Cybersecurity frameworks for AI-enabled leukemia genomic data analysis

 Corresponding author: Mohammad Kabir Hussain Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Cybersecurity frameworks for AI-enabled leukemia genomic data analysis Mohammad Kabir Hussain 1, *, Badhon Sutrudhar 2 and Md Shadman Soumik 3 1 Washington university of Science and Technology MBA Healthcare Management. 2 Master of Science in Cyber Security, Department of Information Technology Bay Atlantic University Washington DC, USA. 3 Master of Science in Information Technology Washington University OF Science & Technology. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 Publication history: Received on 03 September 2025; revised on 11 October 2025; accepted on 13 October 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3501 Abstract Genomic data analysis has now been transformed by artificial intelligence (AI), and analysis of leukemia, specifically, has provided revolutionary possibilities of precision medicine. However, there are certain issues that come with this innovation, the major one being the security of sensitive genetic information. The current article focuses on cybersecurity structures that will ensure AI-based analysis of leukemia genomic data is secured. It highlights the need to have a strong encryption, strong data-access policies, and specialized anomaly-detection systems designed for the use of genomic data. In addition, the manuscript outlines the use of blockchain technology to guarantee safety and privacy of genomic information. This paper provides a systematic plan of risk reduction and regulatory adherence by analyzing the current AI models and determining their vulnerability to cyberattacks. The adoption of these systems can make healthcare professionals and researchers have confidence in AI-based leukemia genomic research, which will eventually lead to better treatment results. Keywords: Systems; Precision Medicine in Leukemia; Data Privacy and Integrity; Blockchain in Healthcare; Genomic Information Protection; Access Control and Machine Learning 1. Introduction The past few years have seen artificial intelligence (AI) making significant strides within the healthcare industry, and medicine in particular, within the field of precision medicine. About the treatment of leukemia, AI has shown significant potential in improving the accuracy and effectiveness of genomic data interpretation. By using algorithms that use machine learning, researchers and clinicians can identify genetic markers, predict disease progression and individual treatment regimens. However, despite the various benefits that AI offers, there are severe cybersecurity threats and especially in the protection of highly sensitive genomic data. Accordingly, the integration of artificial intelligence in genomic data analytics of leukemia requires the establishment of advanced cybersecurity infrastructures to maintain confidentiality of patient data, data integrity, as well as to prevent cyberattacks that may compromise the credibility of AI-drawn conclusions. The analysis of genomic information is a complex task that involves the processing of large amounts of information, often including very sensitive patient data. In the case of leukemia, such data include genetic, environmental and clinical variables. The combination of these dimensions allows the building of an overall phenotypic profile, which in turn can help clinicians to plan more specific and adequate therapeutic interventions. It is more imperative, therefore, that advanced analytic processes carried out by AI models-especially machine-learning algorithms-are implemented to find latent patterns beyond human analysts' capacity, and thus expand our knowledge on the genetic basis of leukemia. The use of AI in medical fields creates several cybersecurity concerns. Despite their ability to crunch numbers, AI models are still prone to adversarial attacks that can distort genomic data analyses and thereby produce a wrong diagnosis or World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 866 inappropriate therapeutic recommendations. Moreover, the enormous amount of data present in genomic analyses makes them an ideal target for cybercriminals to monetize or exploit the sensitive information for malicious reasons. Since the health industry can be considered a highly lucrative target of cyberattacks, the recent increase in data breaches only makes the need to implement strict security even more imperative. The most important is to protect the privacy and safety of information in AI-based genomic analysis of leukemia. Genomic information is inherently personal and an unauthorized access will result in drastic outcomes, including identity theft, insurance fraud, and patient confidentiality breaches. An effective cybersecurity system is therefore required to ensure that this information is not threatened by cybercrime. The framework in question will have to consider the following problems: data encryption, access control, and anomaly detection, so that only the skilled staff will be able to access sensitive information, and that the data will not be damaged during the course of analysis. Encryption forms part of the core components of any cybersecurity policy, especially where there is a high sensitivity to the genomic information. Strong encryption algorithm ensures that the data that has been intercepted cannot be read or deciphered. Nonetheless, encryption is not sufficient. It has to be supplemented with access-control systems that control the access of genomic information. Role-based access control (RBAC) and attribute-based access control (ABAC) impose severe restrictions on how data should be used depending on the roles and attributes of individuals who can access the system and, thus, only qualified researchers, clinicians, or healthcare providers can see or analyze certain genomic data. Another element of the cybersecurity model is related to the area of anomaly detection. Genomic data analysis AI models should be armed with mechanisms that can detect abnormal tendencies or practices that are indicative of potential cyberattacks. This can be achieved by incorporating AI to make sure that one monitors irregular access patterns or data manipulations. The introduction of the use of continuous monitoring systems will allow organizations to identify potential threats and respond to them in real-time, reducing the chances of a successful cyberattack. With the ever-increasing amount and complexity of genomic data, blockchain technology is set to become a potential tool for data integrity and security. The decentralized nature of blockchain creates less potential for targeted attacks by storing the genomic information at a single point of failure. In addition, the unchangeable register of blockchain ensures that the genomic data once added to the system cannot be modified, as it will be accurate and reliable. The attribute is especially essential in the analysis of leukemia genomic data where the integrity of data is essential in the accurate diagnosis and treatment of the disease. Although AI and blockchain can have important potential benefits, the healthcare industry has a lot of challenges related to their implementation. Regulatory compliance is one of the major hindrances to the adoption of artificial intelligence (AI) and blockchain technologies in the healthcare sector. Data-protection statutes such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States involve a higher standard of requirements when it comes to the storage, access, and dissemination of patient information. In other words, the implementation of state-of-the-art AI and blockchain solutions must be closely monitored for these statutory requirements. Apart from technical obstacles, also organizational hindrances must be overcome. Integration of AI in the interrogation of leukemic genomic datasets should be done in cooperation between clinicians, researchers, and informationtechnology experts. Thorough training of all involved parties, including the technical aspects of AI and the ethical aspects of patient data, is required to ensure successful implementation of AI-powered precision medicine. Furthermore, healthcare institutions need to invest in resources towards the necessary infrastructure, such as secure data repositories, high-performance computing platforms and a workforce with specialized expertise. The potential good effects of AI on the analysis of leukemic genomic data are overshadowed by heavy risks. Robust cyber security frameworks are needed not only as a technical precondition, but also as an ethical requirement. Healthcare organizations must ensure that patient information remains accurate and uncorrupted while protecting it from cyber-attacks. When provided with secure cyber-security systems, Artificial Intelligence can bring its potential to fruition to improve diagnosis, treatment and survival outcomes for patients suffering from leukemia. Ultimately, incorporating AI into the analysis of leukemic genomic data has transformative potential for precision medicine. However, it is necessary that the technological advancement be supported by a robust cyber security apparatus, which reduces the malicious exploitation of the sensitive data, ensures that the solutions conform to the regulatory standards, and builds public confidence in AI based healthcare solutions. This paper discusses the key elements of such a structure and provides recommendations based on recent empirical research in the areas of AI, blockchain, and cyber-security. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 867 2. Literature review Integrating artificial intelligence into the analysis of the genomic data in leukemia is a major leap in the field of personalized medicine, as it allowed for the construction of very specific and individualized therapeutic strategies. Nevertheless, the regular use of AI powered instruments and technologies brings up serious concerns about the protection of sensitive genetic information. The growing reliance on genomic information as a basis for both biomedical research and clinical decision-making makes the need for the security, privacy, and integrity of this information increasingly important than ever before. The following section is a critical review of the existing literature at the intersection of AI, genomic data analysis and cybersecurity, but with a special focus on frameworks developed to secure fundamental information on which these endeavors are based. 2.1. AI in Genomic Data Analysis The field of genomic data analytics, in oncology, has gone through a huge leap thanks to the application of artificial intelligence. Machine-learning algorithms (ML) are being actively used in the detection of genetic markers, in predicting oncologic vulnerability and in the personalization of treatment regimens. In the case of leukemia, AI systems can question the numerous genomes to recognize the mutations, biomarkers, and genetic predispositions that can clarify their effect on the development and therapy response of the disease. For example, Srivastav et al. (2025) show that AI can reduce inequalities in cancer care by improving screening, therapy, and cancer survival thanks to the fine analysis of genomic data. In terms of the diverse applications of AI in genomic interpretation, the authors highlight the powerful potential to address some of the barriers caused by limited access to healthcare in underserved populations by offering more equitable and efficient solutions. Moreover, AI is useful when it comes to predicting response to treatment, optimizing treatment protocols, and tracking patient courses. In an article by Khera et al. (2025), the authors analyze the role of AI in the field of precision medicine in the cardio-oncology realm, where AI can help us shed light on the multidimensional interplay between the genomic phenomena, cardiovascular health, and oncologic therapies. Although the study does not specifically refer to leukemia, the principles remain applicable for genomic data analysis of leukemia, for which AI can make it easier to refine treatment plans and make predictions about the prognosis. The application of AI in genomic medicine requires the use of large and heterogeneous data sets for the effective training of AI algorithms, which increases the risk profile of cybersecurity threats. The size and the complexity of the genomic data make it a potential target for cybercriminals. 2.2. Genomic Data Analysis Cybersecurity Issues Genomic data is one of the most sensitive types of personal health information, making the processing of this data highly vulnerable to cyberattacks like the theft of health data, poisoning of algorithms, and adversarial manipulation of artificial intelligence models, among others. Poovathanathil et al 2025 emphasize the emerging concerns about security of the AI-guided precision medicine, particularly in immunologic diseases in which genomic data is an integral part of the treatment process. The authors suggest the deployment of entire cybersecurity systems, which could make the benefits of AI counterbalanced by the potential risks of misuse of data. A corrupted dataset can give incorrect results resulting in incorrect prescriptions of treatments or misdiagnoses. In addition, cybercriminals can use such data to commit identity theft or fraud. Goyal et al. (2025) talk about the usefulness of predictive analytics as a strong tool for disease management and also highlight the need for cybersecurity in ensuring the integrity of healthcare data. They argue that strong encryption and access control mechanisms are needed to maintain the authenticity and confidentiality of clinical information, especially AI-based healthcare systems. 2.3. An Existing Cybersecurity Framework Several cybersecurity models have been suggested to address the vulnerabilities of genomic data analysis using AI. Such models generally include encryption, access control, anomaly detection and secure storage of data. Panahi (2025) takes a close look at the use of encryption in the protection of sensitive healthcare information, such as genomes. Strong encryption means that even if data is intercepted as it is being transmitted, it cannot be accessed without the relevant decryption key. However, encryption alone is inadequate, and it must be complimented by efficient access control protocols to prevent non-authorized retrieval of the encrypted genomic information. Xu et al. (2021) make a case for role-based access control (RBAC) and attribute-based access control (ABAC) as mechanisms to protect AI-driven healthcare systems. While RBAC limits access based on the organizational role of an individual, ABAC adds other contextual parameters (such as access time and geographic location) to determine the level of authorization. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 868 Blockchain technology has become a potential solution to cybersecurity in genomic data analysis. Akingbola et al. (2024) report on the use of blockchain in cancer care in African countries where it has the potential to maintain the integrity of data and to see transparency in healthcare transactions. Blockchain's decentralization eliminates the need for a single point of physical storage and so, it reduces vulnerability to cyber-attack. Furthermore, as a blockchain is immutable, once committed, the data cannot be altered or removed, so there are high guarantees of data integrity - an important property when part of the informed decision-making process for medical treatment is critical. 2.4. The Object of Detection of Anomaly Anomaly detection is part of the basics of AI-based genomics data analysis from a cybersecurity standpoint. Genomic models should include mechanisms that are able to recognize inappropriate behaviors, such as unauthorized access or attempts to alter data as part of AI systems. Renugadevi et al (2024) assess the effectiveness of anomaly detection systems based on machine learning models to continuously monitor the access patterns and data usage of genomic data. These systems help to identify irregular activity in real time and healthcare providers to implement counter measures. Anomaly detection is especially useful in a clinical setting, where any small breaches can cause significant damage to patients. Figure 1 Blockchain-based Genomic Data Security Model Table 1 Key Elements of Cybersecurity Framework for AI-Enabled Genomic Data Analysis Framework Component Description Example in Genomic Data Analysis Encryption Protects data during transmission and storage by converting it into unreadable format without a decryption key. Use of AES-256 encryption for genomic datasets. Access Control Restricts access to genomic data based on user roles or attributes. Role-based access control (RBAC) for healthcare professionals accessing data. Anomaly Detection Identifies unusual behavior that may indicate a potential breach or attack. Machine learning algorithms detecting suspicious access patterns. Blockchain The application of decentralized ledger technology makes it possible to ensure the integrity of data and reduce the chance of unauthorized alterations. The use of blockchain technology allows for the full logging of all access attempts towards genomic data. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 869 The use of artificial intelligence in the analysis of genomic data relevant to the cause of leukemia, has a lot of potential for the development of precision medicine, but this brings with it major cybersecurity risks. To reduce these threats, sensitive genomic data needs to be secured by robust security structures that include encryption, access control, anomaly detection, and blockchain platforms. Such frameworks are critical in ensuring the integrity, confidentiality, and availability of genomic data and thereby, allow clinicians to maximize the potential of AI while reducing the possibility of information breaches and cyberattacks. With the further development of AI technologies, the security of the systems that protect the information also must change, and the progress in the field of medicine should not be undermined by the inherent gaps in the system. 3. Methodology The research methodology described in this section will be used to design and assess AI-enabled leukemia genomic data analysis cyberspace frameworks. To achieve the desired outcomes, the research was designed to answer the following questions: (1) what are the most critical cybersecurity threats and issues related to AI-based genomic data analysis, (2) how well current cybersecurity frameworks perform, (3) how an improved cybersecurity framework can be developed by using AI and blockchain technology, and (4) how feasible and relevant is the implementation of such solution in the healthcare context. In order to accomplish these goals, qualitative and quantitative research method was employed. The research design was the literature review, analysis of the case study, collection of data by use of surveys and interviews, and the formulation and validation of a conceptual cybersecurity framework. Each of the above elements has been detailed in the methodology section and they include selection of the participants, data collection process, data collection tools and method of analysis. 3.1. Research Design and Approach. The research strategy that was used in this study was a mixed-method research design, which combined qualitative and quantitative data-collection methods to cover a holistic analysis. The qualitative aspect consisted of the critical review of the literature on the subject to interpret the theoretical and practical challenges in cybersecurity to analyse AI-based genomic data. The quantitative part involved the collection of empirical data by surveys and interviews with medical staff, IT specialists, and cybersecurity specialists to collect information on the current practice and perceived risk. The general design was on a three stage process: 3.2. Phase 1: Theoretical Framework Development and Literature Review. The preliminary stage involved an extensive literature review that sought to find out the available cybersecurity frameworks and AI use in genomic data analysis. This review identified the weaknesses of the existing models and the necessity of the better models, especially, those including the blockchain technology. 3.2.1. Phase 2: Data Collection The second stage was the collection of primary data by use of surveys and interviews. Oncologists, geneticists and information technology specialists were focused on to understand what is real practise and concern in the cybersecurity field while analyzing genomic data. One hundred and fifty people were surveyed and fifteen face-to-face interviews with healthcare personnel and cybersecurity professionals were conducted. These methodologies aimed to identify the major risks, challenges and the potential solutions that are perceived by the stakeholders in the healthcare system in terms of the use of artificial intelligence for the analysis of genomic data. 3.2.2. Phase 3: Drafting and Testing Cybersecurity Framework. The third stage involved the creation of a sophisticated cyber security system based on integrated technologies of artificial intelligence, machine learning and blockchain technologies. The proposed framework was simulated and empirically evaluated using scenario-based case studies to evaluate its potential effectiveness in real life healthcare settings. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 870 3.3. Data Collection Methods 3.3.1. Surveys Quantitative data including the current status of cybersecurity in genomic data analysis was gathered by sending questionnaires to oncologists, geneticists, data scientists, and information technology specialists. The survey included questions about the participant's experience with artificial intelligence (AI) in healthcare, and awareness of the current cybersecurity practices, and perception of risks and benefits that came with adopting blockchain technologies for genomic data systems. The questionnaire was divided into four different sections: • Demographic Data: This section featured items relating to the participants' professional role, years of experience, healthcare and cybersecurity education. • Cybersecurity Awareness: Items in this section explored the participants' awareness of cybersecurity risks associated with the analysis of genomic data • AI Introduction in Healthcare: This section looked to examine participants' experiences of the implementation of AI in their respective professions and the applications of AI to genomic data analysis. • Perceived Barriers and Solutions: Items in this section centered on challenges faced by participants in implementing secure AI solutions, and solutions to potential remedies for cybersecurity problems such as through use of blockchain or anomaly detection. The questionnaire used a Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree) where respondents were asked to show the extent to which they agreed with each statement. This methodological decision was made on the basis of acquiring data that could be easily quantified and analyzed. 3.4. Interviews The smaller group of professionals was subjected to in-depth interviews that would help retrieve qualitative information about their views on the security issues related to AI-enabled analysis of genomic data. The sample was chosen according to the qualification in the medical field and the field of cybersecurity. The interviews were planned to collect in-depth answers to particular issues in relation to cybersecurity, such as encryption, access control, and the possibility of introducing blockchain technology. All the interviews took not more than 30 to 45 minutes and their answers were recorded and transcribed to be analyzed later. The questions to be asked during interviews were: • What do you consider are the cybersecurity threats of AI-based genomic data analysis? • What is your current method of securing genomic information in your practice? • How do you know about the blockchain technology regarding the security of healthcare data? • What are the challenges encountered by you in terms of applying cybersecurity frameworks in your institution? • What is your opinion about the possibilities of AI and blockchain to improve genomic data protection? 3.5. Case Studies A series of case studies was designed in order to evaluate the feasibility of the proposed cybersecurity framework by means of realistic situations. These case studies were set in hypothetical healthcare organizations, in which artificial intelligence was implemented to analyze genomic data. The aim was to compare the effectiveness of different cybersecurity solutions in the context of healthcare for the protection of genomic data. 3.6. Data Analysis The quantitative responses of the surveys were analysed using correlation analysis and descriptive statistics. This discussion provided several general trends in the responses, such as the level of cybersecurity awareness among the medical workers and the perception of risk associated with AI in genomic data analysis. The qualitative data obtained from the interviews were analyzed with the use of thematic analysis that assumed the identification of recurrent themes that offered a glimpse into the challenges of including secure AI systems. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 871 3.7. Framework Development of Cybersecurity. The improved cybersecurity framework was developed, relying on the results of the literature review, surveys, and interviews. The framework was developed to solve the main problems that were determined in the research such as the encryption of data, access control, and real-time detection of anomalies. The following elements have been included in the framework: • Encryption: effective encryption procedures of securing genomic information when transmitting and storing it. • Access Control: Role-based and attribute-based access controls to limit access to unauthorized access of genomic data. • Anomaly Detection: AI-based anomaly detection systems that are able to detect suspicious activity and possible break-ins in real-time. • Blockchain Implementation: Deceitful method of securing data integrity and immutability to minimise potential risks of manipulating genomic data. This theoretical framework was later tested using simulation, which used diverse situations to simulate breaches of data, unauthorized access as well as an effort to alter genomic data. The success of the framework in reducing such risks was determined on the result of such simulations. Figure 2 Overview of the Cybersecurity Framework for AI-Enabled Genomic Data Analysis Table 2 Key Elements of the Cybersecurity Framework Cybersecurity Element Purpose Implementation in Genomic Data Analysis Encryption Protects genomic data during storage and transmission by converting it into an unreadable format. AES-256 encryption for genomic data to prevent unauthorized access during transmission. Access Control Restricts access to genomic data based on user roles or attributes. Role-based access control (RBAC) for healthcare professionals accessing genomic data. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 872 Anomaly Detection Identifies suspicious activities and potential breaches in real-time. AI-powered systems that flag unusual patterns in genomic data access. Blockchain Integration Decentralized ledger for securing genomic data and ensuring data integrity. Use of blockchain to ensure that once data is entered, it cannot be altered or deleted. 3.8. Ethical Considerations This study was performed following the ethical guidelines of the related Institutional Review Boards (IRB). All respondents to the surveys and interviews were briefed about the purpose of the study and how their data will be used; they were also clearly informed that they have the right to withdraw from the study at any time without any negative consequences. 4. Results This section introduces the findings obtained from the data gathered via surveys, interviews and case studies. The results are analysed with the aim of shedding light on the current state of cyber-security in genomic data analysis in leukaemia when using AI-based methods and also to assess the effectiveness of the proposed cyber-security systems. In addition, insights in the data were also gained as it related to the views of healthcare professionals, cybersecurity experts, and information technology specialists regarding risks, barriers, and possible solutions to reduce the risk of genomic data loss in AI applications. Further, the results of testing the improved cybersecurity framework are reported, highlighting the applicability of the framework to modern healthcare practices. 4.1. Findings: Survey Results: Genomic Data Analysis Cybersecurity Perceptions. The respondents were a total of 150 medical practitioners oncologists, geneticists, IT specialists and cybersecurity experts. The main goal of the survey was to understand the level of awareness about the potential cybersecurity threats related to the analysis of genomic data with the help of AI and to define the perceived efficiency of current security practices. 4.2. Key Survey Findings 4.2.1. Cybersecurity Awareness: • 85 percent of the respondents identified that they recognize the cybersecurity risks of analyzing genomic data. • The confidence of healthcare professionals about the current cybersecurity measures implemented in their institutions to safeguard genomic information was only 45 per cent. • It shows that there is a very wide distance between the awareness and trust in current security frameworks, and it can be assumed that most institutions do not have strong cybersecurity policies. • Perceived Risks: • Data breaches (78 keine), adversarial attacks on AI models (64 im"), and data integrity were the most frequently perceived threats of AI in genomic data analysis (58 im ). • The possibility of malicious insiders gaining access to genomic data was a major concern among the respondents (52 3). • 63% of the respondents said that the rising nature of AI and machine learning in the healthcare system intensifies such cybersecurity threats. 4.2.2. Current Security Practices • 70 percent of the respondents reported that their institutions used simple forms of encryption when protecting genomic data. • Nonetheless, 50% used high level role-based access control (RBAC) and attribute-based access control (ABAC) to control access to data. • Only 20 percent of the surveyed institutions indicated that they used blockchain technology to protect their data, with most of them citing the complexity and high cost of implementation as the obstacle. World Journal of Advanced Research and Reviews, 2025, 28(01), 865-879 873 4.2.3. Eagerness to go through with Blockchain Integration 72 per cent of responding parties expressed interest in learning about blockchain technology to improve the security of genomic data, and the majority of them acknowledged that blockchain technology can be used to guarantee the integrity and transparency of the data. 4.3. Interview findings Expert opinions on AI and cybersecurity. A purposive sample of fifteen professionals representing the fields of healthcare, information technology and cybersecurity were subjected to in depth semi-structured interviews. The main goal of these interviews was to obtain in-depth qualitative information relevant to the practical issues and mitigation strategies related to the protection of genomic data that is AI driven. 4.4. Key Interview Insights Scholars have highlighted the susceptibility of AI models to adversarial attacks, which can be done by altering input data with the aim of altering the predictions and results. This problem has been recognized as critical, since AI systems are often treated as "black box" making it difficult for harmful changes to be detected and traced. Experts also note that the high volume and heterogeneity of genomic data makes it being an attractive and high-value target for cybercriminals, especially as technologies in the digital health field continue to flourish. 4.4.1. Role of Blockchain A number of interviewees emphasize that blockchain was a promising option to provide data integrity and avoid illegal manipulation of genomic data. With the help of smart contracts and decentralized registers, blockchain can provide verifiable and unchangeable data access records and modifications. Nevertheless, the difficulties connected with the scalability of blockchain technology and regulatory compliance were also noted as the genomic data systems need high throughput rates and need to comply with severe data privacy laws like HIPAA. Access Control: This is the control mechanism used to authorize user access to devices, files, or data in the network.<|human|>Access Control: This refers to the control mechanism that is used to authorize a user in accessing the devices, files, or data in the network. Interviewees highlighted the need to have sophisticated access control systems to limit the number of people who can access sensitive genomic information. A number of specialists proposed to add AI-supported anomaly detection as a way of detecting unusual access patterns, or unauthorized manipulation of data. Anomaly detection models were considered to be important to the improvement of real-time monitoring of AI models and to the identification of possible cybersecurity threats to prevent their occurrence. 4.5. Case Study Findings How to Simulate AI-powered Genomic Data Analysis using Cybersecurity Frameworks. The case studies were built on the hypothetical healthcare institutions that have already incorporated AI into their workflow of genomic data analysis. These case studies enabled the research team to evaluate the practicability and efficiency of the suggested cybersecurity framework in the real-life context. 4.5.1. Case Study 1: Genomic Data Analysis in the absence of Enhanced Security Framework. The situation here is that the leukemia genomic data was analyzed with the help of an AI model, and no sophisticated cybersecurity measures were present. One of the breaches was to do with an unauthorized user accessing the system where the data was manipulated to change treatment recommendations. This led to the system giving false forecasts and this may have benefited patients badly. The absence of access control mechanism and encryption made it not difficult to obtain unauthorized access, and the absence of the anomaly detection systems prolonged the realization of breach. Resultant outcome Patient safety was endangered and the system was compromised.