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市場調查報告書

AI安全產業指南

Artificial Intelligence (AI)-based Security Industry Guide, 2018

出版商 Frost & Sullivan 商品編碼 913133
出版日期 內容資訊 英文 62 Pages
商品交期: 最快1-2個工作天內
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AI安全產業指南 Artificial Intelligence (AI)-based Security Industry Guide, 2018
出版日期: 2019年09月20日內容資訊: 英文 62 Pages
簡介

本報告提供全球AI安全產業,AI、機器學習、深度學習概要調查,安全上的課題,網路安全的AI及ML所扮演的角色,AI及ML的引進案例、利用案例,主要解決方案的流程等資料彙整。

摘要整理

  • 主要調查結果

概要

  • AI
  • 機器學習和深度學習
  • AI、機器學習、深度學習
  • 深度學習:2部分組成的流程
  • 實行多樣事務的深度學習演算法
  • 充實的資源支援AI應用

AI引進趨勢

  • AI服務供應商
  • 增加網路安全風險的AI、邊緣運算
  • 人和機器的協調擴大

網路安全的AI

  • 對更智慧且全面的安全架構的需求
  • 保全行動主要4課題
  • 保全行動其他的課題
  • AI對安全的需求
  • 網路安全策略的AI
  • AI引進案例:AI支援安全策略
  • 網路安全的AI:利用案例

AI安全解決方案的簡介

  • 市場環境
  • Balbix
  • CrowdStrike
  • Darktrace
  • DBAPPSecurity
  • eSentire
  • Paladion
  • ReaQta
  • Seceon
  • Shape Security

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  • 免責聲明

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關於FROST & SULLIVAN

目錄
Product Code: PA74-74

The Need for Ai-enhanced and Automated Security Solutions for Better Threat Prevention, Detection and Response

Artificial intelligence (AI) and machine learning (ML) have been adopted widely across industries over the years due to the multifaceted benefits that the technologies bring about.

AI and ML have been also increasingly adopted across industries, from such as healthcare, education, information and communication technologies (ICT), logistics, maritime, aviation, aerospace and defence, entertainment and gaming.

Particularly, AI and ML have been used widely in cybersecurity industries, by both hacking and security communities, making the security landscape even more sophisticated. Many organizations, regardless of size, are now facing greater challenges in day-to-day security operations. Many of them indicate that the cost of threat management, particularly threat detection and response, is too high. Meanwhile, AI-driven attacks have increased in number and frequency, requiring security professionals to have more advanced, smart and automated technologies to combat these automated attacks.

The complex challenges in security operation nowadays suggest the need for a smarter, adaptable, scalable, automated and predictive security strategy in order to deal with the constantly evolving threats more effectively. AI and ML have been increasingly developed by security companies to strengthen their competitiveness. Most of them are now in the midst of developing their own AI/ML algorithm to empower their security products, either in some products or all of the product lines. AI and ML have been used in all stages of cybersecurity to enable a smarter, more proactive, and automated approach to cyber defense, from threat prevention protection, threat detection/threat hunting, or threat response, to predictive security strategy.

Security startup companies are the most proactive in introducing AI-security technologies to the market. However, large traditional security companies have also beefed up their strategies to stay abreast of the trend of integrating AI/ML into their existing security solutions.

There are hundreds of companies now in the market, with different capabilities and focus areas, from application-centric protection, or AEDR, to security analytics platform. In this report, we profile AI-driven companies and AI-centric cybersecurity companies.

This research is delivered by Frost & Sullivan cybersecurity research and practice team.

Key Issues Addressed:

  • What are the needs to adopt a smarter and holistic security framework?
  • What key role are AI and ML expected to play in cybersecurity?
  • How are AI/ ML adopted in cybersecurity?
  • What are the use cases for AI/ML in cybersecurity?
  • What are the key features and differentiators of AI -driven security solutions in the market?

Table of Contents

Executive Summary

  • Key Findings

Overview

  • Artificial Intelligence
  • Machine Learning and Deep Learning
  • AI, Machine Learning, and Deep Learning
  • Deep Learning, a 2-part Process
  • Deep Learning Algorithms that Execute Diverse Tasks
  • Diligent Resources to Support AI Applications

AI Adoption Trends

  • AI Service Providers
  • AI and Edge Computing Which Increase Cybersecurity Risks
  • Increasing Human-machine Coordination

Artificial Intelligence in Cybersecurity

  • The Need for a Smarter & Holistic Security Framework
  • The Top 4 Challenges to Security Operations
  • The Top 4 Challenges to Security Operations (continued)
  • Other Challenges to Security Operations
  • The Need for AI-powered Security
  • AI in Cybersecurity Strategy
  • Use Cases of AI Adoption-AI-enabled Security Strategy
  • Use Cases for AI in Cybersecurity
  • Use Cases for AI in Cybersecurity (continued)

AI-based Security Solution Profiles

  • The Market Landscape
  • Balbix
  • Balbix (continued)
  • CrowdStrike
  • CrowdStrike (continued)
  • CrowdStrike (continued)
  • Darktrace
  • Darktrace (continued)
  • DBAPPSecurity
  • DBAPPSecurity (continued)
  • DBAPPSecurity (continued)
  • eSentire
  • eSentire (continued)
  • Paladion
  • Paladion (continued)
  • ReaQta
  • ReaQta (continued)
  • Seceon
  • Seceon (continued)
  • Shape Security
  • Shape Security (continued)

Conclusion

  • The Final Word
  • Legal Disclaimer

Appendix

  • List of Exhibits

The Frost & Sullivan StoryThe Journey to Visionary Innovation

  • The Frost & Sullivan Story
  • Value Proposition-Future of Your Company & Career
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  • Industry Convergence
  • 360º Research Perspective
  • Implementation Excellence
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