查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1776696

2032 年网路安全市场 AI威胁侦测系统预测：按组件、部署类型、组织规模、技术、最终用户和地区进行的全球分析

AI in Cybersecurity - Threat Detection Systems Market Forecasts to 2032 - Global Analysis By Component (Solution, Service and Hardware), Deployment Mode (Cloud, On-Premise and Hybrid), Organization Size, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球网路安全 AI威胁侦测系统市场预计在 2025 年达到 299.9 亿美元，到 2032 年将达到 1,234.2 亿美元，预测期内的复合年增长率为 22.4%。

人工智慧 (AI) 正在彻底改变网路安全，尤其是在威胁侦测系统领域。利用机器学习演算法和数据分析，AI 可以即时检查海量系统日誌和网路流量，发现可能指向网路威胁的异常趋势和异常情况。与传统的基于规则的系统相比，AI 驱动的检测工具不断从新数据中学习，使其能够更好地识别高级恶意软体、入侵威胁和零时差攻击。透过自动确定警报的优先级，这些系统能够减少误报，并促进更快、更有针对性的回应。此外，随着网路威胁日益复杂，AI 正成为主动和自适应网路安全防御的关键工具。

据欧盟网路安全局 (ENISA) 称，对更快、更具适应性的威胁侦测的需求导致过去一年基于人工智慧的安全解决方案的采用率增加了 30%。

日益复杂和精密的网路威胁

影响网路安全领域采用人工智慧的关键因素之一是网路威胁日益频繁且日益复杂。现代攻击者经常使用超越传统安全工具的复杂技术，包括勒索软体即服务、多态恶意软体、零时差漏洞以及人工智慧生成的网路钓鱼攻击。威胁行为者如今使用人工智慧来自动化和自订攻击，使其更加难以捉摸和识别。组织机构正在使用人工智慧驱动的威胁侦测系统来应对，该系统可以识别异常、分析行为模式并适应不断变化的攻击策略。此外，这些系统透过提供即时侦测新兴威胁所需的速度和威胁情报，显着增强了企业和政府机构的防御态势。

营运和实施成本高

高昂的部署、整合和维护成本是人工智慧在威胁侦测系统中应用的最大障碍之一。基于人工智慧的网路安全解决方案通常需要在最先进的硬体基础设施、软体许可证、客製化开发和云端运算资源方面投入巨额资金。由于人工智慧模型需要使用大量资料进行持续训练和更新，营运成本进一步增加。中小企业 (SME) 可能会发现这些财务要求不切实际。此外，由于投资回报週期长且收益不明确，决策者可能不愿意对此类系统进行大规模投资，尤其是对于没有人工智慧使用经验的企业而言。

结合人工智慧、威胁情报和网路风险评估

将人工智慧与风险评分工具和网路威胁情报平台结合，将带来新的机会。透过整合来自商业资料库、暗网监控和开放原始码的即时威胁讯息，人工智慧系统可以提升情境察觉，更快地识别新兴威胁。使用机器学习模型处理这些非结构化动态数据，可以提供情境关联性并得出有用的见解。此外，基于人工智慧的风险评分系统可以利用内部漏洞和外部威胁情势，帮助组织确定威胁的严重性及其业务影响。这使得资源优先排序和主动网路安全策略成为可能，尤其是在国防、医疗保健和金融等行业。

缺乏互通性和标准化

人工智慧在网路安全领域的应用迅速扩张，形成了一个由众多专有工具、平台和通讯协定组成的脱节生态系统。由于缺乏标准化和互通性，依赖多家供应商和技术的组织面临严重威胁。将各种基于人工智慧的系统整合到一个连贯的网路安全框架中可能会导致相容性问题、威胁可见性不均衡以及安全组件之间的通讯中断。此外，缺乏标准化的基准使得评估和对比不同人工智慧解决方案的有效性变得困难。缺乏明确的行业标准和最佳实践会阻碍人工智慧的广泛采用，使组织无法安全、大规模地部署人工智慧。

COVID-19的影响：

新冠疫情显着加速了人工智慧在网路安全领域的应用，尤其是在威胁侦测系统中的应用，因为各组织机构迅速转向远端办公、云端服务和数位协作平台。这种快速的数位转型扩大了攻击面，并暴露了新的漏洞，导致对能够即时监控分散式网路和端点的智慧自动化安全解决方案的需求不断增长。在疫情期间，网路钓鱼、勒索软体攻击和异常行为的侦测有所增加，而这在很大程度上得益于人工智慧驱动的威胁侦测工具。此外，儘管预算限制影响了部分IT投资，但网路安全仍然是重中之重。最终，这场危机成为推动人工智慧与各行各业安全业务深度融合的催化剂。

预计云端运算市场将成为预测期内最大的市场

预计在预测期内，云端领域将占据最大的市场占有率。随着企业环境变得更加分散式，工作负载越来越多地跨越多个云端平台、远端端点和混合配置，云端原生人工智慧工具凭藉其提供大规模自动化分析和即时威胁监控的优势脱颖而出。企业更青睐云端技术，因为它具有集中管理、易于部署、更新流畅以及快速存取新的人工智慧主导功能等优势。此外，顶级供应商将巨量资料功能与先进的机器学习模型相集成，从而提高了检测准确性，并加快了跨地理分散资产的事件响应速度。

预计自然语言处理 (NLP) 在预测期内将以最高复合年增长率成长

预计自然语言处理 (NLP) 领域将在预测期内呈现最高成长率。自然语言处理 (NLP) 技术的快速发展现在使系统能够分析和解释非结构化资料（例如电子邮件、日誌、警报和聊天通讯），以识别威胁、情绪变化、内部风险和合违规。透过将大规模语言模型与基于 Transformer 的架构相结合，NLP 改进了上下文感知分析，并可用于自动摘要安全事件、建立调查见解，甚至对话式威胁搜寻。此外，NLP 是威胁侦测系统中成长最快的技术领域，这种采用激增是由于它能够处理自然语言输入、弥合安全分析师和人工智慧系统之间的沟通鸿沟以及跨不同资料来源扩展威胁情报。

占比最大的地区：

预计北美将在预测期内占据最大的市场占有率。该地区的主导地位得益于强大的数位生态系统，包括科技巨头、政府机构、金融机构和关键基础设施营运商，这些公司正在大力投资人工智慧主导的网路防御。此外，严格的法规环境和合规要求也推动高阶威胁侦测工具的采用。北美领先的网路安全公司始终处于技术创新的前沿，并正在为人工智慧驱动的安全解决方案树立全球标准。

复合年增长率最高的地区：

预计亚太地区将在预测期内实现最高的复合年增长率，这得益于数位化步伐的加快、网路威胁范围的扩大以及人工智慧技术在政府、製造业、银行业和通讯等行业的日益普及。中国、印度、日本和韩国等国家正大力投资云端基础的安全解决方案、智慧城市和人工智慧基础设施，加速先进威胁侦测系统的普及。此外，资料隐私意识的增强、关键基础设施网路攻击的增加以及政府鼓励人工智慧和网路安全创新的项目，也为快速成长创造了有利的环境。

免费客製化服务

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 对最多三家其他市场公司进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 网路安全 AI威胁侦测系统市场（按组件）

  • 解决方案
    • 威胁侦测和预防解决方案
    • 威胁情报平台
    • 人工智慧驱动的安全分析
    • 事件回应解决方案
    • 安全编配、自动化和回应 (SOAR)
    • 安全资讯和事件管理 (SIEM)
    • 端点检测与响应 (EDR)
  • 服务
    • 託管威胁情报服务
    • 咨询和整合服务
    • 事件回应服务
    • 威胁搜寻服务
    • 培训和支援服务
  • 硬体
    • 感应器
    • 网路装置
    • 安全设备

第六章 网路安全人工智慧威胁侦测系统市场（按部署类型）

  • 云
  • 本地
  • 杂交种

第七章。网路安全人工智慧威胁侦测系统市场（按组织规模）

  • 大公司
  • 小型企业

8. 全球网路安全 AI威胁侦测系统市场（按技术）

  • 机器学习
  • 自然语言处理（NLP）
  • 情境感知计算
  • 深度学习
  • 电脑视觉
  • 行为分析
  • 强化学习

第九章。全球网路安全 AI威胁侦测系统市场（按最终用户）

  • 银行、金融服务和保险（BFSI）
  • 政府和国防
  • 资讯科技和通讯
  • 医疗保健和生命科学
  • 零售与电子商务
  • 能源和公共产业
  • 製造业
  • 其他最终用户

第 10 章。按地区分類的网路安全 AI威胁侦测系统市场

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章：企业概况

  • IBM Corporation
  • Palo Alto Networks
  • SentinelOne Inc
  • Fortinet Inc
  • Check Point Software Technologies（Infinity）
  • Microsoft Corporation
  • Symantec（Broadcom）
  • Vectra AI
  • CrowdStrike Inc
  • Darktrace Inc
  • Cisco Systems
  • Optiv
  • Cybereason Inc
  • UncommonX Inc
Product Code: SMRC30027

According to Stratistics MRC, the Global AI in Cybersecurity - Threat Detection Systems Market is accounted for $29.99 billion in 2025 and is expected to reach $123.42 billion by 2032 growing at a CAGR of 22.4% during the forecast period. Artificial Intelligence (AI) is revolutionizing cybersecurity, particularly in the area of threat detection systems. AI can examine enormous amounts of system logs and network traffic in real time by utilizing machine learning algorithms and data analytics to spot odd trends or anomalies that might point to a cyber threat. AI-driven detection tools, in contrast to conventional rule-based systems, are constantly learning from fresh data, which enhances their capacity to identify sophisticated malware, insider threats, and zero-day attacks. By automatically prioritizing alerts, these systems can lower false positives and facilitate quicker, more precise responses. Moreover, AI is becoming a crucial tool for proactive and adaptive cybersecurity defense as cyber threats become more sophisticated.

According to the European Union Agency for Cybersecurity (ENISA), there was a 30% increase in the adoption of AI-based security solutions in the past year, driven by the need for faster and more adaptive threat detection.

Market Dynamics:

Driver:

Increasingly complex and advanced cyber threats

One of the main factors influencing the adoption of AI in cybersecurity is the growing frequency and complexity of cyber threats. Modern attackers use sophisticated tactics that frequently outperform conventional security tools, such as ransom ware-as-a-service, polymorphic malware, zero-day vulnerabilities, and AI-generated phishing attacks. Threat actors are now using AI to automate and customize their attacks, making them more elusive and challenging to identify. Organizations are responding by using AI-powered threat detection systems that are able to identify anomalies, analyze behavioral patterns, and adjust to changing attack tactics. Additionally, these systems greatly strengthen the defensive posture of businesses and governmental organizations alike by providing the speed and intelligence required to detect new threats in real time.

Restraint:

High operational and implementation costs

The high cost of implementation, integration, and maintenance is one of the biggest obstacles to the use of AI in threat detection systems. Significant expenditures in cutting-edge hardware infrastructure, software licenses, custom development, and cloud computing resources are frequently necessary for AI-driven cybersecurity solutions. Operational costs are further increased by the requirement for AI models to be continuously trained and updated using vast amounts of data. Small and medium-sized businesses (SMEs) may find these financial requirements to be impractical. Furthermore, decision-makers may be reluctant to make significant investments in such systems due to the lengthy ROI cycles and unclear benefits, particularly for businesses with no prior experience with AI.

Opportunity:

Combining AI, threat intelligence, and cyber risk assessment

The combination of AI with risk scoring tools and cyber threat intelligence platforms presents another new opportunity. AI systems can improve their situational awareness and identify new threats more quickly by combining real-time threat feeds from commercial databases, dark web monitoring, and open sources. This unstructured and dynamic data can be processed by machine learning models, which can then provide contextual relevance and produce useful insights. Moreover, using internal vulnerabilities and external threat landscapes, AI-based risk scoring systems assist organizations in determining the seriousness and business impact of threats. This makes it possible to prioritize resources and implement proactive cybersecurity strategies, particularly for industries like defense, healthcare, and finance.

Threat:

Insufficient interoperability and standardization

A disjointed ecosystem with a large number of proprietary tools, platforms, and protocols has resulted from the quick expansion of AI applications in cybersecurity. Organizations that depend on several vendors and technologies are seriously threatened by this lack of standardization and interoperability. Compatibility problems, uneven threat visibility, and communication breakdowns between security components can arise when various AI-based systems are integrated into a coherent cybersecurity framework. Furthermore, it is challenging to assess and contrast the efficacy of various AI solutions in the absence of standardized benchmarks. Widespread adoption may be hampered by organizations' inability to deploy AI securely and at scale in the absence of clear industry-wide standards and best practices.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the adoption of AI in cybersecurity, particularly in threat detection systems, as organizations rapidly shifted to remote work, cloud services, and digital collaboration platforms. The demand for intelligent, automated security solutions that can monitor distributed networks and endpoints in real time has increased as a result of this abrupt digital transformation, which has increased the attack surface and revealed new vulnerabilities. The detection of phishing attempts, ransom ware attacks, and unusual behaviour that increased during the pandemic was made possible in large part by AI-powered threat detection tools. Additionally, cybersecurity remained a top priority, despite budgetary constraints affecting some IT investments. In the end, the crisis served as a catalyst for a deeper integration of AI into security operations across industries.

The cloud segment is expected to be the largest during the forecast period

The cloud segment is expected to account for the largest market share during the forecast period. As enterprise environments become more dispersed-workloads moving across multiple clouds, remote endpoints, and hybrid configurations-cloud-native AI tools perform exceptionally well by providing automated analytics and real-time threat monitoring at scale. Because of their central management, ease of deployment, smooth updates, and quick access to new AI-driven features, cloud deployments are preferred by organizations. Furthermore, big data capabilities and advanced machine learning models are being integrated by top providers to improve detection accuracy and speed up incident response across geographically scattered assets.

The natural language processing (NLP) segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the natural language processing (NLP) segment is predicted to witness the highest growth rate. Systems can now analyze and interpret unstructured data, including emails, logs, alerts, and chat communications, to identify threats, sentiment shifts, insider risks, and compliance violations owing to the quick advancement of natural language processing (NLP) technologies. NLP improves context-aware analysis by integrating large language models and Transformer-based architectures, which can be used to automatically summarize security incidents, produce investigative insights, and even engage in conversational threat hunting. Moreover, NLP is the fastest-growing technology segment in threat detection systems, and this surge in adoption is due to its capacity to process natural-language inputs, close communication gaps between security analysts and AI systems, and scale intelligence across diverse data sources.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. A strong digital ecosystem that makes significant investments in AI-driven cyber defense, including tech behemoths, governmental organizations, financial institutions, and operators of vital infrastructure, is the driving force behind this regional dominance. Additionally, advanced threat detection tools are also being adopted as a result of strict regulatory environments and compliance requirements. Leading North American cybersecurity companies are still at the forefront of innovation and setting the standard for AI-enhanced security solutions worldwide.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by the quickening pace of digitalization, the expanding scope of cyber threats, and the growing use of AI technologies in industries like government, manufacturing, banking, and telecommunications. Advanced threat detection systems are being deployed more quickly as a result of significant investments made by nations like China, India, Japan, and South Korea in cloud-based security solutions, smart cities, and AI-enabled infrastructure. Furthermore, a favorable climate for rapid growth is also being produced by growing awareness of data privacy, an increase in cyberattacks on vital infrastructure, and government programs that encourage innovation in AI and cybersecurity.

Key players in the market

Some of the key players in AI in Cybersecurity - Threat Detection Systems Market include IBM Corporation, Palo Alto Networks, SentinelOne Inc, Fortinet Inc, Check Point Software Technologies (Infinity), Microsoft Corporation, Symantec (Broadcom), Vectra AI, CrowdStrike Inc, Darktrace Inc, Cisco Systems, Optiv, Cybereason Inc and UncommonX Inc.

Key Developments:

In June 2025, Palo Alto Networks is strengthening its presence across key markets in the Asia-Pacific and Japan (APJ) region through an expansion of its cloud infrastructure. This expansion of local cloud infrastructure within critical markets including Australia, India, Indonesia, Japan, and Singapore, is expected to change the way enterprises in the region secure web browsing while adhering to vital local data residency requirements.

In April 2025, IBM announced it has acquired Hakkoda Inc. Hakkoda will expand IBM Consulting's data transformation services portfolio, adding specialized data platform expertise to help clients get their data ready to fuel AI-powered business operations. Hakkoda has leading capabilities in migrating, modernizing, and monetizing data estates and is an award-winning Snowflake partner. This acquisition amplifies IBM's ability to meet the rapidly growing demand for data services and help clients build integrated enterprise data estates that are optimized for speed, cost and efficiency across multiple business use cases.

In October 2024, SentinelOne announced an extension of its strategic collaboration agreement (SCA) with Amazon Web Services (AWS), designed to deliver generative AI benefits. Under the terms of the agreement, SentinelOne's Purple AI cybersecurity analyst will be powered by Amazon Bedrock, to provide AI-powered security and protection for customers. Additionally, the expanded SCA will increase investments in SentinelOne's AI-powered Singularity(TM) Platform within AWS Marketplace, empowering enterprises to quickly and easily access end-to-end protection from a unified, AI-powered platform.

Components Covered:

  • Solution
  • Service
  • Hardware

Deployment Modes Covered:

  • Cloud
  • On-Premise
  • Hybrid

Organization Sizes Covered:

  • Large Enterprises
  • Small and Medium-Sized Enterprises (SMEs)

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Context-Aware Computing
  • Deep Learning
  • Computer Vision
  • Behavioral Analytics
  • Reinforcement Learning

End Users Covered:

  • Banking, Financial Services, and Insurance (BFSI)
  • Government and Defense
  • Information Technology and Telecom
  • Healthcare and Life Sciences
  • Retail and E-commerce
  • Energy and Utilities
  • Manufacturing
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Cybersecurity - Threat Detection Systems Market, By Component

  • 5.1 Introduction
  • 5.2 Solution
    • 5.2.1 Threat Detection and Prevention Solutions
    • 5.2.2 Threat Intelligence Platforms
    • 5.2.3 AI-Powered Security Analytics
    • 5.2.4 Incident Response Solutions
    • 5.2.5 Security Orchestration, Automation, and Response (SOAR)
    • 5.2.6 Security Information and Event Management (SIEM)
    • 5.2.7 Endpoint Detection and Response (EDR)
  • 5.3 Service
    • 5.3.1 Managed Threat Intelligence Services
    • 5.3.2 Consulting & Integration Services
    • 5.3.3 Incident Response Services
    • 5.3.4 Threat Hunting Services
    • 5.3.5 Training & Support Services
  • 5.4 Hardware
    • 5.4.1 Sensors
    • 5.4.2 Network Appliances
    • 5.4.3 Security Appliances

6 Global AI in Cybersecurity - Threat Detection Systems Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-Premise
  • 6.4 Hybrid

7 Global AI in Cybersecurity - Threat Detection Systems Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Large Enterprises
  • 7.3 Small and Medium-Sized Enterprises (SMEs)

8 Global AI in Cybersecurity - Threat Detection Systems Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Context-Aware Computing
  • 8.5 Deep Learning
  • 8.6 Computer Vision
  • 8.7 Behavioral Analytics
  • 8.8 Reinforcement Learning

9 Global AI in Cybersecurity - Threat Detection Systems Market, By End User

  • 9.1 Introduction
  • 9.2 Banking, Financial Services, and Insurance (BFSI)
  • 9.3 Government and Defense
  • 9.4 Information Technology and Telecom
  • 9.5 Healthcare and Life Sciences
  • 9.6 Retail and E-commerce
  • 9.7 Energy and Utilities
  • 9.8 Manufacturing
  • 9.9 Other End Users

10 Global AI in Cybersecurity - Threat Detection Systems Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM Corporation
  • 12.2 Palo Alto Networks
  • 12.3 SentinelOne Inc
  • 12.4 Fortinet Inc
  • 12.5 Check Point Software Technologies (Infinity)
  • 12.6 Microsoft Corporation
  • 12.7 Symantec (Broadcom)
  • 12.8 Vectra AI
  • 12.9 CrowdStrike Inc
  • 12.10 Darktrace Inc
  • 12.11 Cisco Systems
  • 12.12 Optiv
  • 12.13 Cybereason Inc
  • 12.14 UncommonX Inc

List of Tables

  • Table 1 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Solution (2024-2032) ($MN)
  • Table 4 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Threat Detection and Prevention Solutions (2024-2032) ($MN)
  • Table 5 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Threat Intelligence Platforms (2024-2032) ($MN)
  • Table 6 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By AI-Powered Security Analytics (2024-2032) ($MN)
  • Table 7 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Incident Response Solutions (2024-2032) ($MN)
  • Table 8 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Security Orchestration, Automation, and Response (SOAR) (2024-2032) ($MN)
  • Table 9 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Security Information and Event Management (SIEM) (2024-2032) ($MN)
  • Table 10 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Endpoint Detection and Response (EDR) (2024-2032) ($MN)
  • Table 11 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Service (2024-2032) ($MN)
  • Table 12 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Managed Threat Intelligence Services (2024-2032) ($MN)
  • Table 13 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Consulting & Integration Services (2024-2032) ($MN)
  • Table 14 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Incident Response Services (2024-2032) ($MN)
  • Table 15 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Threat Hunting Services (2024-2032) ($MN)
  • Table 16 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Training & Support Services (2024-2032) ($MN)
  • Table 17 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 18 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Sensors (2024-2032) ($MN)
  • Table 19 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Network Appliances (2024-2032) ($MN)
  • Table 20 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Security Appliances (2024-2032) ($MN)
  • Table 21 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 22 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 23 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 24 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 25 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 26 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 27 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Small and Medium-Sized Enterprises (SMEs) (2024-2032) ($MN)
  • Table 28 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Technology (2024-2032) ($MN)
  • Table 29 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 30 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 31 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Context-Aware Computing (2024-2032) ($MN)
  • Table 32 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 33 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 34 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Behavioral Analytics (2024-2032) ($MN)
  • Table 35 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 36 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By End User (2024-2032) ($MN)
  • Table 37 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2024-2032) ($MN)
  • Table 38 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Government and Defense (2024-2032) ($MN)
  • Table 39 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Information Technology and Telecom (2024-2032) ($MN)
  • Table 40 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Healthcare and Life Sciences (2024-2032) ($MN)
  • Table 41 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Retail and E-commerce (2024-2032) ($MN)
  • Table 42 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Energy and Utilities (2024-2032) ($MN)
  • Table 43 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 44 Global AI in Cybersecurity - Threat Detection Systems Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.