The current study suggests the national framework based on the concept of AI-enabled cyber threat intelligence systems. The increased risk of digital warfare and the shortcomings of the traditional patterns of cybersecurity. The use of artificial intelligence in the early warning of such threats, real-time response, and superior data analysis. It is a crucial concern to national security. The study will determine the contribution of AI-driven CTIS to the improvement of cyber defense capabilities in government and defense. It is critical infrastructure sectors and determines factors that affect these capabilities. The descriptive research design was used, and information was gathered using structured surveys that were administered to 300 cybersecurity practitioners in large and small firms. The findings indicate the maturity of an AI system, and the greater the level of automation in detecting the threat, the better the detection accuracy and the faster the incident response time. A multivariate regression model revealed that there was a positive relationship regarding the independent variables and the effectiveness of AI-CTIS, checking in with an R² = 0.76 and a p < 0.001. Inter-agency cooperation and learning of workforce skills were defined as the most crucial ingredients in the streamlining of CTIS performance. The research project has not overlooked some of the major challenges, which include the failure to standardize, poor policy infrastructure, and interoperability. The national cybersecurity approach is the proactive defense strategy. Companies remain digitally resilient in the long-term perspective. It is high quotients of targeted cyber threats that are changing in detail and quantity.

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security.

The evolving complexity of drug discovery and the demand for focused therapeutics have given further impetus to the implementation of AI and data science-based approaches. Such methods can analyze genomic and pharmacological data more rapidly, facilitating the precision design of drugs and genetic biomarkers of such drugs. This work is a machine learning framework that seeks to predict drug responses and find genetic biomarkers in the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Numerous data cleansing, normalization and one-hot encoding were also done to maintain credibility in the analysis. The robustness of a Random Forest classifier on the processing of high-dimensional biological data and excellent predictive performance was also demonstrated, with 97.7% accuracy, 98.4% precision, recall, and F1-score. The comparative studies provided better results than other models like SVM 95%, BiLSTM 80%, and GATv2 77.9%. The discriminative power of the model was proved using ROC and precision-recall curves. The framework of AI + data science in pharmacogenomics can be used to identify patterns of drug sensitivity efficiently, and, thus, promote personalized medicine and biomarker-based treatments. The method is a scalable, interpretable, and time-saving alternative to the traditional pipelines of drug discovery.

As organizations quickly become increasingly digital, they are experiencing escalating, complex cyber threats. These challenges make cybersecurity an important area for managers and governance, and not just a technical problem. Even if you are a heavy investor in security tech, it still happens to many companies to suffer from cyber. The reasons are broken information flows, a lack of clear visibility among managers, and misalignment between the security operations and the overall decision-making. This research rethinks Artificial Intelligence (AI) based Cyber Threat Intelligence (CTI) as a Management Information System (MIS). It combines the approach of cybersecurity governance and IT project management to increase the resilience of organizations. Using MIS theory, cybersecurity governance models, and studies of IT project management, the paper derives one cohesive model for translating raw threat data into useful managerial insight. Through a design science methodology, the research chooses a lot of scholarly sources and demonstrates a layered AI-enabled CTI-MIS architecture. This is good architecture for strategic oversight, risk-based governance, and flexible project execution. The paper extends the theory of MIS to Artificial Intelligence (AI) driven cybersecurity intelligence and provides practical knowledge for companies seeking to achieve resilient digital transformation in a time of evolving cyber threats.

Due to the fast development of the Internet of Things (IoT) in smart buildings, the efficiency of operations, personal comfort, and sustainable operations have all been enhanced. But with all this dependence on potentially linked systems comes vital cybersecurity weaknesses. When such weaknesses are used to attack, they may lead to the compromise of the security of both an individual device as well as a building infrastructure. The present paper introduces a new cybersecure intelligent sensor framework that will be able to protect smart buildings against a wide variety of IOT related cyberattacks. The key element within this structure is a sophisticated AI automated intrusion detector (IDS) that can recognize, categorize and eliminate prospective threats in an instant using machine learning routines. The system uses the combination of intelligent sensor networks with AI-based analytics to continuously observe the environment and system data, with anomalous behaviours being indicators of a security breach being detected. The combination of predictive modelling and automated threat responses will allow the proposed system to achieve resilience against many attacks, including but not limited to denial of service, unauthorized access, and data manipulation. Widespread simulation and testing have shown that the system has a high detection rate, low false alarms, and a fast response time to help secure infrastructure buildings in smart buildings whilst Downtime is minimal. The results demonstrate the possible future of AI-enhanced cybersecurity systems in developing the IoT-based smart building security and enjoyment.