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AI Security: Protecting Users from Impersonation

AI Security: Protecting Users from Impersonation

By: Suresh Dodda

Suresh’s professional stature is further underscored by his membership in prestigious organizations like IEEE and his role as a keynote speaker at esteemed research universities like Eudoxia. His contribution as a journal reviewer for IGI Global highlights his active involvement in advancing technology.

Today, the widespread advancement of technologies such as deep learning has provided ever-growing access to increased computation power and has become one of the most critical situations for user security and privacy. Specifically, “Generative AI,” which is more reliable and accurate, is being developed and implemented as it allows synthesizing and creating a large amount of data from a smaller number of inputs. This is a significant advantage compared to traditional AI. However, the capabilities of generative AI in terms of generating authentic content have also led to enormous risks for sophisticated impersonation attacks. In cybersecurity, more and more attacks and threats use AI methods and are becoming smarter and more powerful. With the rise of generative AI, individuals can likely expect bigger and more AI-oriented severe attacks in the future, and traditional cybersecurity methods and tools may not be effective in confronting those attacks. Therefore, new research areas focusing on improving user security and privacy in the new AI-pervasive age are in demand. Given the risks posed by rogue AI applications, including emerging threats such as those discussed above, it is crucial to understand this activity and consider research that explores safeguarding against such activity in future work. The following sections will explore multidisciplinary research areas in AI security and develop novel solutions for protecting digital content and user privacy. (Ferrara, 2024) (Kaloudi & Li, 2020) (Khoo et al.2022) (Blauth et al., 2022)

Impersonation through Generative AI

AI systems that can generate photorealistic images are now available to the public. Generative algorithms use large data sets and “learn” to create new data with the same statistics as the original set. These have been used to produce realistic images of people, animals, and scenery. Such images are often indistinguishable from real photographs and videos and will become even more realistic over time. Similar technologies, such as natural language processing algorithms, can be used to create text that imitates the writing style of a specific person. Such technology could enable an attacker to generate an image of a particular individual and then develop writing in a text that appears to be that person speaking – effectively, a form of digital impersonation. 

Importance of User Security and Privacy

User security is an essential consideration in developing and deploying generative AI technology, with robust measures necessary to prevent impersonation or misuse of user data. This need is underscored by the increasing volume and variety of personal data being gathered and shared by the users of online services. In recent years, the growth of social media and e-commerce platforms has led to a surge in the availability of user data, providing rich and valuable sources for potential attackers to exploit (Jain & Gupta, 2022). Today, ensuring privacy and secure personal information has been a major focus. However, a notable trade-off exists between the privacy and utility of users’ personal information. As new AI technologies emerge, such as deep learning algorithms and adversarial AI, new categories of security threats begin to show, making the task of protecting user identity and privacy increasingly challenging. Despite these emerging threats, regulatory and legislative progress has been limited. It is essential to consider the legal and ethical issues in the use of technology and the extent to which the state and law can protect against the misuse of personal information. With the explanatory work to be done in the field of AI and the increasing public awareness of privacy and data protection, there should be more movements involving both technical and non-technical measures to secure the safety and privacy of the public. The security and privacy measures employed should be justifiable by the data’s nature and supposed risk level. All possible aspects of private information and potential misuse of technology should be considered during the development of AI projects and the implementation of relevant regulations and legislation.

In particular, it is noted that current machine learning and AI applications have typically been used against traditional signature-based detection systems – that is, systems that search for known string patterns in network traffic and host activities. These detection systems often rely on the presence of an attack discovered and analyzed before a signature is distributed to the wider user community and implemented in security monitoring systems. This means there is a time window in which attacks can be successfully executed without detection until a signature is distributed and the attack is identified.

About the Author 

Suresh Dodda, a seasoned technologist with a strong focus on AI/ML research and 24 years of progressive experience in technology, is adept at leveraging Java, J2EE, AWS, Micro Services, and Angular for innovative design and implementation. With a keen eye for detail, Suresh excels in developing applications from inception to execution, showcasing his deep expertise in Java, as evidenced by his authored book on Microservices and his role as a book reviewer for publications such as Packet and BPB.

Suresh’s technical prowess extends to AI/ML, where he has contributed to research papers. His effective management skills have consistently ensured timely project delivery within allocated budgets. His extensive international experience includes working with esteemed clients such as Dubai Telecom in Abu Dhabi, Nokia in Canada, Epson in Japan, Wipro Technologies in India, Mastercard in the USA, National Grid in the USA, Yash Technologies in the USA, and ADP in the USA.

Within core industries such as banking, telecom, retail, utilities, and payroll, Suresh possesses a deep understanding of domain-specific challenges, bolstered by his track record as a technical lead and manager for globally dispersed teams.

Published by: Holy Minoza


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