Artificial Intelligence (AI)-based Security Industry Guide

Artificial Intelligence (AI)-based Security Industry Guide

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

RELEASE DATE
20-Sep-2019
REGION
Asia Pacific
Research Code: PA74-01-00-00-00
SKU: IT03931-GL-MR_23571

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Description

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

Key Findings

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 Service Providers

AI and Edge Computing Which Increase Cybersecurity Risks

Increasing Human-machine Coordination

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)

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)

The Final Word

Legal Disclaimer

List of Exhibits

The Frost & Sullivan Story

Value Proposition—Future of Your Company & Career

Global Perspective

Industry Convergence

360º Research Perspective

Implementation Excellence

Our Blue Ocean Strategy

Related Research
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.--BEGIN PROMO--

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?
More Information
No Index No
Podcast No
Author Vu Anh Tien
Industries Information Technology
WIP Number PA74-01-00-00-00
Is Prebook No
GPS Codes 9532-C1,9702-C1,9705-C1,9659,9887-C1