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市场调查报告书
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1776716

人工智慧 - 2032 年网路安全市场威胁情报预测：按组件、安全类型、部署模式、技术、应用、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球网路安全市场的人工智慧威胁情报预计在 2025 年将达到 204.6 亿美元，预计到 2032 年将达到 922.2 亿美元，预测期内的复合年增长率为 24%。

网路安全中的人工智慧 - 威胁情报是指利用人工智慧即时侦测、评估和消除线上威胁。它利用机器学习、自然语言处理和数据分析来识别诈欺、预测攻击并自动回应。人工智慧透过消化来自各种来源的大量威胁数据，提升情境察觉和决策能力。它可以识别勒索软体、网路钓鱼和恶意软体的趋势，从而实现主动防御策略。这种巧妙的自动化技术显着提高了威胁侦测的速度、准确性和回应时间，增强了组织的整体网路安全态势。

网路攻击日益复杂

人工智慧主导的恶意软体和零日漏洞等进阶威胁需要更快、更聪明的侦测方法。即时侦测复杂的攻击模式往往是传统安全解决方案面临的挑战。透过自动化资料处理和快速识别异常，人工智慧可以提升威胁情报。它能够在损害发生之前预测并消除威胁，从而实现主动防御。随着越来越多的企业依赖人工智慧解决方案来领先骇客攻击，该行业正在蓬勃发展。

资料隐私和监管风险

由于CCPA和GDPR等严格的资料隐私法规，存取训练AI模型所需的海量资料集受到限制。企业在国际范围内收集或传播威胁情报时，经常会遇到合规性问题。这些法律限制可能会阻碍AI的采用，并降低即时侦测威胁的能力。监管的不确定性也阻碍了对尖端AI驱动的网路安全工具的投资。此外，由于担心违规处罚，企业不愿充分利用AI功能。

自动化和预测分析

自动化和预测分析技术使即时监控和快速分析大量资料成为可能。自动化技术简化了常规安全业务，并最大限度地减少了人为错误。预测分析能够提前发现趋势和异常，在潜在威胁变得严重之前就预见它们。这种主动策略不仅可以帮助组织应对违规行为，还能在违规行为发生之前预防。因此，这些技术能够降低营运成本并改善安全效果，从而推动市场扩张。

攻击者正在快速进化

攻击者的快速进化要求人工智慧模型具备即时适应性。机器学习演算法通常依赖先前的数据，这使得它们在应对新的、未知的威胁时效果不佳。网路犯罪分子对人工智慧的使用日益增多，导致攻击日益复杂且难以捉摸。这需要频繁升级和重新训练模型，从而增加了复杂性和营运成本。因此，安全公司在维持有效的威胁侦测方面面临持续的挑战。

COVID-19的影响

新冠疫情显着加速了人工智慧在网路安全和威胁情报市场的普及。随着远距办公成为常态，企业面临的网路威胁和资料外洩激增，迫切需要智慧自动化的安全解决方案。基于人工智慧的威胁侦测系统可帮助企业快速识别并应对不断演变的网路风险。此外，封锁期间有限的人工干预凸显了机器学习在监控庞大数位环境中的价值。总体而言，这场危机重塑了网路安全战略，并将人工智慧定位为防御机制的关键组成部分。

网路安全领域预计将成为预测期内最大的领域

网路安全领域预计将在预测期内占据最大的市场占有率，这得益于其能够在复杂的IT基础设施中实现即时威胁侦测的能力。人工智慧工具可以分析大量网路流量，并快速识别异常和恶意模式。这种主动方法使企业能够防患于未然。网路攻击日益复杂化，推动了对人工智慧驱动的网路防御解决方案的需求。随着企业数位化业务的扩张，透过智慧自动化保护网路安全已变得势在必行，从而推动了市场的成长。

异常检测部分预计在预测期内达到最高复合年增长率

异常检测能够及早识别可疑模式，预计在预测期内将达到最高成长率。它有助于检测传统方法经常遗漏的零时差攻击和内部威胁。即时网路流量分析可增强主动威胁缓解能力。基于人工智慧的异常检测可减少误报并提高事件回应效率。持续学习能力可增强整个企业的自适应安全框架。

比最大的地区

由于各行业数位化不断提高以及针对关键基础设施的网路攻击增多，预计亚太地区将在预测期内占据最大市场占有率。中国、印度、日本和韩国等国家正大力投资以人工智慧为基础的网路安全工具，以保护金融服务、政府网路和电商平台。云端运算应用和智慧型手机普及率的不断提高进一步推动了这项需求。该地区各国政府也正在推出更严格的资料保护法，促使企业采用预测性威胁侦测和自动回应系统，进而推动网路安全防御策略的创新。

复合年增长率最高的地区：

在预测期内，北美预计将凭藉其先进的IT基础设施和大型科技公司的存在，实现最高的复合年增长率。美国在即时识别、预测和消除网路威胁的人工智慧演算法的开发和部署方面处于领先地位。针对银行、医疗保健和国防领域的网路犯罪高发，推动了对人工智慧驱动的威胁情报平台的需求。此外，网路安全公司之间不断增加的研发投入和策略伙伴关係关係，正在增强威胁侦测能力。 CISA和HIPAA等强有力的法规结构将进一步推动人工智慧的应用，以有效地保护数位生态系统。

免费客製化服务

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  • 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市场进行估计、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
  • 应用分析
  • 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

5. 全球网路安全人工智慧-威胁情报市场（按组件）

  • 解决方案
  • 服务

6. 全球网路安全人工智慧-威胁情报市场（依安全类型）

  • 网路安全
  • 端点安全
  • 应用程式安全
  • 云端安全
  • 网路安全
  • 其他安全类型

7. 全球网路安全领域人工智慧威胁情报市场（按部署模式）

  • 云端基础
  • 本地

8. 全球网路安全人工智慧威胁情报市场（按技术）

  • 机器学习（ML）
  • 自然语言处理（NLP）
  • 情境感知计算
  • 进阶分析
  • 其他技术

9. 全球网路安全人工智慧威胁情报市场（按应用）

  • 威胁情报
  • 异常检测
  • 入侵侦测与预防
  • 身分和存取管理
  • 风险与合规管理
  • 其他用途

第 10 章。全球网路安全 AI - 威胁情报市场（按最终用户）

  • 政府和国防
  • 资讯科技和通讯
  • 卫生保健
  • 製造业
  • 零售
  • 能源和公共产业
  • 教育
  • 运输/物流
  • 其他最终用户

11. 全球网路安全市场人工智慧威胁情报（按地区）

  • 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

第十二章 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

第十三章 公司概况

  • 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.