The rapid development of artificial intelligence technology has brought profound changes to the network security landscape: on the one hand, traditional defense mechanisms exhibit structural deficiencies such as static rule failures, slow security responses, lack of collaborative capabilities, and blind spots in algorithm protection; On the other hand, attacks driven by artificial intelligence present new features of intelligent dimensionality enhancement, breakthrough efficiency enhancement, and rapid expansion of scope, challenging traditional defense paradigms. Faced with this profound change, it is urgent to build a new paradigm of network security for the era of artificial intelligence. With the rapid development and deep application of artificial intelligence technology, new threats driven by artificial intelligence are disrupting traditional defense paradigms. The intelligent upgrading of attack methods, breakthrough enhancement of attack efficiency, and rapid expansion of attack scope are posing multidimensional, deep-seated, and highly destructive challenges to traditional security protection models. These new features signify that network attacks have evolved from single point, static behavior to a highly dynamic, distributed, and intelligent new situation of attack and defense confrontation, posing significant challenges to traditional protection mechanisms. One is that traditional security protection falls into a passive cycle of "repair attack re repair". Due to the variability and diversity of artificial intelligence attacks, they often bypass static detection that relies on feature libraries and rule sets (such as virus signature codes, attack signatures, etc.). For example, code obfuscation and polymorphic variants make signature recognition almost ineffective. Secondly, the long delay in the event response process of traditional security architecture can easily cause chain reactions. The process of threat detection, analysis, response, and disposal involves multiple steps, usually led by human intervention. This response method cannot meet the high speed of attacks in the era of artificial intelligence, and is prone to missing the best disposal window, even causing a chain reaction. Thirdly, the current network security mechanisms are mostly deployed in a "point-to-point" manner, lacking cross platform and cross domain information sharing and collaborative linkage, making it difficult to form a unified defense system. Artificial intelligence attacks often present a distributed and cross network situation, and once a node is lost, it is extremely easy to cause a ripple effect on the whole body. Fourthly, artificial intelligence models themselves have many security risks, including training data contamination, adversarial sample deception, model inversion, backdoor implantation, and so on. At present, the mainstream network security system has not yet incorporated artificial intelligence algorithm security into the overall protection scope, and lacks effective model validation and audit mechanisms. The structural defects of traditional protection systems, such as static rule failure, slow security response, lack of collaborative capabilities, and blind spots in algorithm protection, make them inadequate in the face of intelligent attacks and urgently require deep reshaping at the mechanism and architecture levels. Proactive defense orientation: Building a predictable and adaptive protection system. The deep integration of artificial intelligence into cyberspace highlights the lag and limitations of traditional passive security protection models that rely on "intrusion detection response" in the face of highly covert and rapidly evolving attack behaviors. Traditional mechanisms often only respond passively after the threat causes actual damage, and even miss critical disposal opportunities, leading to systemic risks. Therefore, the paradigm of network security urgently needs to fundamentally shift from "reactive defense" to "predictive intervention" and build a truly proactive defense system. The core of active defense lies in perceiving threats in advance, identifying intentions proactively, and taking proactive measures. Artificial intelligence technology provides key support for this: by using algorithms such as deep learning, natural language processing, and graph neural networks to deeply mine massive amounts of security data, the system can extract potential attack patterns, abnormal behavior characteristics, and threat development trends from it, and gain the ability to foresee future risks. The threat intelligence system integrates multiple data streams to construct a dynamic knowledge graph, achieving continuous learning and evolutionary warning. At the same time, user behavior modeling and traffic analysis techniques establish behavior baselines for individuals and groups, and once deviations are detected, automatic warnings can be issued, effectively identifying attackers' disguises hidden in normal traffic. Active defense is not only a technological upgrade, but also a profound transformation of security concepts. It requires security mechanisms to have the ability of continuous learning, autonomous adaptation, and dynamic evolution, promoting network security from passive threat response to active risk control, expanding from resisting known threats to defending against unknown attacks, upgrading from local external protection to global perception and intelligent regulation, and ultimately building a next-generation network security system with adaptive capabilities. Security as a Service architecture: Achieving elastic and agile security delivery. With the accelerated evolution of information technology architecture towards cloud computing, microservices, and distributed systems, traditional security protection models are no longer able to meet the demands of modern businesses for elasticity, flexibility, and real-time performance. Security as a Service (SECaaS) has emerged as the core carrier of the new paradigm of network security in the era of artificial intelligence. It encapsulates, delivers, and operates security capabilities in an intelligent and service-oriented form, ensuring security while flexibly adapting to the needs of AI driven business systems. The core advantage of SECaaS lies in encapsulating protection functions into standardized API interfaces, allowing security policies to be called on demand and flexibly configured. This enables security mechanisms to be seamlessly integrated into various applications in a modular, orchestrated, and pluggable form of intelligent services, achieving a deep integration of security and business. In addition, with the help of artificial intelligence analysis capabilities, SE SaaS can dynamically optimize security policies according to business forms, greatly improving the efficiency of security resources and achieving integrated intelligent protection across platforms and domains. The core of the new paradigm of network security in the era of artificial intelligence lies in the automation and collaborative intelligence of security response. SECaaS emphasizes that under the scheduling of artificial intelligence orchestration engines, security events can automatically trigger intelligent response processes, and strategies can be dynamically generated and accurately issued based on real-time scenarios, ensuring that protective actions are dynamically synchronized with business changes. This process is achieved through a platform based security operation support system, covering a complete closed loop of threat identification, risk assessment, response and disposal, and strategy feedback, ultimately building an intelligent security management center with adaptive, autonomous, and elastic control capabilities. The cornerstone of trustworthy artificial intelligence: ensuring model security and reliable decision-making. With the deep embedding of artificial intelligence into critical decision-making systems and becoming the core unit of information processing, the security of artificial intelligence systems has become a top priority in the overall network security system. Traditional protection focuses on the data, network, and system levels, while attackers have shifted their focus to the algorithm models themselves - launching new types of attacks through manipulating model logic, contaminating and stealing data, or exploiting the black box nature of artificial intelligence models. Building trustworthy artificial intelligence and implementing comprehensive model protection is an indispensable core pillar of the new generation of network security paradigm. Improving model stability is an important cornerstone for building trustworthy artificial intelligence. By actively introducing adversarial samples into the training set through adversarial training, the model is forced to learn to recognize and resist these disturbances, thereby improving its stability in the face of malicious input. Traceability technologies such as model watermarking and digital signatures can effectively prevent models from being illegally copied, tampered with, or forged, ensuring that deployed models are authorized genuine and not implanted with malicious logic. Ensuring the security and privacy of training data is a key support for building trustworthy artificial intelligence. Data is the "nourishment" of artificial intelligence, and improper protection can easily lead to privacy breaches and security incidents. To achieve this, it is necessary to strengthen data security through technological means. Differential privacy technology can hide individual sample information in statistical learning, reduce the model's dependence on specific data points, and enhance its ability to resist inference attacks; Distributed training mechanisms such as federated learning retain data locally, achieving joint model updates without leaving the domain, and blocking the risk of data leakage from the source. Enhancing model transparency and interpretability is a necessary path for building trustworthy artificial intelligence. Through technical means such as model decision logging, key intermediate state monitoring, and audit tracking, the visibility of the internal operation of the model can be improved. At the same time, using methods such as feature importance analysis and counterfactual interpretation, the complex output of the model is transformed into easy to understand explanations. This not only helps to identify problems and verify compliance, but also serves as an important foundation for establishing accountability mechanisms and winning user trust. It is the ultimate manifestation of the value of trustworthy artificial intelligence. The construction of trustworthy artificial intelligence is not a one-time solution, but a continuous process that runs through the entire lifecycle of model development, deployment, operation, and updates. Only by deeply embedding the concepts of security and trustworthiness in every aspect of model design, development, deployment, and operation, and by comprehensively utilizing techniques such as adversarial training, differential privacy, and federated learning, supplemented by strict data governance, version control, and automated operation and maintenance processes, can we effectively address key challenges such as model stability, data security, and transparency. Human machine collaborative defense: integrating intelligent analysis and expert decision-making. In the increasingly complex and ever-changing network threat environment, relying solely on manual analysis is difficult to handle massive, high-frequency, and multi-dimensional security incidents; However, relying solely on artificial intelligence for automated decision-making is limited by its understanding, judgment, and context awareness abilities, making it difficult to cope with complex situations and unstructured risks. For complex threat scenarios that require the integration of multiple sources of information, judgment of business background, and understanding of potential motivations, current artificial intelligence systems still struggle to accurately model and fully comprehend them. This requires human experts to conduct in-depth analysis, strategy formulation, and response decision-making based on preliminary analysis and auxiliary suggestions provided by artificial intelligence. Experts can also improve the accuracy and adaptability of models by continuously verifying and providing feedback on the output results of artificial intelligence systems, promoting the continuous learning and evolution of artificial intelligence systems. Therefore, integrating the computing speed of artificial intelligence with the deep judgment of human experts to form a complementary collaborative mechanism is the core direction for building the next generation of network security architecture. The security operation center is the core hub of human-machine collaboration mechanism. By integrating multiple processes such as data perception, intelligent analysis, automatic response, and manual judgment, a unified closed-loop protection mechanism is formed. The system can push the analysis results of artificial intelligence to the expert platform, and trigger a customized response process after the experts confirm the key threats. At the same time, the experience and strategic knowledge of experts can be transformed into rules and data, which can feed back into artificial intelligence models and make them perform better in subsequent analysis. Thus, an efficient collaboration mechanism is constructed with artificial intelligence as the first response unit and experts as the final decision-makers. The human-machine collaborative defense system not only improves protection efficiency and accuracy, but also embodies a security governance concept centered on experts and supported by artificial intelligence. This mechanism enhances the transparency and controllability of decision-making, avoids the uncertain risks brought by black box algorithms, and also improves the ability to support compliance and auditability. In the construction of future security systems, human-machine collaboration will become a key supporting force for achieving the organic unity of intelligence, automation, and trustworthiness, promoting network security capabilities to move towards a new stage of advanced intelligent integration. (New Society)