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What is the Growth Potential of the AIDriven Threat Intelligence Market, and How Will It Perform by 2034?

The market size for AI-based threat intelligence has seen significant expansion lately. It is predicted to increase from $6.31 billion in 2024 to $7.87 billion in 2025, with a compound annual growth rate (CAGR) of 24.7%. The remarkable growth experienced during the historic period can be traced back to the burgeoning demand for instantaneous threat detection systems, the surge in AI-powered cybersecurity solutions, the growth of startups focusing on AI-enabled threat intelligence, increased availability of big data for threat intelligence purposes, and the escalating usage of predictive analytics.

Over the upcoming years, the market size of AI-driven threat intelligence is predicted to expand significantly. Forecasted to reach a value of $18.82 billion in 2029 with a compound annual growth rate (CAGR) of 24.4%, its growth can be largely linked to several factors. These consist of the surge in cyber threats, advances in digital technologies, rising dependence on digital infrastructure, increased governmental spending on cybersecurity, and broader embrace of cloud computing. The forecast period also expects a series of trends such as cutting-edge technological developments, AI-based predictive analytics, real-time threat detection, automated patch administration, and edge computing.

How Are the key drivers Contributing to the Expansion of the AIDriven Threat Intelligence Market?

The surge in the regularity and complexity of cyber breaches is projected to fuel the expansion of the AI-driven threat intelligence market. Cyber breaches involve planned attacks by hackers to undermine the security of computer systems, networks, or devices. The escalation in regularity and complexity of these breaches can be linked to elements such as heightened digital transformation, evolving strategies of cybercriminals, and insufficient security protocols. AI-driven threat intelligence fortifies cybersecurity by examining extensive data to identify trends and anticipate potential cyber breaches, facilitating quicker identification and reaction to emerging threats. It enhances threat detection precision and diminishes response durations. For example, the Australian Cyber Security Centre, a cybersecurity organization based in Australia, stated that in February 2023, the number of cybercrime reports collected in 2022 was 76,000, an increase of 13% from the previous year. Consequently, the mounting regularity and complexity of cyber breaches are estimated to fuel the expansion of the AI-driven threat intelligence market.

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Who Are the Major Companies Shaping the Competitive Landscape of the AIDriven Threat Intelligence Market?

Major companies operating in the AI-driven threat intelligence market are Google LLC, Broadcom Incorporated, NTT DATA Corporation, Juniper Networks Inc., Fortinet Inc., Check Point Software Technologies Ltd., Trellix Inc., CrowdStrike Holdings Inc., Kaspersky Lab, SentinelOne Inc., Zerofox Holdings Incorporated, McAfee Corp., OPSWAT Inc., Flashpoint Inc., Anomali Inc., SparkCognition Inc., Cyware Labs Inc., Cyble Inc., ThreatConnect Inc., Resecurity Inc., Trend Micro Incorporated, Silobreaker AB

What Are the Key Emerging Trends in the AIDriven Threat Intelligence Market for the Next Decade?

Leading businesses in the AI-infused threat intelligence market are honing their capabilities in threat identification by developing cutting-edge technologies such as machine learning. This technology allows systems to refine and learn from data without programmed instruction. Trellix, a cybersecurity firm based in the US, for instance, introduced Trellix Wise in May 2024. This sophisticated GenAI tools suite, integrated with the Trellix XDR platform, is designed to refine cybersecurity operations. The platform automates key tasks such as threat identification, alert management, and investigation – enhancing the productivity of security teams. Trellix Wise leverages AI-based analytics to boost threat prevention, detection, and remediation features. It provides thorough protection in both physical and cloud settings, delivering real-time threat intelligence. This solution empowers businesses to efficiently address formidable security challenges such as zero-day attacks, ransomware, and supply chain threats, concurrently diminishing operational expenses and the demand for specialized knowledge.

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What Are the Core Categories and Segments in the AIDriven Threat Intelligence Market Driving Demand and Growth?

The AI-driven threat intelligence market covered in this report is segmented –

1) By Component: Solution, Services

2) By Deployment Mode: Cloud, On-Premise

3) By Organization Size: Large Enterprises, Small And Medium-Sized Enterprises (SMEs)

4) By Application: Threat Detection, Risk Management, Security Operations, Incident Response, Other Application

5) By Industry Vertical: Banking, Financial Services, and Insurance, Government And Defense, Information Technology And Telecom, Healthcare And Lifesciences, Retail And E-commerce, Energy And Utilities, Manufacturing, Other Industry Verticals

Subsegments:

1) By Solution: Threat Detection and Prevention Solutions, Threat Intelligence Platforms, AI-Powered Security Analytics, Incident Response Solutions

2) By Services: Managed Threat Intelligence Services, Consulting Services, Incident Response Services, Threat Hunting Services

Which Regions Are Making a Mark in the AIDriven Threat Intelligence Market Growth?

North America was the largest region in the AI-driven threat intelligence market in 2024. The regions covered in the AI-driven threat intelligence market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

How Is the AIDriven Threat Intelligence Market Categorized and Defined in the Industry?

AI-driven threat intelligence refers to the use of artificial intelligence (AI) and machine learning technologies to gather, analyze, and interpret data for identifying potential cybersecurity threats and vulnerabilities. It is used to identify and analyze cybersecurity threats in real-time by processing large volumes of data with AI and machine learning. It helps organizations predict vulnerabilities, automate threat detection, enhance incident response, and improve overall security measures.

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