AI-driven threat detection in pharmaceutical R and D: Mitigating cyber risks in drug discovery platforms

 Corresponding author: Rama Devi Drakshpalli 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. AI-driven threat detection in pharmaceutical R and D: Mitigating cyber risks in drug discovery platforms Rama Devi Drakshpalli * Independent Researcher, North Carolina, USA. Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 Publication history: Received on 12 April 2025; revised on 29 May 2025; accepted on 01 June 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.23.3.0176 Abstract The integration of Artificial Intelligence (AI) into pharmaceutical research and development (R&D) has transformed drug discovery, biomarker identification, and clinical trial automation, significantly reducing costs and expediting breakthroughs. However, the increasing reliance on AI-driven processes exposes pharmaceutical R&D to evolving cybersecurity threats, including adversarial AI manipulations, ransomware attacks, and AI poisoning. To address these challenges, this study explores AI-driven cybersecurity solutions, with a focus on machine learning-based Intrusion Detection Systems (IDS) capable of identifying anomalies in AI-generated predictions. Furthermore, it examines the role of federated learning in securing sensitive research data and proposes a national AI security framework aligned with the Cybersecurity and Infrastructure Security Agency (CISA) directives. By leveraging AI-powered anomaly detection, deep learning models, and automated incident response, organizations can enhance their resilience against sophisticated cyber threats. Despite these advancements, challenges such as algorithmic bias, false positives, and adversarial vulnerabilities persist. Keywords: Artificial Intelligence (AI); Pharmaceutical R&D; Cybersecurity; Intrusion Detection Systems (IDS); Federated Learning; Anomaly Detection 1. Introduction AI-powered drug discovery platforms have significantly enhanced pharmaceutical R&D, enabling faster and more costefficient innovation. However, the rapid adoption of AI has introduced new cybersecurity challenges that threaten data integrity, intellectual property, and regulatory compliance. As pharmaceutical organizations increasingly rely on AIdriven processes for drug discovery, biomarker identification, and clinical trial management, they become prime targets for cyber threats such as adversarial AI manipulations, ransomware attacks, and AI poisoning. The convergence of AI and cybersecurity is critical in safeguarding sensitive research data and ensuring the reliability of AI-generated predictions. Traditional security measures are often inadequate against sophisticated cyber threats that exploit vulnerabilities in machine learning models. Consequently, the need for AI-driven security solutions has become paramount in mitigating emerging risks and ensuring the integrity of pharmaceutical research [24], [26]. Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 49 Table 1 AI and machine learning models in the pharmaceutical industry AI/Machine Learning Models Description/Usage References Generative Adversarial Networks (GANs) GANs are widely used in drug product development to generate novel chemical structures and optimize their properties. GANs consist of a generator network that creates new molecules and a discriminator network that evaluates their quality, leading to the generation of structurally diverse and functionally optimized drug candidates. [1] Recurrent Neural Networks (RNNs) RNNs are commonly employed for sequence-based tasks in drug development, such as predicting protein structures, analyzing genomic data, and designing peptide sequences. They capture sequential dependencies and can generate new sequences based on learned patterns. Pharmaceutics 2023, 15, 1916 8 of 46 [2] Convolutional Neural Networks (CNNs) CNNs are effective in image-based tasks, including analyzing molecular structures and identifying potential drug targets. They can extract relevant features from molecular images and aid in drug design and target identification [3] , [4] Long Short-Term Memory Networks (LSTMs) LSTMs are a type of RNN that excel in modeling and predicting temporal dependencies. They have been used in pharmacokinetics and pharmacodynamics studies to predict drug concentration-time profiles and evaluate drug efficacy. [3], [4] Transformer Models Transformer models, such as the popular BERT (Bidirectional Encoder Representations from Transformers), have been employed in natural language processing tasks in the pharmaceutical domain. They can extract useful information from the scientific literature, patent databases, and clinical trial data, enabling researchers to make informed decisions in drug development. [5] Reinforcement Learning (RL) RL techniques have been applied to optimize drug dosing strategies and develop personalized treatment plans. RL algorithms learn from interactions with the environment to make sequential decisions, aiding in dose optimization, and improving patient outcomes. [6] Bayesian Models Bayesian models, such as Bayesian networks and Gaussian processes, are employed for uncertainty quantification and decision-making in drug development. They enable researchers to make probabilistic predictions, assess risks, and optimize experimental designs. [7], [8] Deep Q-Networks (DQNs) DQNs, a combination of deep learning and reinforcement learning, have been used to optimize drug discovery processes by predicting the activity of compounds and suggesting high-potential candidates for further experimentation. [9], [10] Autoencoders Autoencoders are unsupervised learning models used for dimensionality reduction and feature extraction in drug development. They can capture essential characteristics of molecules and assist in compound screening and virtual screening. Advanced bioinformatics platforms also integrate AI-based molecular modeling tools for medicinal biology, further improving compound profiling and screening processes [11], [12], [16] Graph Neural Networks (GNNs) GNNs are designed to process graph-structured data, making them suitable for drug discovery tasks that involve molecular structures. They can model molecular graphs, predict properties, and aid in virtual screening and de novo drug design. Additionally, in silico protein design approaches leveraging AI have enabled efficient virtual screening of therapeutic compounds, enhancing the early stages of drug discovery [13], [14], [15] Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 50 1.1. Problem Statement AI models in pharmaceutical research are vulnerable to adversarial attacks, such as model poisoning, where malicious data manipulates drug predictions. These threats can lead to faulty molecular structures, incorrect biomarker identification, and misclassified drug candidates, potentially endangering public health. As these threats continue to evolve, traditional security approaches are often inadequate. The complexity and volume of AI-generated data demand advanced cybersecurity frameworks capable of detecting and mitigating malicious activities in real time. Recent studies indicate a rising frequency of targeted cyberattacks on AI-centric pharmaceutical IT systems, highlighting the need for resilient and adaptive security models [24]. Strengthening these frameworks is essential to ensure the safety, reliability, and trustworthiness of AI-driven pharmaceutical innovation. 1.2. Objectives This paper aims to examine AI-driven cybersecurity solutions in response to the growing integration of Artificial Intelligence in pharmaceutical research and development. First, it analyzes evolving cybersecurity vulnerabilities within AI-enabled pharmaceutical ecosystems. Reports such as "Artificial Intelligence: Cybersecurity Threats in Pharmaceutical" (ResearchGate) and "R&D under Siege" (RD World Online) highlight how increased AI reliance expands the attack surface, exposing firms to data breaches, ransomware, intellectual property theft, and insider threats. The 2020 ransomware attack on a major pharmaceutical company, which disrupted drug discovery operations, underscores the urgency of robust security strategies. Second, the study proposes using AI-based Intrusion Detection Systems (IDS) as a proactive defense. Studies from NSF and “A Comprehensive Review of AI-Based Intrusion Detection Systems” (ResearchGate) compare machine learning, deep learning, and reinforcement learning techniques in healthcare cybersecurity, emphasizing intelligent IDS for timely threat detection and mitigation. Third, it explores federated learning as a method for securing AI models. Articles from PubMed Central and “Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development” (Preprints) show how federated learning enables privacy-preserving AI training across distributed datasets, ensuring compliance with data governance while supporting collaborative research. Finally, the paper outlines the necessity of a national AI security framework tailored to pharmaceutical R&D. Guidance from the Federal Register's "Framework for Artificial Intelligence Diffusion" and the NIST AI Risk Management Framework supports standardized protocols to foster trust, compliance, and secure deployment of AI technologies in drug development. Together, these objectives address the intersection of AI advancement and cybersecurity in pharmaceutical innovation. 2. Literature review 2.1. Review of Existing Research In recent years, a growing body of research has explored the transformative impact of Artificial Intelligence (AI) on various sectors, particularly in drug discovery and pharmaceutical research and development (R&D). AI has demonstrated its potential to accelerate drug discovery by predicting molecular interactions, identifying potential drug candidates, and optimizing clinical trial processes. Numerous studies have explored machine learning (ML) models, deep learning algorithms, and natural language processing techniques in analyzing vast datasets to generate novel pharmaceutical compounds, reduce the time to market, and lower costs. For instance, an article by Roy et al. (2022) examines the role of AI in pharmaceutical technology and drug discovery, offering a broad overview of AI-driven advancements in optimizing drug formulations and drug discovery processes (Roy et al., 2022). Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 51 Figure 1 AI-Driven Solutions for Key Challenges in the Pharmaceutical Industry Figure 1 Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. However, a significant issue accompanying the adoption of AI and machine learning in sensitive and critical industries such as pharmaceuticals is the increasing exposure to cybersecurity threats. Researchers have highlighted the cyber risks associated with AI implementation, especially in sectors handling sensitive health data. Studies focusing on cybersecurity threats to AI systems have mainly cantered on the threats arising from adversarial attacks that manipulate AI models or compromise data integrity. Companies Leveraging AI and ML Technologies in Pharmaceutical Research and Development [17] Table 2. Leading Companies and Platforms Utilizing AI/ML Technologies in Pharmaceutical Research and Development Sr. No. Domain Technology and Outcome Industry and Collaborations 1 Drug design Novel therapeutic antibodies Exscientia 2 Molecular drug discovery Atom Net–a deep learning-driven computational platform for structure-based drug design AtomWise Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 52 3 Gene mutation related disease Machine learning based recursion operating system for biological and chemical datasets Recursion 4 Drug design A ligandand structure-based de novo drug design, especially in multiparametric optimization Iktos 5 Drug discovery Generative modeling AI technology Iktos and Galapagos 6 Drug development Potential preclinical candidates Iktos and Ono Pharma 7 Drug design Rapid drug design by software “Makya” Iktos and Sygnature Discovery 8 Drug discovery and Drug development Pharma.AI, PandaMics, ALS.AI Insilico Medicine 9 Drug target and Drug development ChatPandaGPT Insilico Medicine 10 Drug development Protein motion in drug development lie RLY-4008 (Novel allosteric, pan mutant and isoform selective inhibitor of PI3Kα Relay therapeutics 11 Drug discovery AI and machine learning for selection of drug target BenevolentAI 12 Drug target Drug target selection for chronic kidney disease and idiopathic pulmonary fibrosis BenevolentAI and AstraZeneca, GlaxoSmithKline, Pfizer 13 Clinical trials AI in clinical trials Pfizer and Vysioneer 14 Disease treatment AI and supercomputing for oral COVID-19 treatment Paxloid Pfizer 15 Drug discovery NASH drugs and sequencing behemoth Illumina AstraZeneca and Viking therapeutics 16 Drug development Trials360.ai platform in clinical trials for site feasibility, site engagement and patient recruitment Janssen 17 Drug research Automate medical literature review by using natural language processing Sanofi 18 Drug development AI in drug development BioMed X and Sanofi 19 Drug research and drug development AI empowerment and AI exploration platforms Novartis and Microsoft 20 Drug discovery AI drug discovery platform Bayer For example, Zhao et al. (2021) explored the concept of signature-based intrusion detection systems (IDS) utilizing machine learning and deep learning algorithms to enhance cybersecurity and prevent adversarial attacks (Zhao et al., 2021). While much attention has been paid to general AI-driven cybersecurity strategies and machine learning for threat detection, there is a noticeable gap in the literature addressing the specific risks that target AI-driven pharmaceutical R&D systems. Such systems are often involved in handling proprietary drug discovery data and sensitive clinical trial information, making them prime targets for adversarial attacks, data breaches, and cyber sabotage. Furthermore, real - world implementations of machine learning-based IDS in drug discovery systems are still limited, with few studies providing comprehensive frameworks for deploying such models in these sensitive contexts. A study by Singh et al. (2023) demonstrated how machine learning-based intrusion detection can be used in IoT environments, but the application of these IDS techniques in drug discovery R&D environments has yet to be fully explored (Singh et al., 2023). Moreover, current research has mostly examined AI security in more general terms or in unrelated domains like finance or manufacturing. The adaptation of cybersecurity models to the unique challenges posed by pharmaceutical R&D, Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 53 particularly those involving proprietary information and compliance with regulatory bodies (such as HIPAA, FDA, etc.), is underexplored. Furthermore, Kaur and Sharma (2022) focus on the development of a deep learning-based intrusion detection system, shedding light on how adversarial machine learning can challenge existing IDS models and the need for systems resilient to these evolving threats (Kaur & Sharma, 2022). Multi-label approaches have also been employed to enhance the specificity of AI-driven target prediction, particularly in cases involving ligand promiscuity [18]. Emerging chemogenomics strategies now employ AI for in silico target fishing, enabling researchers to better anticipate off-target effects and drug repurposing opportunities [19]. 2.2. Identified Gaps Based on current research, several critical gaps remain that require further exploration: Lack of Comprehensive AI-driven Cybersecurity Frameworks Tailored to Pharmaceutical R&D: Despite the importance of protecting AI-driven pharmaceutical R&D systems, there is a lack of well-defined cybersecurity frameworks specifically designed for these environments. Existing frameworks tend to be generalized for AI systems in industrial or enterprise settings and do not consider the nuances of pharmaceutical R&D systems, which often require specialized protections. Research is needed to create robust frameworks that can address the unique challenges of ensuring data integrity, protecting intellectual property, and preventing adversarial interventions in drug discovery pipelines. Limited Studies on Real-world Implementations of Machine Learning-based IDS in Drug Discovery: Intrusion detection systems (IDS) based on machine learning have shown promise in detecting cybersecurity breaches in various sectors. However, there remains a significant gap in real-world applications of these systems within the context of drug discovery and pharmaceutical R&D. Machine learning models used in these fields often handle vast and complex data, making the design and implementation of effective IDS for AI-driven drug discovery systems a unique challenge. More research is needed to evaluate and deploy these IDS systems in real-world settings to assess their efficacy and scalability. Insufficient Exploration of Federated Learning for Securing Multi-Institutional AI Models: Federated learning, a type of machine learning where multiple institutions collaborate without sharing sensitive data, has emerged as an innovative solution for securing multi-institutional AI models. While federated learning holds promise for fields like healthcare, where institutions wish to collaborate without compromising patient privacy, there is insufficient exploration of this technique in the context of pharmaceutical R&D. Collaborative drug discovery across different institutions can be vulnerable to adversarial attacks, data poisoning, and other cybersecurity threats. Research on how federated learning can be effectively applied to multi-institutional AI models in drug discovery is crucial for advancing secure AI-driven pharmaceutical research and development. 2.3. High-Level Solution Approach As Artificial Intelligence (AI) becomes foundational to pharmaceutical research and development (R&D), the sector is increasingly vulnerable to sophisticated cyber threats that target sensitive data, AI model integrity, and regulatory compliance. Recent findings reveal that AI-centric pharmaceutical IT systems face a higher frequency of targeted attacks, necessitates resilient cybersecurity models [24]. To address this evolving threat landscape, this paper proposes a tripartite cybersecurity strategy [25]. First, the deployment of AI-Driven Intrusion Detection Systems (IDS), leveraging machine learning and deep learning algorithms, can detect anomalous behaviors, adversarial attacks, and unauthorized access in real-time. These systems utilize techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) to identify tampered molecular structures and deviations in predictive model outputs. For instance, during the COVID-19 pandemic, adversarial manipulations in drug discovery datasets led to inaccurate AI predictions—an issue that a robust IDS framework could have prevented [27], [28]. Second, Federated Learning (FL) is introduced as a decentralized AI training approach that enhances data privacy by keeping sensitive information localized while enabling collaborative model development across institutions. By incorporating secure multi-party computation (SMPC) and homomorphic encryption, FL ensures compliance with data protection regulations such as HIPAA, GDPR, and FDA standards. The healthcare use case of Google’s federated learning illustrates how decentralized AI can support privacy-preserving research without centralizing patient data [29], [30]. Third, the establishment of a National AI Security Framework, aligned with CISA’s cybersecurity directives and the NIST AI Risk Management Framework, is proposed to standardize cybersecurity policies across pharmaceutical AI applications. This framework advocates for zero-trust architectures, real-time threat intelligence sharing, and the formation of a national AI cybersecurity task force. Analogous to the European Medicines Agency’s (EMA) AI security guidelines, such a U.S.-based regulatory model would support consistent, secure, and innovation-driven pharmaceutical R&D [31], [32]. Together, Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 54 these three pillars—AI-driven IDS, federated learning, and national regulation—form a comprehensive defense strategy to safeguard the integrity, privacy, and reliability of AI-driven drug discovery and clinical development processes. 2.4. Detailed Solution or Methodology To secure AI-driven pharmaceutical research and development (R&D) against evolving cyber threats, this study adopts a multi-layered methodology comprising AI-driven Intrusion Detection Systems (IDS), federated learning for secure AI model training, and a national AI security framework. First, AI-driven IDS are employed using Deep Reinforcement Learning (DRL) models that adapt dynamically to sophisticated attack vectors. Techniques such as Markov Decision Processes (MDPs) allow the system to learn optimal defense strategies in real-time, while multi-agent reinforcement learning (MARL) ensures coordinated threat response across distributed pharmaceutical networks. Anomaly detection models including autoencoders and recurrent neural networks (RNNs) are trained to identify deviations in AI model behavior, with adversarial training enhancing robustness against manipulated inputs. A hybrid detection framework, combining supervised (e.g., decision trees, SVM) and unsupervised (e.g., clustering, PCA) learning, is proposed to improve detection of both known and novel threats. For instance, in 2022, an AI-driven IDS successfully mitigated a ransomware attack on a clinical trial system by identifying anomalies before encryption could occur [33]. As highlighted by Hindy (2021), machine learning and deep learning methodologies significantly elevate IDS effectiveness across healthcare environments [34]. Second, Federated Learning (FL) is leveraged for privacy-preserving model training. As demonstrated by Quach, federated learning implementations in healthcare settings enable collaborative AI development without compromising patient confidentiality [22]. Homomorphic encryption techniques integrated into federated learning frameworks have shown promise in safeguarding biomedical AI pipelines [23]. By implementing Secure Aggregation (SecAgg) and differential privacy techniques, FL enables decentralized AI training while maintaining strict data confidentiality. This model permits pharmaceutical firms to collaboratively develop predictive tools without transferring raw patient or research data across institutions. Encryption methods such as homomorphic encryption further safeguard federated data exchanges. Notably, Pfizer and Moderna adopted FL to expedite COVID-19 vaccine R&D while ensuring compliance with global privacy standards [35]. Research by Quach (2020) validates that federated learning not only preserves data integrity but also enhances clinical AI model performance and compliance [36]. Third, a National AI Security Framework is proposed, aligned with the CISA AI Risk Management Framework, to unify cybersecurity protocols across pharmaceutical AI systems. This includes developing AI regulatory guidelines in collaboration with the FDA, EMA, and ISO bodies. The framework incorporates Zero-Trust Architecture, enforcing rolebased access control (RBAC), multi-factor authentication (MFA), and real-time threat intelligence integration. Additionally, a centralized incident response unit is recommended for active monitoring and response to AI-specific cyber threats, supported by a pharmaceutical threat intelligence-sharing network. A precedent exists in the collaboration between the U.S. Department of Health and Human Services (HHS) and AI researchers, where secure deployment strategies were outlined for AI-driven medical research [37]. Further reinforcement comes from Wang (2023), who explored the application of homomorphic encryption in federated learning environments to enhance secure AI deployment frameworks [38]. 3. Results and Analysis 3.1. Observations from AI-driven IDS Implementation The deployment of Artificial Intelligence (AI) within Intrusion Detection Systems (IDS) in the pharmaceutical industry has delivered significant enhancements in both cybersecurity posture and data reliability. One of the most notable outcomes was the substantial reduction in false drug candidate classifications. Traditional computational methods often misclassify potential compounds, leading to costly delays and inaccuracies in drug discovery. However, the integration of deep learning models into the IDS framework allowed for improved detection of inconsistencies in complex pharmaceutical datasets, minimizing such classification errors [39]. Moreover, the AI-enabled IDS demonstrated robust capabilities in the early detection of adversarial attacks and data manipulations. These threats, commonly orchestrated through the injection of deceptive inputs to compromise AI model integrity, were identified proactively, thereby mitigating damage before any major breaches could occur [40]. A compelling case study from a global pharmaceutical enterprise highlighted that AI-driven IDS improved ransomware detection efficiency by 40% through real-time behavioral and anomaly detection techniques, intercepting threats before the ransomware could initiate its encryption cycle [41]. In addition, continuous real-time monitoring of AI model integrity enabled by the IDS contributed to a 30% reduction in unauthorized data alterations. This ensured that sensitive research data remained intact, preserving its scientific validity and compliance with data governance standards [42]. Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 55 Illustration 1: AI-driven IDS Workflow Diagram (This diagram should visually depict how AI-driven IDS identifies and mitigates cyber threats in pharmaceutical R&D, highlighting real-time monitoring, anomaly detection, and response mechanisms.) Figure 2 AI-Integrated Intrusion Detection and Response System Workflow in Cybersecurity Infrastructure 3.2. Comparative Analysis with Traditional Cybersecurity Approaches Illustration 2 Figure 3 Comparative Analysis of Traditional Security Systems vs. AI-Integrated Security in Cyber Threat Detection and Response Global Journal of Engineering and Technology Advances, 2025, 23(03), 048–062 56 To evaluate the effectiveness of AI-driven Intrusion Detection Systems (IDS), a comparative analysis was performed against conventional cybersecurity mechanisms. The findings clearly highlight the superior performance of AI-driven systems in managing today’s sophisticated cyber threats. Notably, AI-driven IDS demonstrated a 50% improvement in response time when detecting and mitigating attacks, significantly outpacing traditional rule-based security systems. This acceleration is largely due to the self-learning capabilities of AI models, which continuously refine their detection strategies by adapting to evolving threat vectors without human intervention [43]. Furthermore, while conventional systems depend heavily on static rules and known signatures, AI-driven IDS solutions showcased dynamic threat adaptation. These systems evolved autonomously to identify and neutralize previously unknown attack patterns, a critical advantage in defending against zero-day exploits and advanced persistent threats [44]. The comparison, as depicted in Illustration 2, underscores the transformative role of AI in advancing cybersecurity resilience in pharmaceutical and other high-risk domains. 3.3. Benefits and Impact 3.3.1. Advantages The adoption of AI-driven Intrusion Detection Systems (IDS) has introduced transformative benefits to cybersecurity in the pharmaceutical sector, particularly in safeguarding the integrity of drug discovery and research operations. One of the foremost advantages is the enhanced security of AI-driven drug discovery platforms. These systems effectively monitor and detect unauthorized access, data breaches, and advanced cyber threats in real-time, thereby protecting high-value research assets and models from tampering or theft [45]. Furthermore, AI-driven IDS have been instrumental in reducing the frequency and severity of cybersecurity incidents affecting pharmaceutical R&D infrastructures. By proactively identifying and mitigating threats before they can escalate, these systems ensure the continuity and safety of experimental and clinical workflows [46]. Additionally, AI-driven IDS support compliance with critical regulatory frameworks such as GDPR and HIPAA by ensuring the security and traceability of AI training datasets and research outputs, which is crucial for audit readiness and legal assurance [47]. Finally, the implementation of robust AI-powered security protocols has fostered increased trust among research institutions, enabling secure data sharing and collaborative research initiatives across organizations. This strengthened inter-institutional cooperation plays a vital role in accelerating innovation while maintaining strict data confidentiality standards [48]. Illustration 3: AI-Driven Cybersecurity Benefits in Pharma (This diagram should illustrate the role of AI-driven IDS in securing pharmaceutical research and drug discovery pipelines.) Figure 4 AI Integration Across the Drug Development Lifecycle: From Target Identification to Personalized Medicine 3.4. Applications AI-driven Intrusion Detection Systems (IDS) have emerged as a critical enabler of cybersecurity across multiple facets of pharmaceutical research and innovation. One of the most impactful applications is in the domain of secure AI-driven drug discovery and biomarker identification, where IDS solutions help safeguard sensitive datasets and machine learning models. Major pharmaceutical organizations, including Pfizer and Moderna, have integrated AI-driven IDS to protect their vaccine development platforms and ensure the authenticity of biomarker identification pipelines [49]. Another significant application lies in the protection of intellectual property (IP), which is highly vulnerable during early-stage R&D. AI-enabled IDS systems assist in securing proprietary formulations, patent drafts, and experimental data from cyber intrusions. AstraZeneca’s deployment of AI-driven IDS during its COVID-19 vaccine development phase serves as a prominent example of thwarting state-sponsored cyber espionage [50]. Moreover, these IDS technologies play a strategic role in bolstering national cybersecurity postures by shielding federally funded pharmaceutical research initiatives from digital threats. The U.S. National Institutes of Health (NIH), for instance, has partnered with AI vendors