Rapid digitalization of essential national infrastructure has created new vulnerabilities to cyber threats, leading to major security threats against the nation. The current security measures prove inadequate for keeping pace with developing cyberattacks, so artificial intelligence needs integration for threat detection enhancements and response improvements. The combination of AI-enabled cybersecurity systems gives them the ability to examine huge data collections instantly, monitor irregularities and perform automatic threat response functions to improve national security. Research investigates the system of artificial intelligence to enhance cyber threat response capabilities alongside its specific use for defending crucial infrastructure elements like energy networks as well as financial organizations and government IT infrastructure. Methodology The study combines qualitative approaches with quantitative methods as its research methodology. The analysis includes a structured review of existing frameworks that use AI for cybersecurity purposes with their performance evaluation. The paper evaluates real-world AI deployments across critical infrastructure systems through case studies to reveal successful strategies with encountered problems. The empirical proof of machine learning-based intrusion detection systems is carried out by testing IDS along with real-world dataset assessment to verify AI's threat mitigation effectiveness through the accuracy and precision & recall method. Security experts who perform interviews deliver valuable information about the current use of AI technology in national security applications. The national cybersecurity capabilities gain strength from AI-driven systems because these systems accomplish improved threat detection and swift responses without requiring human involvement. AI deliver its maximum effectiveness only when data privacy issues with adversarial AI attacks and regulatory hurdles, receive proper solutions,

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.

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.