The Business Research Company’s report on the AI-Driven Threat Intelligence Market provides insights into the global market size, growth rate, regional distribution, competitive landscape, key segments, emerging trends, and strategic opportunities.
What are the key drivers behind the ai-driven threat intelligence market’s growth in recent years?
The rising frequency and sophistication of cyberattacks are expected to propel the growth of the AI-driven threat intelligence market. Cyberattacks refer to intentional attempts by hackers to compromise the security of computer systems, networks, or devices. The rising frequency and sophistication of cyberattacks can be attributed to factors such as increased digital transformation, evolving tactics of cybercriminals, and inadequate security measures. AI-driven threat intelligence enhances cybersecurity by analyzing vast amounts of data to detect patterns and predict potential cyberattacks, enabling faster identification and response to emerging threats. It improves threat detection accuracy and reduces response times. For instance, in February 2023, according to the Australian Cyber Security Centre, an Australia-based cybersecurity agency, the number of cybercrime reports received in 2022 was 76,000, up 13% from the previous year. Therefore, rising frequency and sophistication of cyberattacks are expected to propel the growth of the AI-driven threat intelligence market.
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How does the future projection of the ai-driven threat intelligence market size compare to its historical growth?
The AI-driven threat intelligence market size has grown exponentially in recent years. It will grow from $6.31 billion in 2024 to $7.87 billion in 2025 at a compound annual growth rate (CAGR) of 24.7%. The growth in the historic period can be attributed to rise in demand for real-time threat detection, growth in AI-driven cybersecurity solutions, rise in the number of AI-powered threat intelligence startups, growth in the availability of big data for threat intelligence, and increasing use of predictive analytics.
The AI-driven threat intelligence market size is expected to see exponential growth in the next few years. It will grow to $18.82 billion in 2029 at a compound annual growth rate (CAGR) of 24.4%. The growth in the forecast period can be attributed to increasing cyber threats, rising digital technologies increasing reliance on digital infrastructure, increasing government cybersecurity investments, and increasing adoption of cloud computing. Major trends in the forecast period include technological advancements, AI-powered predictive analytics, real-time threat detection, automated patch management, and edge computing.
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Which key players are shaping the future of the ai-driven 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 trends will propel the growth and evolution of the ai-driven threat intelligence market?
Major companies operating in the AI-driven threat intelligence market are focusing on developing advanced technologies such as machine learning to enhance their capabilities in threat detection. Machine learning enables systems to learn and improve from data without the need for explicit programming. For instance, in May 2024, Trellix, a US-based cybersecurity company, launched Trellix Wise, an advanced suite of generative artificial intelligence (GenAI) tools integrated with the Trellix XDR (extended detection and response) platform to streamline cybersecurity operations. The platform automates essential tasks such as threat detection, alert triage, and investigation, boosting the efficiency of security teams. By utilizing AI-powered analytics, Trellix Wise enhances threat prevention, detection, and remediation capabilities. It offers comprehensive protection across both on-premises and cloud environments, providing real-time threat intelligence. This solution enables organizations to quickly tackle complex security issues like zero-day attacks, ransomware, and supply chain risks while reducing operational costs and the need for specialized expertise.
Which regions are expected to become dominant players in the ai-driven threat intelligence market?
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.
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What are the emerging key segments in the ai-driven threat intelligence market, and how are they evolving?
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
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How is the ai-driven threat intelligence market defined, and what are its core characteristics?
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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