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Tag: Generative AI

  • What is the importance of cybersecurity in generative AI? Explaining specific risks and how to manage them

    What is the importance of cybersecurity in generative AI? Explaining specific risks and how to manage them

    cybersecurity in generative AI

    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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  • 7 disadvantages of generative AI? A detailed explanation of specific examples of failure and how to avoid them!

    7 disadvantages of generative AI? A detailed explanation of specific examples of failure and how to avoid them!

    7 disadvantages of generative AI

    Recently, generative AI has been garnering significant attention, with various companies utilizing it to achieve greater operational efficiency and productivity improvements. Generative AI is a very useful tool, but did you know that it also has several disadvantages?

    In this article, we will introduce seven representative disadvantages of generative AI. We will also explain specific failure examples and key points for utilization, so please read through to the end.

     

    What is Generative AI?

    First, let’s explain the basic knowledge about generative AI.

    Generative AI refers to next-generation AI where computer programs have the ability to generate data or information by themselves. While traditional AI primarily focused on finding appropriate answers from pre-learned data, generative AI can create new data or content. Its main characteristic is the ability to create something from nothing.

    Typical generative AI is designed to generate new data or content through iterative learning based on vast training data. Taking image generation AI as an example, by inputting a large number of images of “dogs” or “cats” into the AI, it learns the characteristics of dogs and cats. As a result, when you instruct the AI to generate images of dogs or cats, it will output detailed images capturing those characteristics.

    A famous example of generative AI is OpenAI’s ChatGPT, which can automatically generate new text based on textual data. However, it’s worth noting that ChatGPT’s latest model (as of September 2024), GPT-4o (full name: 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, audio, and images. The generative AI model “Gemini” announced by Google in December 2023 also supports this multimodal capability.

    Thus, there are AI models capable of automatically generating not only text but also images and videos, and they are used in various scenarios, from content creation for marketing activities to TV commercials. It can be said that generative AI is a powerful tool for companies to achieve operational efficiency, improve productivity, and connect these to business growth.

     

    7 Disadvantages of Generative AI

    This chapter explains seven representative disadvantages of generative AI. Understand the specific details to avoid failure in utilizing generative AI.

    1. Implementation Costs Incurred

    Generally, implementing generative AI involves certain costs. While some generative AI tools are available for free, from the perspectives of security and support, paid services are recommended for business use. At this point, to maximize the effectiveness of tool implementation, it is important to compare multiple services and select a tool that fits your company’s situation. Since the benefits of introducing generative AI are significant, consider the implementation cost as a necessary expense and proactively proceed with the consideration.

    2. Involves Ethical and Social Risks

    Utilizing generative AI involves ethical and social risks. For example, if the AI learns from data containing biases, it may generate content that includes those biases. Furthermore, the generated content could lead to misunderstanding or confusion among people, and in the worst case, could result in serious trouble or a loss of corporate trust. To avoid this, it is important to establish mechanisms for the appropriate use of generative AI across the organization, such as developing policies and rules regarding its use.

    3. Involves Data Security and Privacy Risks

    When using generative AI, the security and privacy protection of the data handled become critical issues. For example, when using large amounts of personal information or confidential data for AI learning, there are risks of unauthorized access to or leakage of that information to the outside. Also, since there is a risk that generative AI might output incorrect data, unintentionally disclosing confidential information, be sure to implement data management and security measures. Many generative AI services offered to enterprises have enhanced security features, allowing for safe use of generative AI. Therefore, carefully selecting a service is a key point when introducing generative AI.

    4. Difficulty Complying with Legal Responsibilities and Regulations

    When using generative AI, issues related to legal responsibilities and regulations cannot be ignored. This is because if the copyright or responsibility for AI-generated content is unclear, it could lead to legal disputes. However, since regulations vary by country and region, complying with all of them without omission is extremely difficult. As global expansion involves such legal risks, collaborating closely with your company’s legal department or external experts while using generative AI is an effective option for avoiding legal troubles.

    5. The AI’s Internal Process Becomes a Black Box

    One disadvantage of generative AI is that its internal process can become a black box. This refers to a state where the process by which the AI judges and generates is complex and difficult for developers and users to understand. If the AI cannot explain why it produced a specific output, its reliability and transparency may be called into question. Especially in fields requiring high specialization and accuracy, such as medicine and law, the risk of misunderstanding or trouble increases due to the AI’s black-box nature. To avoid such black-box situations, it is necessary to develop prompt collections and operation manuals for generative AI and promote appropriate AI use throughout the organization. This helps maintain a certain level of AI accuracy and obtain answers aligned with the user’s intent.

    6. Difficulty Securing Human Resources to Develop Generative AI

    Developing high-precision generative AI requires IT personnel with advanced specialized knowledge. However, Japan faces a chronic shortage of IT human resources, making it difficult to secure talented individuals. Therefore, when considering developing generative AI, in addition to actively recruiting mid-career professionals, also consider training your current employees to become IT personnel. If it’s difficult to conduct education and training in-house, requesting assistance from external experts is also a valid option. Our company, G-gen, offers programs to support the utilization of generative AI. For example, we support the use of Google’s latest generative AI and can handle everything from PoC to production-level implementation, so if you are interested, please feel free to contact us via our inquiry form.

    7. Risk of AI Hallucinations

    AI hallucinations, where the AI generates incorrect information, are another disadvantage of using generative AI. The accuracy of the output generated by AI depends on the quality and reliability of the data. Therefore, training AI models based on reliable information leads to ensuring technical reliability and explainability. We explain AI hallucinations in detail in the article below.
    Related article: Pitfalls of AI Utilization? A Clear Explanation of the Causes and Countermeasures for AI Hallucinations!

    We have introduced seven disadvantages of generative AI. While generative AI is a very useful tool, there are also many points to be cautious about, such as ethical and social risks and the challenge of securing human resources.

    Therefore, when using generative AI, it is important to clarify the purpose and application of its introduction to maximize its effectiveness, while also understanding its disadvantages and establishing mechanisms and rules to avoid various risks. By practicing the correct way to interact with generative AI in this manner, it can become a powerful tool for promoting your company’s productivity improvement and business growth.

     

    Failure Examples of Generative AI Utilization

    So far, we have explained the disadvantages of generative AI. What kind of failures actually occur? This chapter introduces three examples of failures in utilizing generative AI.

    Samsung Electronics (External Leakage of Source Code)

    At Samsung Electronics in South Korea, source code was leaked externally due to the use of generative AI. This incident is said to have been caused by an employee inputting code information into a generative AI, which resulted in data existing on the server being mistakenly shared with external users. Following this external leak, the company established a new policy regarding the use of generative AI. For example, they banned the use of generative AI tools on company devices and, for personal devices, imposed certain restrictions on input data (e.g., not uploading company-related information), taking steps to prevent the recurrence of information leaks.

    Macnica Networks Company (AI Quality Issues)

    Macnica Networks Company, a technology provider within the Macnica Group, embarked on developing a generative AI chatbot aimed at improving employee knowledge and operational efficiency. However, once the chatbot’s operation began, quality issues were discovered, such as the AI responding in English to Japanese questions. Upon investigating the cause, they found various problems, including a lack of mechanisms to verify the accuracy and operation of each component, and a failure to define the details of user business processes targeted by the chatbot. Consequently, the company reviewed its RAG (Retrieval-Augmented Generation) mechanism. By separating the retrieval and generation processes, they were able to obtain more accurate answers than before. RAG is a mechanism for improving the quality and performance of generative AI and is gaining attention primarily in the field of natural language processing. Instead of the AI generating information independently, it searches for relevant information in databases or websites to create answers, resulting in higher accuracy. We explain RAG in detail in the article below.
    What is RAG? A Thorough Explanation of Its Mechanism, Benefits, and Precautions!

    OpenAI (Lawsuit Regarding Copyright)

    When using generative AI in business, it is not uncommon for it to lead to copyright lawsuits. In August 2023, more than ten authors, including George R.R. Martin and John Grisham, filed a lawsuit against OpenAI, claiming that “ChatGPT is using copyrighted material without permission.” Furthermore, in December of the same year, The New York Times sued OpenAI and Microsoft for billions of dollars in damages, alleging “unauthorized use of New York Times articles, depriving them of subscription and advertising revenue opportunities.” As seen, this can sometimes lead to lawsuits, so when using generative AI, be extremely careful about copyright and other rights issues.

     

    Key Points for Avoiding Failure in Generative AI Utilization

    There are several points to keep in mind when utilizing generative AI. Finally, we will introduce key points for successfully utilizing generative AI.

    Clarify the Purpose of AI Utilization

    When introducing generative AI, the first step is to clarify the purpose of its use. Be specific about what you want to achieve and in which business processes you will use the AI. If you use AI with a vague purpose, you may not be able to fully utilize its capabilities, and the cost could be wasted. Therefore, clarifying the purpose of AI use and setting specific, measurable goals is a crucial point.

    Implement Thorough Security Measures

    The data handled by generative AI is a company asset, making thorough security measures mandatory. This is because the data processed by AI often contains confidential information or personal data, requiring robust security to prevent data leaks and unauthorized access. Additionally, since the AI itself could become a target of cyberattacks, it is also important to continuously review and update security measures.

    Be Careful Not to Infringe on Rights

    Regarding content created by generative AI, it is important to pay attention to rights issues such as copyright and trademark rights. Especially when the AI generates content by referencing other works or data, it is necessary to verify whether the final output infringes on the rights of others. Seeking advice from legal experts can help minimize these risks.

    Conduct Employee Education and Skill-Up Training

    To effectively utilize generative AI, employees need to understand the AI’s mechanisms and Operating instructions.. Therefore, it is important to conduct employee education and skill-up training, enabling them to acquire knowledge from basic AI concepts to practical skills. Having personnel proficient in AI within the company allows you to maximize the potential of generative AI and enhance your company’s competitiveness.

    Check the Quality of Training Data

    The accuracy and reliability of generative AI depend heavily on the quality of its training data. If the training data contains biases or errors, the AI’s output will be affected. Therefore, when using generative AI, quality control of the training data is a key point. Implement data selection and cleaning to ensure the AI can learn properly, enabling it to generate accurate and useful content.

    Refine the Content of Prompts

    The content of the “prompt” given to instruct the generative AI is one of the key factors that significantly influences the generated result. Setting specific and clear prompts leads to more accurate outputs. Creating prompts requires trial and error, but since refining them can dramatically improve AI performance, investing sufficient time in crafting them is key to success. Note that “prompt engineering” exists as a technical and academic field for efficiently utilizing AI, so keep that in mind for reference.

     

    Conclusion

    In this article, we introduced seven representative disadvantages of generative AI.

    Generative AI is a very useful tool, but it also has several disadvantages. Understand the key points on what to be cautious about when using it.

    Furthermore, Google Cloud can be an effective tool when utilizing generative AI. Google Cloud is a public cloud service provided by Google, equipped with many generative AI-related services to help companies achieve operational efficiency and productivity improvements. For example, a major feature of Google Cloud is the availability of various generative AI services, such as Conversational AI services like Dialogflow for natural language processing, and Imagen for image processing. Additionally, using Vertex AI allows you to freely customize AI models, enabling flexible AI utilization tailored to your company’s specific situation.

     

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