欺骗科技市场 - 全球产业规模、份额、趋势、机会、预测：按部署方式、组件、欺骗技术堆迭、最终用户、地区和竞争对手划分，2021-2031 年

全球欺骗科技市场预计将从 2025 年的 48.3 亿美元大幅成长至 2031 年的 113.1 亿美元，复合年增长率达 15.24%。

该市场专注于部署主动防御措施，例如诱饵、诱饵令牌和蜜罐令牌，这些措施旨在模拟合法资产，从而迷惑已突破网路边界的攻击者。该行业的主要驱动力是高级持续性威胁 (APT) 的日益频繁，以及在攻击生命週期早期检测横向移动以缩短攻击者潜伏时间的迫切营运需求。此外，传统预防策略在阻止复杂攻击方面的不足也推动了对这些高精度检测解决方案的需求，与标准异常检测系统相比，这些解决方案因误报率更低而备受青睐。

儘管安全优势显而易见，但市场仍面临许多挑战，包括营运复杂性以及有效管理这些环境所需专业知识的短缺。鑑于许多组织面临的人才短缺问题日益严重，成功实施欺骗策略通常需要高度成熟的技术。根据ISACA预测，到2024年，全球45%的网路安全专业人员将把员工培训不足列为一项重大挑战，这项短缺直接阻碍了欺骗技术等先进主动防御层的部署和维护。

市场驱动因素

进阶持续性威胁 (APT) 和勒索软体攻击的日益频繁和复杂化是全球欺骗技术市场的主要驱动力。随着攻击者越来越多地绕过边界防御，企业被迫部署主动防御层，以识别已绕过预防机制的威胁。欺骗技术透过部署逼真的诱饵来迷惑攻击者，将其註意力从关键资产上转移，从而应对这项挑战。这些事件造成的经济损失加剧了投资的迫切性。根据 Sophos 于 2024 年 4 月发布的《2024 年勒索软体现况报告》，平均赎金已飙升 500%，达到 200 万美元。这种成本不断攀升的趋势要求企业开发能够在造成重大损失之前中断攻击链的侦测技术。

因此，早期主动的威胁侦测和缩短攻击者潜伏时间正在推动市场发展。被动的安全措施往往无法快速侦测到横向移动，使攻击者得以建立持久化防御并窃取资料。欺骗平台透过在诱饵启动时立即向安全团队发出警报来缓解这项挑战，从而显着缩短检测时间。随着攻击生命週期的加速，这种速度至关重要。根据 CrowdStrike 发布的 2024 年 2 月全球威胁报告，入侵网路犯罪系统的平均时间已缩短至仅 62 分钟。为了应对这种快速变化，企业正越来越多地采用欺骗工具来儘早发现攻击者。此外，安全漏洞造成的经济损失也正在推动这项变革。 IBM 报告称，2024 年全球资料外洩的平均成本将达到 488 万美元，这促使企业采用各种技术来最大限度地减少资料外洩的影响。

市场挑战

部署欺骗性环境的维运复杂性，加上熟练人员的严重短缺，构成了全球欺骗性技术市场扩张的主要障碍。虽然欺骗性技术能够提供高度精确的侦测，但管理这些主动防御层需要精湛的技能，才能设定逼真的诱饵并准确解读攻击者的行为。由于缺乏内部专业知识来维护这些解决方案，企业往往犹豫不决，担心维运负担过重。这种技能的匮乏迫使安全团队优先考虑基础控製而非高阶检测策略，从而导致销售週期延长和市场渗透率受限。

人才短缺从根本上限制了欺骗平台的潜在市场。根据ISC2预测，到2024年，全球网路安全人才缺口预计将达到创纪录的480万人，凸显了能够管理复杂安全架构的人员严重匮乏。鑑于安全运行中心的人员配备本就不足，引入需要持续协调和专家分析的额外层级变得难以令人信服。因此，招募和留住合格分析师的困难直接导致企业无法分配足够的人力资源来有效运作这些先进工具，这又直接导致欺骗技术的应用速度放缓。

市场趋势

人工智慧整合在动态自主诱饵生成领域的应用正在重塑市场格局，使平台能够部署可适应对抗行为的自学习诱饵。与静态诱饵系统不同，这些人工智慧驱动的系统能够分析网路流量以产生真实痕迹，从而有效地自动创建高精度陷阱，无需人工调整。这种向自主防御的转变源于保护日益增长的数位足迹的需求。根据思科2024年11月发布的《人工智慧就绪指数》，网路安全是人工智慧应用的首要任务，42%的组织正在实施高阶安全整合以支援这些工作。这一趋势降低了配置负担，使企业能够维护一个可动态扩展的弹性欺骗基础架构。

同时，对于那些面临保护传统基础设施挑战且无法安装传统代理商的组织而言，将欺骗能力扩展到物联网 (IoT) 和营运技术 (OT) 环境至关重要。供应商正在开发无需代理的专用模拟技术，这些技术能够模拟工业系统，从而检测这些不透明网路中的横向移动。整合环境的脆弱性凸显了此类专用检测的迫切性。根据 Fortinet 于 2024 年 6 月发布的《2024 年营运技术和网路安全状况报告》，约 73% 的组织报告称其 OT 系统遭受了入侵。这一趋势反映出一种策略转变，即利用欺骗技术作为关键基础设施的主要补偿控製手段。

目录

第一章概述

第二章：调查方法

第三章执行摘要

第四章：客户心声

第五章：全球欺骗技术市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 部署方式（云端/本地）
  • 按组件（服务、解决方案）
  • 透过欺骗堆迭（应用安全、资料安全、网路安全、终端安全）
  • 依最终用户（製造业、医疗保健业、银行、金融服务业、运输业、政府部门、零售业、其他行业）划分
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美欺骗技术市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国别分析
  • 我们
  • 加拿大
  • 墨西哥

第七章：欧洲欺骗技术市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国别分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太地区欺骗科技市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国别分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲欺骗科技市场展望

• 市场规模及预测
• 市占率及预测
• 中东与非洲：国别分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美洲欺骗性技术市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国别分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 促进因素
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 近期趋势

第十三章：全球欺骗技术市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的潜力
• 供应商的议价能力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• SentinelOne, Inc.
• Akamai Technologies, Inc.
• Cybereason Inc.
• Ricksoft, Inc.
• Huntress Labs Incorporated
• Fortra LLC
• Acalvio, Inc.
• Microsoft Corporation
• Sumo Logic, Inc.

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Deception Technology Market is projected to expand significantly, growing from USD 4.83 Billion in 2025 to USD 11.31 Billion by 2031, reflecting a CAGR of 15.24%. This market focuses on the implementation of proactive defense measures, including decoys, lures, and honeytokens, which are engineered to simulate legitimate assets and mislead attackers who have breached the network perimeter. The industry is primarily driven by the increasing incidence of Advanced Persistent Threats (APTs) and the critical operational need to shorten dwell times by detecting lateral movement early in the attack lifecycle. Additionally, the inadequacy of traditional prevention-first strategies in stopping sophisticated breaches has heightened the demand for these high-fidelity detection solutions, which are valued for generating fewer false-positive alerts compared to standard anomaly detection systems.

Despite the clear security advantages, the market encounters substantial obstacles regarding operational complexity and a shortage of specialized talent needed to manage these environments effectively. Successfully deploying deception strategies often requires a level of maturity that many organizations lack due to a widening workforce gap. According to ISACA, in 2024, 45% of global cybersecurity professionals identified insufficiently trained staff as a major challenge, a deficiency that directly impedes the ability of organizations to adopt and sustain sophisticated active defense layers like deception technology.

Market Driver

The rising frequency and sophistication of Advanced Persistent Threats (APTs) and ransomware campaigns act as the main catalysts for the Global Deception Technology Market. As adversaries increasingly evade perimeter defenses, organizations are compelled to adopt active defense layers to identify threats that have bypassed prevention mechanisms. Deception technology addresses this by planting realistic decoys to confuse attackers and divert them from critical assets. The financial impact of these incidents drives this investment urgency; according to Sophos, April 2024, in the 'State of Ransomware 2024' report, the average ransom payment surged by 500% to reach $2 million. This rising cost profile necessitates detection methods that can disrupt attack chains before significant damage occurs.

Consequently, the imperative for proactive early threat detection and reduced attacker dwell time has become a critical market driver. Reactive security measures often fail to detect lateral movement swiftly, allowing adversaries to establish persistence and exfiltrate data. Deception platforms mitigate this by instantly alerting security teams when a decoy is engaged, drastically shrinking the time-to-detect. This speed is vital as attack lifecycles accelerate; according to CrowdStrike, February 2024, in the '2024 Global Threat Report', the average eCrime breakout time dropped to just 62 minutes. To counter such rapid advancements, enterprises are integrating deception tools to force adversaries into revealing their presence early. Furthermore, the economic burden of security failures underscores this shift, as IBM reported in 2024 that the global average cost of a data breach climbed to $4.88 million, incentivizing the adoption of technologies that minimize breach impact.

Market Challenge

The operational complexity associated with deploying deception environments, combined with a severe scarcity of specialized talent, constitutes a primary barrier to the expansion of the Global Deception Technology Market. While deception technology offers high-fidelity detection, managing these active defense layers requires a sophisticated skillset to configure realistic lures and interpret adversary interactions accurately. Organizations often hesitate to adopt these solutions because they lack the internal expertise to maintain them without becoming overwhelmed by the operational overhead. This skill deficit forces security teams to prioritize foundational controls over advanced detection strategies, thereby lengthening sales cycles and limiting market penetration.

This workforce deficiency fundamentally restricts the addressable market for deception platforms. According to ISC2, in 2024, the global cybersecurity workforce gap reached a record 4.8 million professionals, highlighting the acute shortage of available personnel to manage complex security architectures. When security operations centers are already understaffed, the introduction of an additional layer requiring constant tuning and expert analysis becomes difficult to justify. Consequently, the inability to hire and retain qualified analysts directly correlates with slower adoption rates for deception technology, as organizations struggle to allocate sufficient human resources to operationalize these advanced tools effectively.

Market Trends

The Integration of AI for Dynamic and Autonomous Decoy Generation is reshaping the market by enabling platforms to deploy self-learning lures that adapt to adversary behavior. Unlike static honeypots, these AI-driven systems analyze network traffic to generate authentic breadcrumbs, effectively automating the creation of high-fidelity traps without manual tuning. This shift towards autonomous defense is driven by the necessity to secure expanding digital footprints; according to Cisco, November 2024, in the '2024 AI Readiness Index', cybersecurity is the top priority for AI deployment, with 42% of organizations having achieved advanced security integration to support these initiatives. By reducing configuration burdens, this trend allows enterprises to maintain resilient deception fabrics that scale dynamically.

Simultaneously, the Expansion of Deception Capabilities into IoT and OT Environments is critical as organizations struggle to protect legacy infrastructure where traditional agents cannot be installed. Vendors are engineering specialized, agentless emulation techniques that mimic industrial systems to detect lateral movement within these opaque networks. The urgency for such specialized detection is highlighted by the vulnerability of converged environments; according to Fortinet, June 2024, in the '2024 State of Operational Technology and Cybersecurity Report', nearly 73% of organizations reported experiencing an intrusion that impacted their OT systems. This trend reflects a strategic pivot towards using deception as a primary compensatory control in critical infrastructure.

Key Market Players

• SentinelOne, Inc.
• Akamai Technologies, Inc.
• Cybereason Inc.
• Ricksoft, Inc.
• Huntress Labs Incorporated
• Fortra LLC
• Acalvio, Inc.
• Microsoft Corporation
• Sumo Logic, Inc.

Report Scope

In this report, the Global Deception Technology Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Deception Technology Market, By Deployment

• Cloud
• On-Premises

Deception Technology Market, By Component

• Service
• Solution

Deception Technology Market, By Deception Stack

• Application Security
• Data Security
• Network Security
• Endpoint Security

Deception Technology Market, By End-User

• Manufacturing
• Healthcare
• BFSI
• Transportation
• Government
• Retail
• Others

Deception Technology Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Deception Technology Market.

Available Customizations:

Global Deception Technology Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Deception Technology Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Deployment (Cloud, On-Premises)
  • 5.2.2. By Component (Service, Solution)
  • 5.2.3. By Deception Stack (Application Security, Data Security, Network Security, Endpoint Security)
  • 5.2.4. By End-User (Manufacturing, Healthcare, BFSI, Transportation, Government, Retail, Others)
  • 5.2.5. By Region
  • 5.2.6. By Company (2025)
• 5.3. Market Map

6. North America Deception Technology Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Deployment
  • 6.2.2. By Component
  • 6.2.3. By Deception Stack
  • 6.2.4. By End-User
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Deception Technology Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Deployment
      • 6.3.1.2.2. By Component
      • 6.3.1.2.3. By Deception Stack
      • 6.3.1.2.4. By End-User
  • 6.3.2. Canada Deception Technology Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Deployment
      • 6.3.2.2.2. By Component
      • 6.3.2.2.3. By Deception Stack
      • 6.3.2.2.4. By End-User
  • 6.3.3. Mexico Deception Technology Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Deployment
      • 6.3.3.2.2. By Component
      • 6.3.3.2.3. By Deception Stack
      • 6.3.3.2.4. By End-User

7. Europe Deception Technology Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Deployment
  • 7.2.2. By Component
  • 7.2.3. By Deception Stack
  • 7.2.4. By End-User
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Deception Technology Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Deployment
      • 7.3.1.2.2. By Component
      • 7.3.1.2.3. By Deception Stack
      • 7.3.1.2.4. By End-User
  • 7.3.2. France Deception Technology Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Deployment
      • 7.3.2.2.2. By Component
      • 7.3.2.2.3. By Deception Stack
      • 7.3.2.2.4. By End-User
  • 7.3.3. United Kingdom Deception Technology Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Deployment
      • 7.3.3.2.2. By Component
      • 7.3.3.2.3. By Deception Stack
      • 7.3.3.2.4. By End-User
  • 7.3.4. Italy Deception Technology Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Deployment
      • 7.3.4.2.2. By Component
      • 7.3.4.2.3. By Deception Stack
      • 7.3.4.2.4. By End-User
  • 7.3.5. Spain Deception Technology Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Deployment
      • 7.3.5.2.2. By Component
      • 7.3.5.2.3. By Deception Stack
      • 7.3.5.2.4. By End-User

8. Asia Pacific Deception Technology Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Deployment
  • 8.2.2. By Component
  • 8.2.3. By Deception Stack
  • 8.2.4. By End-User
  • 8.2.5. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Deception Technology Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Deployment
      • 8.3.1.2.2. By Component
      • 8.3.1.2.3. By Deception Stack
      • 8.3.1.2.4. By End-User
  • 8.3.2. India Deception Technology Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Deployment
      • 8.3.2.2.2. By Component
      • 8.3.2.2.3. By Deception Stack
      • 8.3.2.2.4. By End-User
  • 8.3.3. Japan Deception Technology Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Deployment
      • 8.3.3.2.2. By Component
      • 8.3.3.2.3. By Deception Stack
      • 8.3.3.2.4. By End-User
  • 8.3.4. South Korea Deception Technology Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Deployment
      • 8.3.4.2.2. By Component
      • 8.3.4.2.3. By Deception Stack
      • 8.3.4.2.4. By End-User
  • 8.3.5. Australia Deception Technology Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Deployment
      • 8.3.5.2.2. By Component
      • 8.3.5.2.3. By Deception Stack
      • 8.3.5.2.4. By End-User

9. Middle East & Africa Deception Technology Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Deployment
  • 9.2.2. By Component
  • 9.2.3. By Deception Stack
  • 9.2.4. By End-User
  • 9.2.5. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Deception Technology Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Deployment
      • 9.3.1.2.2. By Component
      • 9.3.1.2.3. By Deception Stack
      • 9.3.1.2.4. By End-User
  • 9.3.2. UAE Deception Technology Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Deployment
      • 9.3.2.2.2. By Component
      • 9.3.2.2.3. By Deception Stack
      • 9.3.2.2.4. By End-User
  • 9.3.3. South Africa Deception Technology Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Deployment
      • 9.3.3.2.2. By Component
      • 9.3.3.2.3. By Deception Stack
      • 9.3.3.2.4. By End-User

10. South America Deception Technology Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Deployment
  • 10.2.2. By Component
  • 10.2.3. By Deception Stack
  • 10.2.4. By End-User
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Deception Technology Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Deployment
      • 10.3.1.2.2. By Component
      • 10.3.1.2.3. By Deception Stack
      • 10.3.1.2.4. By End-User
  • 10.3.2. Colombia Deception Technology Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Deployment
      • 10.3.2.2.2. By Component
      • 10.3.2.2.3. By Deception Stack
      • 10.3.2.2.4. By End-User
  • 10.3.3. Argentina Deception Technology Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Deployment
      • 10.3.3.2.2. By Component
      • 10.3.3.2.3. By Deception Stack
      • 10.3.3.2.4. By End-User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Deception Technology Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. SentinelOne, Inc.
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Akamai Technologies, Inc.
• 15.3. Cybereason Inc.
• 15.4. Ricksoft, Inc.
• 15.5. Huntress Labs Incorporated
• 15.6. Fortra LLC
• 15.7. Acalvio, Inc.
• 15.8. Microsoft Corporation
• 15.9. Sumo Logic, Inc.

16. Strategic Recommendations

17. About Us & Disclaimer