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AIセキュリティ産業ガイド

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

発行 Frost & Sullivan 商品コード 913133
出版日 ページ情報 英文 62 Pages
納期: 即日から翌営業日
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本日の銀行送金レート: 1USD=109.93円で換算しております。
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AIセキュリティ産業ガイド Artificial Intelligence (AI)-based Security Industry Guide, 2018
出版日: 2019年09月20日 ページ情報: 英文 62 Pages
概要

当レポートでは、世界のAIセキュリティ産業を調査し、AI・マシンラーニング・ディープラーニングの概要、セキュリティ上の課題、サイバーセキュリティにおけるAIおよびMLの役割、AIおよびMLの導入事例・利用事例、主要ソリューションのプロセスなどをまとめています。

エグゼクティブサマリー

  • 主要調査結果

概要

  • AI
  • マシンラーニングとディープラーニング
  • AI・マシンラーニング・ディープラーニング
  • ディープラーニング:2部構成のプロセス
  • 多様なタスクを実行するディープラーニングアルゴリズム
  • AIアプリケーションをサポートする充実のリソース

AI導入動向

  • AIサービスプロバイダー
  • サイバーセキュリティリスクを増すAI・エッジコンピューティング
  • 人とマシンのコーディネーションの拡大

サイバーセキュリティにおけるAI

  • よりスマートで包括的なセキュリティの枠組みに対するニーズ
  • セキュリティ運用の主要4課題
  • セキュリティ運用のその他の課題
  • AIによるセキュリティへのニーズ
  • サイバーセキュリティ戦略におけるAI
  • AI導入事例:AI対応セキュリティ戦略
  • サイバーセキュリティにおけるAI:利用事例

AIセキュリティソリューションのプロファイル

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

総論

  • 総論
  • 免責事項

付録

FROST & SULLIVANについて

目次
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
  • Global Perspective
  • Industry Convergence
  • 360º Research Perspective
  • Implementation Excellence
  • Our Blue Ocean Strategy
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