The Importance of Cybersecurity in Generative AI
While generative AI is a highly convenient tool, it also brings increasing risks from a cybersecurity perspective that cannot be ignored. For example, issues such as data leakage through unauthorized access and the generation and spread of false information can have serious impacts on individuals, companies, and society as a whole.
In particular, phishing scams and deepfakes (technologies that convincingly forge people’s faces and voices) that misuse generative AI carry risks of diminishing corporate trustworthiness and causing social disruption. To address these risks, managing AI model training data and implementing access restrictions to prevent misuse are essential.
Additionally, users of generative AI themselves need to heighten their security awareness and handle suspicious information cautiously. Alongside legal frameworks and guideline development by governments and companies, raising awareness at the individual level will likely be key to safe AI utilization in the future.
What is Generative AI?
Generative AI refers to next-generation artificial intelligence with the capability to independently generate data and information. While traditional AI primarily focused on finding appropriate answers from pre-learned data, generative AI can create new data and content, with its distinctive characteristic being the ability to generate something from nothing.
Typical generative AI systems are designed to generate new data and content through iterative learning based on extensive training data. A famous example of generative AI is OpenAI’s ChatGPT, which can automatically generate new text based on textual data.
However, ChatGPT’s latest model, GPT-4o (officially named GPT-4 omni), is provided as a multimodal model. “Multimodal” refers to a mechanism that collects, integrates, and processes information from multiple different sources such as text, voice, and images. Google’s latest generative AI model “Gemini,” announced in December 2023, also supports this multimodal capability.
In recent years, numerous generative AIs specialized in images and videos have also emerged. Specific examples include Midjourney, Stable Diffusion, and Runway Gen-2, which can easily generate high-quality images and videos without much effort.
Thus, there are AI systems capable of automatically generating not only text but also images and videos, and they are being utilized in various scenarios, from content creation for marketing activities to television commercials. Generative AI can be said to be a powerful tool for companies aiming to achieve operational efficiency, improve productivity, and connect these efforts to business growth.
Typical Risks of Generative AI
We have explained the importance of cybersecurity in generative AI, but what specific risks are lurking? This chapter introduces three typical risks of generative AI.
Information Leakage
Generative AI learns from vast amounts of data, but if this data includes confidential information, there is a risk that your organization’s sensitive data could be leaked. For example, if internal company information or personal data is exposed externally, it could lead to irreversible consequences. To avoid such risks, appropriate security measures such as proper anonymization of input data and access restrictions to AI models are essential.
Spread of Misinformation
A major advantage of generative AI is its ability to easily create high-quality text and images, but this can also be exploited to generate misinformation and fake news. When fake content spreads throughout society, users may become unable to distinguish truth from falsehood, potentially leading to loss of corporate credibility and social disruption. To address this, mechanisms to verify the reliability of information sources and careful identification and evaluation of AI-generated content are important.
Cyber Attacks
Generative AI carries risks of being misused to imitate cyber attack methods or generate sophisticated phishing emails and malware. This makes individuals and companies more likely targets of cyber attacks, potentially causing economic losses and social disruption. Therefore, security tools capable of detecting AI-generated attack patterns and security education are strongly demanded.
As shown, the risks lurking in generative AI are diverse. To safely utilize generative AI, it is essential to deepen understanding of these risks and implement appropriate countermeasures.
Cases Where Generative AI Use Escalated into Major Problems
There are numerous cases where the use of generative AI has led to significant problems. Let’s examine three specific examples to understand the concrete details.
Fraud Damage Caused by Deepfakes
A multinational company based in Hong Kong experienced large-scale fraud damage due to deepfakes. An employee received an email purportedly from the company’s CFO and joined a video conference via a link contained in the email, where the CFO appeared to be present.
Following the CFO’s instructions, the employee transferred approximately 3.8 billion yen to a specific account. However, this CFO was a fake created using deepfake technology, and by the time this was discovered, the funds had been transferred to overseas accounts and could not be recovered. This case illustrates the threat that sophisticated AI-based forgery technology poses to corporate decision-making processes.
External Leakage of Program Code
A major overseas electronic products manufacturer experienced an incident where its confidential program code was leaked externally. An employee instructed ChatGPT to modify code for a service under development, resulting in the code being exposed externally. This case suggests that when employees have insufficient awareness of cybersecurity and rules for AI utilization are not established, there is a risk that it could lead to information leakage.
Ransomware Creation
Cases where generative AI use has escalated into major problems have occurred not only overseas but also in Japan. In May 2024, an unemployed man in Kawasaki City, Kanagawa Prefecture, instructed multiple interactive generative AIs to design and create original ransomware (a type of virus that infects PCs and smartphones). It must not be forgotten that while generative AI is a convenient tool that enriches daily life and business, it is a double-edged sword that can also be misused depending on how it is used.
Risk Management Methods When Utilizing Generative AI
When using generative AI, it is necessary to prepare for various risks. This chapter explains risk management methods for utilizing generative AI.
Thorough Data Management
When utilizing generative AI, extreme care must be taken in handling input data. If data containing confidential information or personal information is used as-is, the information may be stored in the AI model, creating a risk of unauthorized output.
Data sanitization (methods that anonymize data and remove unnecessary information) is an effective option for avoiding this risk. Additionally, it is important to limit data access and use AI in a secure environment that meets security standards.
Monitoring and Verification of Generated Content
Content output by generative AI needs to be appropriately managed under human supervision. For example, it is important to establish processes for regularly verifying generated content to prevent the spread of misinformation or inappropriate material.
Furthermore, utilizing technologies to identify AI-generated information and filtering functions based on pre-established rules contributes to improving AI safety. Thus, establishing mechanisms for monitoring and verifying generated content is an important point in achieving appropriate risk management.
Security Education and System Reinforcement
To minimize the risks of generative AI, security education for users and stakeholders is essential. Deepen knowledge about potential risks and misuse possibilities of generative AI, and cultivate skills to recognize suspicious content and attack patterns.
It is also important to strengthen security systems within companies and organizations and prepare for rapid response to cyber attacks. If it is difficult to conduct security education or build systems internally, consulting external experts is also an effective option.
Government Initiatives Regarding Generative AI Cybersecurity
In recent years, cybersecurity in generative AI has become a societal issue. The Japanese government is also undertaking various initiatives toward creating an environment for safe generative AI utilization.
For example, the Ministry of Internal Affairs and Communications aims to advance cyber attack countermeasures utilizing generative AI, striving to improve the collection and analysis of threat information and the accuracy of attack infrastructure detection. It is also promoting research and development related to AI safety through the formulation of guidelines for safe AI development and provision, as well as joint research with specialized US institutions.
Furthermore, the Digital Agency published the “Agreement on the Business Use of Generative AI such as ChatGPT (2nd Edition)” in September 2023, providing guidelines on risk management and appropriate usage methods for business use of generative AI. These initiatives aim to reduce risks associated with utilizing generative AI and realize a safe and reliable digital society.
Thus, the Japanese government is also actively promoting initiatives for the safe use of generative AI.
However, relying entirely on government and corporate initiatives is extremely dangerous. To safely use generative AI, it is essential for each user to heighten their awareness of generative AI cybersecurity and implement appropriate risk management.
Conclusion
This article has explained the importance of cybersecurity in generative AI, specific risk management methods, and government initiatives.
While generative AI is a highly convenient tool, it is also true that it involves various risks such as information leakage and the spread of misinformation. Please reread this article to understand the content of typical risks and risk management methods.
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