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.

By virtue of the fact that authorized individuals are able to circumvent traditional barriers, insider attacks continue to be a persistent worry for organizational cybersecurity. Different user behaviors and complex connection patterns can cause static detection systems to miss potential dangers and slow down the discovery process. An adaptive insider threat detection system and a real-time adaptive security framework are both presented in this study. SentriDeep-RL, which is a sentinel based on deep learning and reinforcement learning, is also shown. At the same time that graph neural networks (GNNs) simulate user-asset interactions, deep learning models represent behavioral characteristics. Within the SentriDeep-RL framework, real-time anomaly detection frameworks are able to identify dangers in an instant, and adaptive thresholding that is based on reinforcement learning is able to make dynamic decisions about threats that are unknown. Changing insider threat patterns can be identified by the system without the need for retraining because it monitors user behaviors, access records, and network telemetry. Gradual model modifications and temporal attention are utilized in the SentriDeep-RL approach. In early threat detection, SentriDeep-RL outperforms static models by 12-18% on multisource corporate datasets that imitate both planned and incidental insider acts. This results in a reduction in the number of false positives while keeping the same level of computing efficiency! Adaptive learning methods and idea drift detection are two techniques that can be utilized to obtain improved performance in changing business environments. Through the utilization of deep learning, GNN-based relational modeling, reinforcement learning, and real-time anomaly detection, this platform will be able to identify insider threats in a proactive, scalable, and resilient manner. Last but not least, SentriDeepRL leverages technology to address concerns regarding company cybersecurity, reaction time, as well as operational and reputational issues.

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.

This research assesses the way AI-driven analytics help organizations operate more flexibly, make decisions quickly, and fully align their IT with business priorities. In the current digital era, companies depend on business analytics powered by AI to modify their IT strategies and improve their ability to decide. AI with business intelligence, organizations process huge amounts of data quickly to get useful information that supports efficient work and gives them an edge over their competitors. The focus is on how using smart automation and integrated data allows IT governance to become proactive and enterprise planning to adapt. A combination of qualitative and quantitative methods is used in this study. Through surveys given to 300 IT experts and leaders from many industries. The experience with AI-driven analytics tools and how they see AI helping with strategic planning was recorded. Deep insight into these factors was gathered through interviews with 20 IT executives. The author used SPSS software for statistics on numbers and thematic coding on words to look for similar themes and links between AI adoption and IT plan results. AI technology in business analytics is shown to help companies react more quickly, support data-driven choices and ensure alignment between the business and IT. With intelligent automation, there is less complexity in operating the business and to real-time analytics, reactions to market changes are faster. Some integration obstacles and data quality issues did not stop organizations from achieving better innovation and standing out in the market. The research makes clear that AI is key to having successful data-based and flexible IT systems in the future.