查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1776696

2032 年網路安全市場 AI威脅偵測系統預測：按組件、部署類型、組織規模、技術、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球網路安全 AI威脅偵測系統市場預計在 2025 年達到 299.9 億美元，到 2032 年將達到 1,234.2 億美元，預測期內的複合年成長率為 22.4%。

人工智慧 (AI) 正在徹底改變網路安全，尤其是在威脅偵測系統領域。利用機器學習演算法和數據分析，AI 可以即時檢查海量系統日誌和網路流量，發現可能指向網路威脅的異常趨勢和異常情況。與傳統的基於規則的系統相比，AI 驅動的檢測工具不斷從新數據中學習，使其能夠更好地識別高級惡意軟體、入侵威脅和零時差攻擊。透過自動確定警報的優先級，這些系統能夠減少誤報，並促進更快、更有針對性的回應。此外，隨著網路威脅日益複雜，AI 正成為主動和自適應網路安全防禦的關鍵工具。

據歐盟網路安全局 (ENISA) 稱，對更快、更具適應性的威脅偵測的需求導致過去一年基於人工智慧的安全解決方案的採用率增加了 30%。

日益複雜和精密的網路威脅

影響網路安全領域採用人工智慧的關鍵因素之一是網路威脅日益頻繁且日益複雜。現代攻擊者經常使用超越傳統安全工具的複雜技術，包括勒索軟體即服務、多態惡意軟體、零時差漏洞以及人工智慧生成的網路釣魚攻擊。威脅行為者如今使用人工智慧來自動化和自訂攻擊，使其更加難以捉摸和識別。組織機構正在使用人工智慧驅動的威脅偵測系統來應對，該系統可以識別異常、分析行為模式並適應不斷變化的攻擊策略。此外，這些系統透過提供即時偵測新興威脅所需的速度和威脅情報，顯著增強了企業和政府機構的防禦態勢。

營運和實施成本高

高昂的部署、整合和維護成本是人工智慧在威脅偵測系統中應用的最大障礙之一。基於人工智慧的網路安全解決方案通常需要在最先進的硬體基礎設施、軟體許可證、客製化開發和雲端運算資源方面投入巨額資金。由於人工智慧模型需要使用大量資料進行持續訓練和更新，營運成本進一步增加。中小企業 (SME) 可能會發現這些財務要求不切實際。此外，由於投資回報週期長且收益不明確，決策者可能不願意對此類系統進行大規模投資，尤其是對於沒有人工智慧使用經驗的企業而言。

結合人工智慧、威脅情報和網路風險評估

將人工智慧與風險評分工具和網路威脅情報平台結合，將帶來新的機會。透過整合來自商業資料庫、暗網監控和開放原始碼的即時威脅訊息，人工智慧系統可以提升情境察覺，更快地識別新興威脅。使用機器學習模型處理這些非結構化動態數據，可以提供情境關聯性並得出有用的見解。此外，基於人工智慧的風險評分系統可以利用內部漏洞和外部威脅情勢，幫助組織確定威脅的嚴重性及其業務影響。這使得資源優先排序和主動網路安全策略成為可能，尤其是在國防、醫療保健和金融等行業。

缺乏互通性和標準化

人工智慧在網路安全領域的應用迅速擴張，形成了一個由眾多專有工具、平台和通訊協定組成的脫節生態系統。由於缺乏標準化和互通性，依賴多家供應商和技術的組織面臨嚴重威脅。將各種基於人工智慧的系統整合到一個連貫的網路安全框架中可能會導致相容性問題、威脅可見性不均衡以及安全組件之間的通訊中斷。此外，缺乏標準化的基準使得評估和對比不同人工智慧解決方案的有效性變得困難。缺乏明確的行業標準和最佳實踐會阻礙人工智慧的廣泛採用，使組織無法安全、大規模地部署人工智慧。

COVID-19的影響：

新冠疫情顯著加速了人工智慧在網路安全領域的應用，尤其是在威脅偵測系統中的應用，因為各組織機構迅速轉向遠端辦公、雲端服務和數位協作平台。這種快速的數位轉型擴大了攻擊面，並暴露了新的漏洞，導致對能夠即時監控分散式網路和端點的智慧自動化安全解決方案的需求不斷成長。在疫情期間，網路釣魚、勒索軟體攻擊和異常行為的偵測有所增加，而這在很大程度上得益於人工智慧驅動的威脅偵測工具。此外，儘管預算限制影響了部分IT投資，但網路安全仍然是重中之重。最終，這場危機成為推動人工智慧與各行各業安全業務深度融合的催化劑。

預計雲端運算市場將成為預測期內最大的市場

預計在預測期內，雲端領域將佔據最大的市場佔有率。隨著企業環境變得更加分散式，工作負載擴大跨越多個雲端平台、遠端端點和混合配置，雲端原生人工智慧工具憑藉其提供大規模自動化分析和即時威脅監控的優勢脫穎而出。企業更青睞雲端技術，因為它具有集中管理、易於部署、更新流暢以及快速存取新的人工智慧主導功能等優勢。此外，頂級供應商將巨量資料功能與先進的機器學習模型相整合，從而提高了檢測準確性，並加快了跨地理分散資產的事件回應速度。

預計自然語言處理 (NLP) 在預測期內將以最高複合年成長率成長

預計自然語言處理 (NLP) 領域將在預測期內呈現最高成長率。自然語言處理 (NLP) 技術的快速發展現在使系統能夠分析和解釋非結構化資料（例如電子郵件、日誌、警報和聊天通訊），以識別威脅、情緒變化、內部風險和合違規。透過將大規模語言模型與基於 Transformer 的架構相結合，NLP 改進了上下文感知分析，並可用於自動摘要安全事件、建立調查見解，甚至對話式威脅搜尋。此外，NLP 是威脅偵測系統中成長最快的技術領域，這種採用激增是由於它能夠處理自然語言輸入、彌合安全分析師和人工智慧系統之間的溝通鴻溝以及跨不同資料來源擴展威脅情報。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率。該地區的主導地位得益於強大的數位生態系統，包括科技巨頭、政府機構、金融機構和關鍵基礎設施營運商，這些公司正在大力投資人工智慧主導的網路防禦。此外，嚴格的法規環境和合規要求也推動高階威脅偵測工具的採用。北美領先的網路安全公司始終處於技術創新的前沿，並正在為人工智慧驅動的安全解決方案樹立全球標準。

複合年成長率最高的地區：

預計亞太地區將在預測期內實現最高的複合年成長率，這得益於數位化步伐的加快、網路威脅範圍的擴大以及人工智慧技術在政府、製造業、銀行業和通訊等行業的日益普及。中國、印度、日本和韓國等國家正大力投資雲端基礎的安全解決方案、智慧城市和人工智慧基礎設施，加速先進威脅偵測系統的普及。此外，資料隱私意識的增強、關鍵基礎設施網路攻擊的增加以及政府鼓勵人工智慧和網路安全創新的項目，也為快速成長創造了有利的環境。

免費客製化服務

訂閱此報告的客戶可享有以下免費自訂選項之一：

  • 公司簡介
    • 對最多三家其他市場公司進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 網路安全 AI威脅偵測系統市場（按組件）

  • 解決方案
    • 威脅偵測和預防解決方案
    • 威脅情報平台
    • 人工智慧驅動的安全分析
    • 事件回應解決方案
    • 安全編配、自動化和回應 (SOAR)
    • 安全資訊和事件管理 (SIEM)
    • 端點檢測與響應 (EDR)
  • 服務
    • 託管威脅情報服務
    • 諮詢和整合服務
    • 事件回應服務
    • 威脅搜尋服務
    • 培訓和支援服務
  • 硬體
    • 感應器
    • 網路裝置
    • 安全設備

第6章 網路安全人工智慧威脅偵測系統市場（按部署類型）

  • 雲
  • 本地
  • 混合

第7章。網路安全人工智慧威脅偵測系統市場（按組織規模）

  • 主要企業
  • 小型企業

8. 全球網路安全 AI威脅偵測系統市場（按技術）

  • 機器學習
  • 自然語言處理（NLP）
  • 情境感知計算
  • 深度學習
  • 電腦視覺
  • 行為分析
  • 強化學習

第9章。全球網路安全 AI威脅偵測系統市場（按最終用戶）

  • 銀行、金融服務和保險（BFSI）
  • 政府和國防
  • 資訊科技和通訊
  • 醫療保健和生命科學
  • 零售與電子商務
  • 能源和公共產業
  • 製造業
  • 其他最終用戶

第 10 章。按地區分類的網路安全 AI威脅偵測系統市場

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章：企業概況

  • 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.