protection, access management, and system vulnerabilities. For example, the guidelines may include rules for handling data used in training AI models, measures to prevent unauthorized access to AI systems, and processes for responding to incidents. Additionally, in formulating these guidelines, it’s necessary to consider industry standards and regulatory requirements to ensure alignment with the company-wide security policy. In practice, many companies have developed security guidelines and effectively managed the risks associated with generative AI. To reiterate, the construction of effective security guidelines is indispensable for minimizing risks associated with the use of generative AI and protecting corporate value.
In Japan, there are public guidelines such as the Generative AI Usage Guidelines issued by the Japan Deep Learning Association and the Text Generation AI Usage Guidelines by the Tokyo Metropolitan Government. Referring to these, companies or organizations can create guidelines tailored to their specific needs.