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1776716

人工智慧 - 2032 年網路安全市場威脅情報預測：按組件、安全類型、部署模式、技術、應用、最終用戶和地區進行的全球分析

AI in Cybersecurity - Threat Intelligence Market Forecasts to 2032 - Global Analysis By Component, Security Type, Deployment Mode, Technology, Application, 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，全球網路安全市場的人工智慧威脅情報預計在 2025 年將達到 204.6 億美元，預計到 2032 年將達到 922.2 億美元，預測期內的複合年成長率為 24%。

網路安全中的人工智慧 - 威脅情報是指利用人工智慧即時偵測、評估和消除線上威脅。它利用機器學習、自然語言處理和數據分析來識別詐欺、預測攻擊並自動回應。人工智慧透過消化來自各種來源的大量威脅數據，提升情境察覺和決策能力。它可以識別勒索軟體、網路釣魚和惡意軟體的趨勢，從而實現主動防禦策略。這種巧妙的自動化技術顯著提高了威脅偵測的速度、準確性和回應時間，增強了組織的整體網路安全態勢。

網路攻擊日益複雜

人工智慧主導的惡意軟體和零日漏洞等進階威脅需要更快、更聰明的偵測方法。即時偵測複雜的攻擊模式往往是傳統安全解決方案面臨的挑戰。透過自動化資料處理和快速識別異常，人工智慧可以提升威脅情報。它能夠在損害發生之前預測並消除威脅，從而實現主動防禦。隨著越來越多的企業依賴人工智慧解決方案來領先駭客攻擊，該行業正在蓬勃發展。

資料隱私和監管風險

由於CCPA和GDPR等嚴格的資料隱私法規，存取訓練AI模型所需的海量資料集受到限制。企業在國際範圍內收集或傳播威脅情報時，經常會遇到合規性問題。這些法律限制可能會阻礙AI的採用，並降低即時偵測威脅的能力。監管的不確定性也阻礙了對尖端AI驅動的網路安全工具的投資。此外，由於擔心違規處罰，企業不願充分利用AI功能。

自動化和預測分析

自動化和預測分析技術使即時監控和快速分析大量資料成為可能。自動化技術簡化了常規安全業務，並最大限度地減少了人為錯誤。預測分析能夠提前發現趨勢和異常，在潛在威脅變得嚴重之前就預見它們。這種主動策略不僅可以幫助組織應對違規行為，還能在違規行為發生之前預防。因此，這些技術能夠降低營運成本並改善安全效果，從而推動市場擴張。

攻擊者正在快速進化

攻擊者的快速進化要求人工智慧模型具備即時適應性。機器學習演算法通常依賴先前的數據，這使得它們在應對新的、未知的威脅時效果不佳。網路犯罪分子對人工智慧的使用日益增多，導致攻擊日益複雜且難以捉摸。這需要頻繁升級和重新訓練模型，從而增加了複雜性和營運成本。因此，安全公司在維持有效的威脅偵測方面面臨持續的挑戰。

COVID-19的影響

新冠疫情顯著加速了人工智慧在網路安全和威脅情報市場的普及。隨著遠距辦公成為常態，企業面臨的網路威脅和資料外洩激增，迫切需要智慧自動化的安全解決方案。基於人工智慧的威脅偵測系統可幫助企業快速識別並應對不斷演變的網路風險。此外，封鎖期間有限的人工干預凸顯了機器學習在監控龐大數位環境中的價值。總體而言，這場危機重塑了網路安全戰略，並將人工智慧定位為防禦機制的關鍵組成部分。

網路安全領域預計將成為預測期內最大的領域

網路安全領域預計將在預測期內佔據最大的市場佔有率，這得益於其能夠在複雜的IT基礎設施中實現即時威脅偵測的能力。人工智慧工具可以分析大量網路流量，並快速識別異常和惡意模式。這種主動方法使企業能夠防患於未然。網路攻擊日益複雜化，推動了對人工智慧驅動的網路防禦解決方案的需求。隨著企業數位化業務的擴張，透過智慧自動化保護網路安全已變得勢在必行，從而推動了市場的成長。

異常檢測部分預計在預測期內達到最高複合年成長率

異常檢測能夠及早識別可疑模式，預計在預測期內將達到最高成長率。它有助於檢測傳統方法經常遺漏的零時差攻擊和內部威脅。即時網路流量分析可增強主動威脅緩解能力。基於人工智慧的異常檢測可減少誤報並提高事件回應效率。持續學習能力可增強整個企業的自適應安全框架。

比最大的地區

由於各行業數位化不斷提高以及針對關鍵基礎設施的網路攻擊增多，預計亞太地區將在預測期內佔據最大市場佔有率。中國、印度、日本和韓國等國家正大力投資以人工智慧為基礎的網路安全工具，以保護金融服務、政府網路和電商平台。雲端運算應用和智慧型手機普及率的不斷提高進一步推動了這項需求。該地區各國政府也正在推出更嚴格的資料保護法，促使企業採用預測性威脅偵測和自動回應系統，進而推動網路安全防禦策略的創新。

複合年成長率最高的地區：

在預測期內，北美預計將憑藉其先進的IT基礎設施和大型科技公司的存在，實現最高的複合年成長率。美國在即時識別、預測和消除網路威脅的人工智慧演算法的開發和部署方面處於領先地位。針對銀行、醫療保健和國防領域的網路犯罪高發，推動了對人工智慧驅動的威脅情報平台的需求。此外，網路安全公司之間不斷增加的研發投入和策略夥伴關係關係，正在增強威脅偵測能力。 CISA和HIPAA等強力的法規結構將進一步推動人工智慧的應用，以有效地保護數位生態系統。

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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
  • 應用分析
  • 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

5. 全球網路安全人工智慧-威脅情報市場（按組件）

  • 解決方案
  • 服務

6. 全球網路安全人工智慧-威脅情報市場（依安全類型）

  • 網路安全
  • 端點安全
  • 應用程式安全
  • 雲端安全
  • 網路安全
  • 其他安全類型

7. 全球網路安全領域人工智慧威脅情報市場（按部署模式）

  • 雲端基礎
  • 本地

8. 全球網路安全人工智慧威脅情報市場（按技術）

  • 機器學習（ML）
  • 自然語言處理（NLP）
  • 情境感知計算
  • 進階分析
  • 其他技術

9. 全球網路安全人工智慧威脅情報市場（按應用）

  • 威脅情報
  • 異常檢測
  • 入侵偵測與預防
  • 身分和存取管理
  • 風險與合規管理
  • 其他用途

第 10 章。全球網路安全 AI - 威脅情報市場（按最終用戶）

  • 政府和國防
  • 資訊科技和通訊
  • 衛生保健
  • 製造業
  • 零售
  • 能源和公共產業
  • 教育
  • 運輸/物流
  • 其他最終用戶

11. 全球網路安全市場人工智慧威脅情報（按地區）

  • 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12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13章 公司概況

  • Palo Alto Networks
  • CrowdStrike
  • Fortinet
  • Darktrace
  • SentinelOne
  • Vectra AI
  • Wiz
  • Orca Security
  • Netskope
  • Check Point
  • Trellix
  • Tanium
  • Trend Micro
  • Splunk
  • Deep Instinct
  • Cybereason
  • SparkCognition
  • Armis
Product Code: SMRC30047

According to Stratistics MRC, the Global AI in Cybersecurity - Threat Intelligence Market is accounted for $20.46 billion in 2025 and is expected to reach $92.22 billion by 2032 growing at a CAGR of 24% during the forecast period. Artificial Intelligence in Cybersecurity: Threat Intelligence is the use of AI to detect, evaluate, and neutralise online threats instantly. To identify irregularities, anticipate attacks, and automate reactions, it makes use of machine learning, natural language processing, and data analytics. AI improves situational awareness and decision-making by digesting massive volumes of threat data from various sources. By spotting trends in ransomware, phishing, and malware activity, it makes proactive defence tactics possible. This clever automation strengthens an organization's entire cybersecurity posture by greatly increasing threat detection speed, accuracy, and response time.

Market Dynamics:

Driver:

Rising sophistication of cyber-attacks

Advanced threats like as AI-driven malware and zero-day exploits necessitate quicker and more intelligent detection methods. Real-time detection of intricate attack patterns is frequently a challenge for traditional security solutions. By automating data processing and quickly identifying abnormalities, AI improves threat intelligence. It enables proactive defence by foreseeing and removing threats before damage is done. The industry is growing as a result of organisations depending more and more on AI-powered solutions to stay ahead of hackers.

Restraint:

Data privacy & regulatory risk

Access to the vast datasets required to train AI models is restricted by stringent data privacy regulations such as the CCPA and GDPR. When gathering or disseminating threat intelligence internationally, organisations frequently encounter compliance issues. These legal restrictions may hinder the uptake of AI and reduce its capacity to detect threats in real time. Investment in cutting-edge AI-driven cybersecurity tools is also deterred by regulatory uncertainty. Additionally, businesses are reluctant to fully utilise AI capabilities due to a concern of non-compliance penalties.

Opportunity:

Automation and predictive analytics

Real-time monitoring and quick analysis of massive amounts of data are made possible via automation and predictive analytics. Automated technologies simplify everyday security chores and minimise human mistake. By spotting trends and abnormalities before they become more serious, predictive analytics foresees possible hazards. Instead of only responding to breaches, this proactive strategy assists organisations in preventing them. Consequently, by lowering operating expenses and enhancing security results, these technologies propel market expansion.

Threat:

Rapid attacker evolution

Rapid attacker evolution refers to the real-time adaptability of AI models. Machine learning algorithms are less successful against novel, invisible dangers since they frequently rely on prior data. The use of AI by cybercriminals is growing, leading to increasingly complex and elusive attacks. This increases the complexity and operational expenses by necessitating frequent model upgrades and retraining. As a result, security firms have ongoing challenges in maintaining effective threat detection.

Covid-19 Impact

The COVID-19 pandemic significantly accelerated the adoption of AI in the cybersecurity - threat intelligence market. As remote work became the norm, organizations faced a surge in cyber threats and data breaches, prompting an urgent need for intelligent, automated security solutions. AI-powered threat detection systems helped companies quickly identify and respond to new and evolving cyber risks. Additionally, limited human intervention during lockdowns emphasized the value of machine learning in monitoring vast digital environments. Overall, the crisis reshaped cybersecurity strategies, positioning AI as a crucial component of defense mechanisms.

The network security segment is expected to be the largest during the forecast period

The network security segment is expected to account for the largest market share during the forecast period by enabling real-time threat detection across complex IT infrastructures. AI-powered tools analyze vast volumes of network traffic to identify anomalies and malicious patterns swiftly. This proactive approach helps organizations prevent breaches before they occur. The growing sophistication of cyberattacks has intensified the demand for AI-driven network defense solutions. As enterprises expand their digital presence, securing networks through intelligent automation becomes essential, boosting market growth.

The anomaly detection segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the anomaly detection segment is predicted to witness the highest growth rate by enabling early identification of suspicious patterns. It helps in detecting zero-day attacks and insider threats that traditional methods often miss. Real-time analysis of network traffic enhances proactive threat mitigation. AI-driven anomaly detection reduces false positives, improving incident response efficiency. Its continuous learning capability strengthens adaptive security frameworks across enterprises.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to the increasing digitization across sectors and rising cyberattacks on critical infrastructure. Countries like China, India, Japan, and South Korea are investing heavily in AI-based cybersecurity tools to protect financial services, government networks, and e-commerce platforms. This demand is further fuelled by rising cloud use and smartphone prevalence. Regional governments are also implementing stricter data protection laws, encouraging enterprises to deploy predictive threat detection and automated response systems, thereby fostering innovation in cybersecurity defense strategies.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to advanced IT infrastructure and the presence of major tech companies. The U.S. leads in developing and deploying AI algorithms that identify, predict, and neutralize cyber threats in real-time. High cybercrime rates targeting banking, healthcare, and defense sectors push demand for AI-powered threat intelligence platforms. Additionally, rising investments in R&D and strategic partnerships among cybersecurity firms enhance threat detection capabilities. Strong regulatory frameworks like CISA and HIPAA further drive adoption of AI to secure digital ecosystems efficiently.

Key players in the market

Some of the key players profiled in the AI in Cybersecurity - Threat Intelligence Market include Palo Alto Networks, CrowdStrike, Fortinet, Darktrace, SentinelOne, Vectra AI, Wiz, Orca Security, Netskope, Check Point, Trellix, Tanium, Trend Micro, Splunk, Deep Instinct, Cybereason, SparkCognition and Armis.

Key Developments:

In April 2025, CrowdStrike entered a strategic partnership with Wipro to integrate its Falcon Next-Gen SIEM and threat intelligence into Wipro's cybersecurity services. This alliance aims to enhance global enterprise Security Operations Centers (SOCs) using AI-powered analytics and automation, streamlining threat detection, response workflows, and reducing operational complexity.

In March 2025, Palo Alto Networks signed a multiyear agreement with the NHL to be its Official Cybersecurity Partner. They'll deploy AI-powered next-gen firewalls, cloud and browser security to protect league operations and fan experiences across arenas ﹣ boosting IoT threat blocking and reducing MTTR.

In October 2024, Fortinet and CrowdStrike integrated Falcon's AI-native endpoint detection with FortiGate firewalls, creating a unified AI-powered threat intelligence platform that enhances attack surface visibility, automates threat response, and streamlines detection-to-remediation across hybrid and cloud network environments.

Components Covered:

  • Solutions
  • Services

Security Types Covered:

  • Network Security
  • Endpoint Security
  • Application Security
  • Cloud Security
  • Web Security
  • Other Security Types

Deployment Modes Covered:

  • Cloud-based
  • On-premises

Technologies Covered:

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Context-Aware Computing
  • Advanced Analytics
  • Other Technologies

Applications Covered:

  • Threat Intelligence
  • Anomaly Detection
  • Intrusion Detection and Prevention
  • Identity and Access Management
  • Risk and Compliance Management
  • Other Applications

End Users Covered:

  • Government and Defense
  • IT and Telecom
  • Healthcare
  • Manufacturing
  • Retail
  • Energy and Utilities
  • Education
  • Transportation and Logistics
  • 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 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 Intelligence Market, By Component

  • 5.1 Introduction
  • 5.2 Solutions
  • 5.3 Services

6 Global AI in Cybersecurity - Threat Intelligence Market, By Security Type

  • 6.1 Introduction
  • 6.2 Network Security
  • 6.3 Endpoint Security
  • 6.4 Application Security
  • 6.5 Cloud Security
  • 6.6 Web Security
  • 6.7 Other Security Types

7 Global AI in Cybersecurity - Threat Intelligence Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Cloud-based
  • 7.3 On-premises

8 Global AI in Cybersecurity - Threat Intelligence Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning (ML)
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Context-Aware Computing
  • 8.5 Advanced Analytics
  • 8.6 Other Technologies

9 Global AI in Cybersecurity - Threat Intelligence Market, By Application

  • 9.1 Introduction
  • 9.2 Threat Intelligence
  • 9.3 Anomaly Detection
  • 9.4 Intrusion Detection and Prevention
  • 9.5 Identity and Access Management
  • 9.6 Risk and Compliance Management
  • 9.7 Other Applications

10 Global AI in Cybersecurity - Threat Intelligence Market, By End User

  • 10.1 Introduction
  • 10.2 Government and Defense
  • 10.3 IT and Telecom
  • 10.4 Healthcare
  • 10.5 Manufacturing
  • 10.6 Retail
  • 10.7 Energy and Utilities
  • 10.8 Education
  • 10.9 Transportation and Logistics
  • 10.10 Other End Users

11 Global AI in Cybersecurity - Threat Intelligence Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Palo Alto Networks
  • 13.2 CrowdStrike
  • 13.3 Fortinet
  • 13.4 Darktrace
  • 13.5 SentinelOne
  • 13.6 Vectra AI
  • 13.7 Wiz
  • 13.8 Orca Security
  • 13.9 Netskope
  • 13.10 Check Point
  • 13.11 Trellix
  • 13.12 Tanium
  • 13.13 Trend Micro
  • 13.14 Splunk
  • 13.15 Deep Instinct
  • 13.16 Cybereason
  • 13.17 SparkCognition
  • 13.18 Armis

List of Tables

  • Table 1 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Solutions (2024-2032) ($MN)
  • Table 4 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Security Type (2024-2032) ($MN)
  • Table 6 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Network Security (2024-2032) ($MN)
  • Table 7 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Endpoint Security (2024-2032) ($MN)
  • Table 8 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Application Security (2024-2032) ($MN)
  • Table 9 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Cloud Security (2024-2032) ($MN)
  • Table 10 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Web Security (2024-2032) ($MN)
  • Table 11 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Other Security Types (2024-2032) ($MN)
  • Table 12 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 13 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 14 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By On-premises (2024-2032) ($MN)
  • Table 15 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Technology (2024-2032) ($MN)
  • Table 16 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 17 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 18 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Context-Aware Computing (2024-2032) ($MN)
  • Table 19 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Advanced Analytics (2024-2032) ($MN)
  • Table 20 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 21 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Application (2024-2032) ($MN)
  • Table 22 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Threat Intelligence (2024-2032) ($MN)
  • Table 23 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Anomaly Detection (2024-2032) ($MN)
  • Table 24 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Intrusion Detection and Prevention (2024-2032) ($MN)
  • Table 25 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Identity and Access Management (2024-2032) ($MN)
  • Table 26 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Risk and Compliance Management (2024-2032) ($MN)
  • Table 27 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 28 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By End User (2024-2032) ($MN)
  • Table 29 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Government and Defense (2024-2032) ($MN)
  • Table 30 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By IT and Telecom (2024-2032) ($MN)
  • Table 31 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 32 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 33 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Retail (2024-2032) ($MN)
  • Table 34 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Energy and Utilities (2024-2032) ($MN)
  • Table 35 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Education (2024-2032) ($MN)
  • Table 36 Global AI in Cybersecurity - Threat Intelligence Market Outlook, By Transportation and Logistics (2024-2032) ($MN)
  • Table 37 Global AI in Cybersecurity - Threat Intelligence 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.