With the continuous emergence of new business models and innovative application scenarios, AI technology has permeated various corners of the economy and society, driving unprecedented transformations across fields. However, this rapidly changing new situation has also brought numerous new problems and challenges, especially with the rise of generative AI, which, supported by big data and large models, has an influence that cannot be underestimated.
In the Context of Large Models
The Cyber Risks Brought by Generative AI
Generative AI has brought revolutionary innovations and significant conveniences in many fields. However, it also comes with a series of complex and severe cybersecurity risks and challenges.
First, the risk of misuse of deepfake technology. Generative AI can easily create extremely realistic audio, images, and videos. If this capability is exploited by criminals, it will lead to a proliferation of deepfake content, which can be widely used in defamation, spreading false information, political manipulation, and financial fraud, seriously damaging the reputation of individuals or organizations, misleading public perception, and even facilitating illegal transactions.
Second, the risk of cyber attacks cannot be ignored. The efficiency of generative AI in automatically detecting and exploiting software vulnerabilities greatly enhances the efficiency and scale of cyber attacks. Using big data analytics, attackers can more precisely target their attack objectives and choose the most suitable methods for attack. Additionally, attackers may exploit potential vulnerabilities in large models to carry out model injection attacks or data poisoning attacks, disrupting the normal training and prediction processes of machine learning models by injecting false data into datasets. Moreover, vulnerabilities in the complex supply chain of big data and large models also provide opportunities for attackers to compromise the entire system.
Third, the risks of data leakage and privacy infringement are becoming increasingly prominent. During the large-scale data collection by generative AI, issues such as the exposure of sensitive information, identity theft, and privacy violations frequently occur. Criminals can use generative AI to synthesize personal identity information or biometric data to engage in financial fraud and other criminal activities; at the same time, they can generate personal sensitive information or synthetic images to infringe upon personal privacy for inappropriate or illegal purposes.
Fourth, the spread of information warfare and fake news also brings severe public opinion risks. Generative AI can quickly generate and disseminate large amounts of fake news and misleading information, especially on social media platforms, where this information can rapidly spread, influencing public opinion and even interfering with election results and political decisions.
Fifth, the protection of intellectual property rights for individuals and organizations faces severe challenges. The ability of generative AI to imitate artworks, music, literature, and other forms makes the copyrights and intellectual property of original authors vulnerable to infringement. At the same time, content generated by AI may also trigger a series of legal and ethical issues, such as disputes over liability, authorship, freedom of speech, and censorship.
Responding to Cybersecurity Threats Posed by Generative AI
1. Application of Diversified New Technological Means
To effectively respond to the cybersecurity threats posed by generative AI, it is essential to fully utilize diversified new technological means. For instance, leveraging the immutability of blockchain can trace and verify the source and dissemination path of content, ensuring the authenticity and integrity of information. At the same time, developing and deploying deepfake detection tools that utilize machine learning algorithms can accurately identify the authenticity of videos, audio, and images to prevent the spread of false information. Additionally, applying biometric recognition technologies, such as facial action and micro-expression analysis, combined with watermarking and metadata techniques, can further validate the authenticity of content and protect personal privacy and copyrights.
2. Intelligent Identification and Early Warning of AI Technology
By leveraging big data and large models, the advantages of AI in big data analysis and machine learning can be fully utilized. Through AI-driven security monitoring systems, network traffic and behavior patterns can be analyzed in real-time to quickly identify abnormal activities and malicious behaviors, automatically triggering alerts and taking appropriate defensive measures. For example, AI can analyze massive amounts of network data to detect abnormal traffic, access, and malicious code, promptly identifying and responding to potential threats. Furthermore, by learning users’ normal behavior patterns, AI can accurately identify abnormal user behaviors, such as frequent access to sensitive data outside of work hours, timely issuing alerts to prevent data leakage and other security issues.
3. AI Security Trend Analysis and Prediction
AI can not only help identify current cybersecurity threats but also analyze historical attack and threat data to deeply learn and analyze the behavior patterns, targets, and potential attack methods of attackers. Based on this, AI can provide corresponding defense recommendations and predict future potential threats, issuing warnings and providing response plans. This helps network administrators take preventive measures in advance and reduce the risk of being attacked.
4. Automated Defense and Rapid Response
In terms of automated defense, AI can proactively discover security vulnerabilities and provide remediation measures through attack simulation and vulnerability scanning techniques. At the same time, AI systems can autonomously learn, make decisions, and automatically execute network defense tasks, freeing network administrators from tedious defense work, improving defense efficiency, and reducing human operational errors. Moreover, AI can quickly identify threats and respond within seconds, significantly shortening the time interval from threat detection to actual attack, thereby minimizing losses.
5. Building a Multi-layered AI Defense System
To further enhance the overall security of the system, a comprehensive multi-layered AI defense system needs to be constructed. On the basis of traditional defenses at the network layer, host layer, application layer, and data layer, an AI intelligent defense layer should be added. The AI intelligent defense layer can implement functions such as anomaly detection, behavior analysis, and adaptive defense, such as using streaming virus filtering engines and file restoration AI detection engines to quickly identify and block virus spread. Additionally, attention should be paid to the application security of large language models in complex network environments, implementing strict security measures from data isolation, model access control, and other aspects to ensure the safety and reliability of large language models.
6. Improving Laws and Regulations and Strengthening Cybersecurity Education
It is necessary to formulate and update relevant laws and regulations to address the challenges posed by generative AI. Clearly defining the boundaries of responsibility for AI technology through legal means, establishing traceability and accountability mechanisms, and curbing behaviors that use AI technology to disrupt the normal functioning of society through legal constraints and penalties. At the same time, it is essential to strengthen the supervision of online platforms and improve their preventive measures. Moreover, there is a need to enhance cybersecurity education and training regarding the threats posed by generative AI, improving the public’s ability to recognize and prevent generative AI-related criminal activities, thereby reducing security vulnerabilities and attack incidents caused by human factors. By applying a human-machine collaborative analysis model, we can collectively respond to the cybersecurity threats posed by generative AI.
7. Strengthening Audit and Supervision of the Design, Development, and Use of Large Models
Strengthen real-time monitoring and auditing of model-generated content, strictly screening model training data to ensure data quality. Setting up sensitive word filtering and maintaining a sensitive word library to filter text generated by the model; continuously promoting model iteration and updates, paying attention to collecting feedback on issues encountered in the application of large model algorithms, and making timely adjustments; when necessary, introducing human intervention to conduct secondary verification of the content generated by the model. Enhance vulnerability remediation to improve content risk prevention capabilities while advancing user identity verification systems to prevent abuse of models by anonymous users; establish a responsibility traceability mechanism, so that once content risks are identified, the source of the problem can be quickly located, and appropriate measures can be taken to avoid the misuse or even malicious use of AI technology.
Conclusion
In summary, cybersecurity faces multiple challenges in the AI era, and addressing AI risks requires a comprehensive consideration from various aspects of technology, management, and regulations. Balancing AI innovation and risk is a long-term and complex process that requires thoughtful collaboration from all sectors of society. Users also need to enhance their awareness of AI security risks and take appropriate protective measures. Only through joint efforts and collaborative advancement can we ensure the healthy development of AI technology and promote the sustainable development of the global economy and society.
Source: “Cybersecurity and Informatization” Magazine
Authors: Liu Lequn, Ji Yating, Li Shujia
(This article does not involve confidential information)
