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全球安全市场人工智慧 (AI) - 2023-2030

Global Artificial Intelligence (AI) in Security Market - 2023-2030

出版日期: 2023年10月11日 | 出版商:

DataM Intelligence | 英文 204 Pages | 商品交期: 约2个工作天内

价格

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简介目录

概述：

2022年，全球人工智慧（AI）安全市场规模达到210亿美元，预计2030年将达到542亿美元，2023-2030年预测期间复合年增长率为19.1%。

网路威胁的数量和复杂性不断增加，以及实体安全问题，正在推动组织寻求更先进和自动化的安全解决方案。数位时代产生的大量资料对于人类分析师来说难以有效处理。人工智慧对海量资料集的即时分析有助于发现漏洞和风险。

例如，2023 年 9 月 21 日，领先的网路安全公司麦克菲 (McAfee) 最近推出了一项创新的人工智慧诈骗防护功能，旨在即时识别和阻止诈骗。随着网路威胁不断发展并变得更加复杂，这项技术代表了网路安全的重大进步。由人工智慧驱动的诈骗防护系统利用机器学习演算法和即时资料分析来侦测诈欺活动。

亚太地区是全球人工智慧（AI）安全市场不断增长的地区之一，占据超过3/7的市场份额，近年来该地区网路威胁和攻击激增。随着对数位技术和互联网的日益依赖，该地区的企业和政府面临持续的网路安全挑战。人工智慧提供了先进的威胁侦测和回应能力，使其成为应对这些威胁的重要工具。

动态：

政府措施提振市场

政府机构经常分配大量资金来支持安全领域的人工智慧研究和开发，这些投资可以资助人工智慧技术、网路安全解决方案和相关项目的创建。财政支持鼓励创新并加速人工智慧在安全领域的采用。各国政府为人工智慧在安全领域的应用制定监管框架和标准，这些法规可以确保负责任的人工智慧使用、资料隐私和道德实践。

根据卡内基国际和平基金会的报告，到2022年，中国政府实施了三种不同的人工智慧治理方法，每种方法都由官僚机构的不同部门倡导，而这个强大的监管机构专注于针对特定人工智慧应用的基于规则的治理。它发布了规范网路推荐演算法的规则草案，包括演算法可解释性和用户权利保护的规定。中国国家互联网资讯办公室正在製定为期三年的路线图，以管理所有互联网演算法，涉及多个监管机构。

利用人工智慧实施机器学习会引发威胁和恶意软体

网路威胁和恶意软体攻击变得越来越复杂和适应性强。传统的基于签名的方法不再足以检测和预防这些高级威胁。机器学习和人工智慧可以分析模式和行为来识别新的威胁。大型资料集的可用性对于训练模型识别正常和恶意行为至关重要。

根据参议院军事委员会的报告，美国参议院军事委员会网路小组委员会于2022 年5 月举行了一次国会听证会，重点讨论了在网路空间领域利用人工智慧（AI）和机器学习（ML）的重要性。来自谷歌和乔治城大学安全与新兴技术中心的主要代表参加了听证会。

技术进步推动市场发展

恶意软体、勒索软体和网路钓鱼攻击等网路威胁日益复杂，对人工智慧驱动的安全解决方案的成长产生了强烈需求。即时人工智慧可以准确、快速地分析大量资料，这对于识别表明安全漏洞的模式和异常至关重要。它还可以自动执行许多安全任务例程，例如监控网路流量和标记可疑活动。

例如，2022年9月20日，NVIDIA推出了用于高精度边缘AI的NVIDIA IGX平台，旨在增强製造、物流和医疗保健等行业的安全性和安全性，该平台具有可编程性和可配置性，可为各种场景提供适应性强的解决方案。产业需求。它旨在改善物理世界环境中的人机协作和安全性。 IGX 专注于提高在製造和物流等监管严格的行业中运作的自主系统的安全性。

错误识别和资料洩露

人工智慧驱动的安全系统会产生不正确的识别威胁活动，称为误报和漏报（未能侦测到实际威胁）。在准确的威胁检测和最大限度地减少误报之间实现适当的平衡可能具有挑战性。网路犯罪分子可以利用对抗性攻击来操纵人工智慧演算法。他们可以製作旨在逃避检测或误导人工智慧系统的恶意输入，从而降低其可靠性。

根据 IBM 2023 年的报告，全球资料外洩的平均成本升至 445 万美元的历史新高，比前三年成长了 15%。在同一三年期间，检测和升级成本显着增加了 42%，目前它们构成了违规费用的大部分，这表明违规调查已转向更复杂的调查。

The increasing number and rising complexity for cyber threats, as well as physical security concerns, are pushing organizations to seek more advanced and automated security solutions. The sheer volume of data generated in the digital age is overwhelming for human analysts to process effectively. Real-time analysis of enormous data sets by AI could help to find vulnerabilities and risks.

For instance, on 21 September 2023, McAfee, a leading cybersecurity company, recently unveiled an innovative AI-powered scam protection feature designed to identify and block scams in real-time. As cyber threats continue to evolve and become more sophisticated, this technology represents a significant advancement in online security. The AI-powered scam protection system leverages machine learning algorithms and real-time data analysis to detect fraudulent activities.

Asia-Pacific is among the growing regions in the global artificial intelligence (AI) in security market covering more than 3/7th of the market and the region has witnessed a surge in cyber threats and attacks in recent years. With the growing reliance on digital technologies and the internet, businesses and governments in the region face constant cybersecurity challenges. AI offers advanced threat detection and response capabilities, making it a crucial tool for addressing these threats.

Dynamics:

Government Initiatives Boost the Market

Government agencies often allocate substantial funds to support AI research and development in the security domain and these investments can fund the creation of AI technologies, cybersecurity solutions and related projects. Financial support encourages innovation and accelerates the adoption of AI in security. Governments establish regulatory frameworks and standards for AI adoption in security, these regulations can ensure responsible AI usage, data privacy and ethical practices.

According to a report from the Carnegie Endowment for International Peace, in 2022, the Chinese government has implemented three distinct approaches to artificial intelligence governance, each championed by different branches of the bureaucracy and this powerful regulator focuses on rule-based governance for specific AI applications. It released draft rules for regulating internet recommendation algorithms, including provisions for algorithmic interpretability and user rights protection. The Cyberspace Administration of China is working on a three-year roadmap for governing all internet algorithms, involving multiple regulators.

Implementing Machine Learning with AI Enables Threats and Malware

Cyber threats and malware attacks have grown increasingly sophisticated and adaptive. Traditional signature-based approaches are no longer sufficient to detect and prevent these advanced threats. ML and AI can analyze patterns and behaviors to identify novel threats. The availability of large datasets is crucial for training models to recognize normal and malicious behavior.

According to a report from the Senate Committee on Armed Services, U.S. Senate Armed Services Committee's Subcommittee on Cyber held a congressional hearing in May 2022 focused on the significance of leveraging artificial intelligence (AI) and machine learning (ML) in the realm of cyberspace. The hearing featured key representatives from Google and the Center for Security and Emerging Technology at Georgetown University.

Technology Advancement Boosts the Market

The rising complexity of cyber threats like malware, ransomware and phishing attacks has created a strong demand for the growth of AI-powered security solutions. In real-time AI analyzes a large volume of data accurately and quickly which is essential for identifying patterns and anomalies that indicate security breaches. It also automates many routines of security tasks like monitoring network traffic and flagging suspicious activities.

For instance, on 20 September 2022, NVIDIA introduced the NVIDIA IGX platform for high-precision edge AI, designed to enhance security and safety in industries such as manufacturing, logistics and healthcare and this platform is programmable and configurable, providing an adaptable solution for various industries needs. It aims to improve human-machine collaboration and safety in physical-world environments. IGX focuses on improving the safety and security of autonomous systems operating in industries with stringent regulations, such as manufacturing and logistics.

Incorrect Identification and Data Breaches

AI-powered security systems generate incorrect identification threat activity called false positives and false negatives (failing to detect actual threats). Achieving the right balance between accurate threat detection and minimizing false alarms can be challenging. Cybercriminals can use adversarial attacks to manipulate AI algorithms. They can craft malicious inputs designed to evade detection or mislead AI systems, making them less reliable.

According to an IBM report in 2023, the average cost of a data breach worldwide increased to an all-time high of US$ 4.45 Million, a 15% rise over the previous three years. Over the same three-year period, detection and escalation costs significantly increased by 42% and they now make up the majority of breach expenses, indicating a shift toward more involved breach investigations.

Segment Analysis:

The global artificial intelligence (AI) in security market is segmented based on offering, deployment type, security type, technology, application, end-user and region.

AI-Powered Security Solutions in Cloud Environments

Advanced security solutions are becoming more and more important as cyber threats become more complex and widespread. Massive volumes of data may be instantly analyzed by AI, enabling real-time danger detection and reaction. The size and complexity of cloud environments provide many opportunities for possible threats to enter. AI can handle the scale of cloud computing, continuously monitoring and analyzing network traffic, user behavior and system logs.

For instance, on 29 August 2023, Google Cloud leveraged its acquisition of cybersecurity firm Mandiant to offer managed threat-hunting services to its Chronicle Security Operations customers. The service, Mandiant Hunt for Chronicle Security Operations, will provide access to Mandiant's threat-hunting intelligence and personnel within customers' Chronicle environments.

Geographical Penetration:

Rising Cyber Threats Boost the Market

North America is dominating the global artificial intelligence (AI) in security market covering more than 1/3rd of the market particularly U.S. and Canada face a growing and evolving cyber threat landscape. The frequency and sophistication of cyberattacks, data breaches and ransomware incidents have increased. For efficiently identifying, reducing and responding to these dangers, AI is viewed as a crucial tool.

For instance, on 24 April 2023, North Dakota used artificial intelligence and machine learning to enhance its cybersecurity efforts. The state partnered with cybersecurity vendor Palo Alto Networks to build an autonomous security operations center (SOC) to protect 250,000 endpoints, including schools, government offices and police stations. AI and ML help automate low-level security incident resolution and address backlogged incidents, allowing human analysts to focus on more complex tasks.

Competitive Landscape

The major global players in the market include: Palo Alto Networks Inc., Trellix, Darktrace,Cyclance Inc., Fortinet, Inc., Nozomi Networks Inc., , ESET, s.r.o., ThreatMetrix, Inc. and Vectra AI, Inc..

COVID-19 Impact Analysis:

The lockdowns and social distancing measures in place organizations have turned to AI-driven surveillance and security solutions for remote monitoring of facilities and properties. AI-powered cameras and sensors can detect anomalies and potential security threats, reducing the need for physical security personnel on-site. AI-driven cybersecurity tools have become more critical as cyberattacks surged during the pandemic.

AI-driven cybersecurity tools have become more critical as cyberattacks surged during the pandemic. AI systems examine huge quantities of data to quickly identify and address dangers, assisting enterprises in safeguarding their networks and data. Security teams are able to focus on more complex and strategic aspects of security operations since AI has been used to automate regular security duties, such as security incident investigation and response.

Cyberattacks and cyber espionage activities tend to rise during periods of geopolitical crisis. Critical infrastructure, governmental institutions and private organizations may be the target of state-sponsored as well as non-state actors. Cybersecurity solutions powered by AI will be essential in identifying and countering these attacks. The conflict may lead to geopolitical tensions affecting international collaboration in AI research and development.

AI Impact

AI-powered security solutions analyze large amounts of data in real-time which allows for the rapid detection of security threats and anomalies. Machine learning algorithms identify patterns and behaviors associated with cyberattacks and enable quicker response times. Based on previous data and continuous network monitoring, AI may be used to anticipate potential security risks and this proactive approach helps organizations get ready for and stop cyberattacks before they happen.

AI-driven security tools can analyze user and entity behavior to identify suspicious activities. By understanding typical user behavior, AI can flag deviations that may indicate a security breach. AI can automate responses to security incidents, such as isolating compromised devices, blocking malicious traffic or initiating incident response procedures and this automation reduces the response time and minimizes human error.

For instance, on 11 September 2022, Lockheed Martin, in collaboration with the University of Iowa's Operator Performance Laboratory, successfully demonstrated the use of artificial intelligence (AI) in coordinating manned and unmanned aircraft in a simulated air-to-ground mission. The AI agents provided data for rapid decision-making and reduced pilot workload. The success of this demonstration could pave the way for AI-enabled collaboration in Joint All Domain Operations and autonomous mission completion.

Russia- Ukraine War Impact

The conflict may disrupt the global supply chain for technology components, including semiconductors and other hardware critical for AI infrastructure. Supply chain disruptions can lead to delays in AI projects and impact the availability of AI-powered security solutions. Organizations and governments reallocate resources and priorities in AI development and deployment based on evolving security threats and national interests and which leads to a shift in focus towards AI technologies with direct relevance to security and defense.

By Offering

Hardware
Software
Services

By Deployment Type

Cloud
On-Premise

By Security Type

Network Security
Endpoint Security
Application Security
Cloud Security

By Technology

Machine Learning
Natural Language Processing
Context-Aware Computing

By Application

Identity and Access Management
Risk and Compliance Management
Data Loss Prevention
Unified Threat Management
Security and Vulnerability Management
Others

By End-User

BFSI
Retail
Defense
Manufacturing
Enterprise
Others

By Region

North America
- U.S.
- Canada
- Mexico

Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe

South America
- Brazil
- Argentina
- Rest of South America

Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific

Middle East and Africa

Key Developments

In October 2022, UBS and Microsoft recently announced an expansion of their relationship. UBS intends to run more than 50% of its applications, including crucial workloads, on Azure during the following five years. This action intends to update UBS's global technological infrastructure and is consistent with the bank's "cloud first" policy.
In September 2022, Kyndryl, an independent company spun off from IBM's Managed Infrastructure Services business and Elastic, the company behind Elasticsearch and the Elastic Stack, announced an expanded partnership to enable data observability. The collaboration aims to help organizations gain better insights from their data by leveraging Kyndryl's expertise in data and AI operations alongside Elastic's solutions for search, observability and security.
In March 2022, in collaboration with Mastercard, Samsung is developing biometric credit cards with integrated fingerprint scanners and these cards will feature numerous distinct Samsung chips and be compatible with the majority of point-of-sale terminals that support Mastercard chip payments.

Why Purchase the Report?

To visualize the global artificial intelligence (AI) in security market segmentation based on offering, deployment type, security type, technology, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of artificial intelligence (AI) in security market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global artificial intelligence (AI) in security market report would provide approximately 85 tables, 93 figures and 204 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Offering
3.2. Snippet by Deployment Type
3.3. Snippet by Security Type
3.4. Snippet by Technology
3.5. Snippet by Application
3.6. Snippet by End-User
3.7. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Government Initiatives Boost the Market
  - 4.1.1.2. Implementing Machine Learning with AI Enables Threats and Malware
  - 4.1.1.3. Technology Advancement Boosts the Market
- 4.1.2. Restraints
  - 4.1.2.1. Incorrect Identification and Data Breaches
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Offering

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 7.1.2. Market Attractiveness Index, By Offering
7.2. Hardware*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Software
7.4. Services

8. By Deployment Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 8.1.2. Market Attractiveness Index, By Deployment Type
8.2. Cloud*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. On-Premise

9. By Security Type

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 9.1.2. Market Attractiveness Index, By Security Type
9.2. Network Security*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Endpoint Security
9.4. Application Security
9.5. Cloud Security

10. By Technology

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.1.2. Market Attractiveness Index, By Technology
10.2. Machine Learning*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Natural Language Processing
10.4. Context-Aware Computing

11. By Application

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.1.2. Market Attractiveness Index, By Application
11.2. Identity and Access Management*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Risk and Compliance Management
11.4. Data Loss Prevention
11.5. Unified Threat Management
11.6. Security and Vulnerability Management
11.7. Others

12. By End-User

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.1.2. Market Attractiveness Index, By End-User
12.2. BFSI*
- 12.2.1. Introduction
- 12.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
12.3. Retail
12.4. Defense
12.5. Manufacturing
12.6. Enterprise
12.7. Others

13. By Region

13.1. Introduction
- 13.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 13.1.2. Market Attractiveness Index, By Region
13.2. North America
- 13.2.1. Introduction
- 13.2.2. Key Region-Specific Dynamics
- 13.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 13.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 13.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.2.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.2.9.1. U.S.
  - 13.2.9.2. Canada
  - 13.2.9.3. Mexico
13.3. Europe
- 13.3.1. Introduction
- 13.3.2. Key Region-Specific Dynamics
- 13.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 13.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 13.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.3.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.3.9.1. Germany
  - 13.3.9.2. UK
  - 13.3.9.3. France
  - 13.3.9.4. Italy
  - 13.3.9.5. Russia
  - 13.3.9.6. Rest of Europe
13.4. South America
- 13.4.1. Introduction
- 13.4.2. Key Region-Specific Dynamics
- 13.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 13.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 13.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.4.9.1. Brazil
  - 13.4.9.2. Argentina
  - 13.4.9.3. Rest of South America
13.5. Asia-Pacific
- 13.5.1. Introduction
- 13.5.2. Key Region-Specific Dynamics
- 13.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 13.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 13.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.5.9.1. China
  - 13.5.9.2. India
  - 13.5.9.3. Japan
  - 13.5.9.4. Australia
  - 13.5.9.5. Rest of Asia-Pacific
13.6. Middle East and Africa
- 13.6.1. Introduction
- 13.6.2. Key Region-Specific Dynamics
- 13.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 13.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Security Type
- 13.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.6.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

14. Competitive Landscape

14.1. Competitive Scenario
14.2. Market Positioning/Share Analysis
14.3. Mergers and Acquisitions Analysis

15. Company Profiles

15.1. Palo Alto Networks Inc.*
- 15.1.1. Company Overview
- 15.1.2. Product Portfolio and Description
- 15.1.3. Financial Overview
- 15.1.4. Key Developments
15.2. Trellix
15.3. Darktrace
15.4. Cyclance Inc.
15.5. Fortinet, Inc.
15.6. Nozomi Networks Inc.
15.7. Bitdefender
15.8. ESET, s.r.o.
15.9. ThreatMetrix, Inc.
15.10. Vectra AI, Inc.

LIST NOT EXHAUSTIVE

16. Appendix

16.1. About Us and Services
16.2. Contact Us

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+886-2-2729-4219

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