This is all because AI-based security is looking for malicious behaviors rather than known malicious code. We see little possibility in which signature-based security solutions will work with IoT in an edge computing environment for a variety of reasons including the limitation on the throughput of communications between distributed endpoints and centralized cloud.ĪI has various advantages including the fact that it is a more lightweight application (because it does not require all the data that comes with tracking digital signatures/code for known viruses), more effective in identifying malware, easier and less costly to maintain as there is no need to constantly identify new malware code. There are varying opinions about security in IoT.įor example, some companies favor a distributed(decentralized) approach whereas other companies believe a more centralized approach leveraging strictly centralized cloud architecture makes more sense. For example, AI may be used in IoT to bolster security, safeguard assets, and reduce fraud. There are many potential use cases for AI within the cybersecurity domain. Longer-term, the publisher sees many solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics. The AI segment is currently very fragmented, characterized by most companies focusing on silo approaches to solutions. We see AI increasingly embedded within many systems and applications including everything from data management to retail shopping. Key AI technology systems integration opportunities include Expert Systems, Decision Support Systems, Fuzzy Systems, and Multi-Agent SystemsĪrtificial Intelligence (AI) represents a wide variety of technologies including Machine Learning, Deep Learning, Natural language processing, and more. The combination of AI and IoT (AIoT) will drive up to 27% of new AI systems integration, primarily involving IIoTĪI solutions in a public cloud environment shall be almost three times those of private cloud deployments through 2028 Global unsupervised machine learning market will reach $15.6 billion by 2028, growing at 25.1% CAGR Total global AI solution market will reach $301.2 billion by 2028, growing at 29.4% CAGR The report analyzes the forecasts AI market sizing for by technology type, deployment method, solution type, network and technology integration, and by industry verticals from 2023 through 2028. The report assesses the state of AI development, implementation, and operation. This report evaluates the AI technology and solutions market, including an analysis of leading AI vendors, strategies, solutions and applications. DUBLIN-( BUSINESS WIRE)-The "AI Market by Technology Type, Deployment Method, Solution Type, Integration (Technologies, Networks, and Devices) and Industry Verticals 2023 - 2028" report has been added to 's offering.
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