WHAT WE HAVE ON THIS PAGE
Report Overview
According to Market.us, The global Edge AI for cybersecurity market is set for exponential growth, projected to expand from USD 30.6 billion in 2024 to approximately USD 643.2 billion by 2034, registering a CAGR of 35.60% over the forecast period. This surge is driven by the rising need for real-time threat detection, increasing adoption of AI-driven security solutions, and growing concerns over cyber threats targeting edge devices.
In 2024, North America accounted for over 36.5% of the global Edge AI for cybersecurity market, generating revenues of around USD 11.1 billion. The United States alone contributed USD 8.93 billion, supported by strong technological advancements, high investments in cybersecurity, and the presence of key industry players. With a CAGR of 33.5%, the U.S. market is expected to maintain its dominance, fueled by increasing AI integration in cybersecurity frameworks across government, financial, and enterprise sectors.
Source: Market.us
According to the Nasscom community, The shift towards remote work is accelerating, with 36 million U.S. employees expected to be working remotely by the end of 2025 – accounting for approximately 22% of the workforce. This represents an 87% increase from pre-pandemic levels, as organizations continue to adopt flexible work models. In this rapidly evolving digital landscape, Edge AI is becoming a critical tool for cybersecurity, addressing both security challenges and operational efficiency.
As cyber threats grow more sophisticated, businesses are recognizing that traditional security models are no longer sufficient. Edge AI is emerging as a game-changer, enabling real-time threat detection and response while reducing latency and reliance on centralized cloud infrastructures. Enterprises leveraging Edge AI for cybersecurity gain a dual advantage – enhanced security and improved efficiency in handling vast amounts of decentralized data.
In 2024, network security emerged as the largest segment, capturing over 37.4% of the market share. The increasing frequency of cyberattacks, particularly on enterprise networks and cloud-based systems, has driven investments in AI-driven intrusion detection, firewalls, and endpoint security solutions.
The hardware segment held a significant share, accounting for more than 35.6% of the total market. With the expansion of edge computing and AI-based security solutions, demand for AI chips, edge processors, and smart security devices has surged, contributing to this segment’s growth.
Machine learning (ML) technologies were at the core of AI-driven cybersecurity solutions, holding over 42.8% of the market share. ML algorithms enhance threat detection, predict security breaches, and automate responses, making them essential for fraud prevention, anomaly detection, and behavioral analysis in cybersecurity.
With over 30.7% of the market, the Fraud Detection and Anti-Fraud segment saw strong adoption in sectors like finance, e-commerce, and telecommunications. AI-powered fraud prevention solutions are increasingly used to identify suspicious transactions, detect identity theft, and prevent financial fraud.
The Banking, Financial Services, and Insurance (BFSI) sector accounted for more than 40.0% of the market share in 2024. The rise in digital banking, fintech expansion, and increasing online transactions have heightened the need for advanced AI-based security solutions to combat cyber fraud, ensure compliance, and protect customer data.
Key Growth Drivers
- Rising Cybersecurity Threats at the Edge – The growing adoption of IoT, cloud computing, and 5G networks has increased the number of connected devices, making traditional security measures insufficient. Edge AI enhances real-time threat detection and mitigation, reducing potential vulnerabilities.
- Regulatory Push for AI-Powered Security Solutions – Governments and regulatory bodies worldwide are pushing for stricter cybersecurity frameworks, encouraging organizations to adopt advanced AI-driven security mechanisms.
- Advancements in AI and Machine Learning – Continuous improvements in AI algorithms, deep learning models, and neural networks are enhancing the efficiency of edge-based security solutions, enabling faster decision-making and response times.
- Growth in Edge Computing – As businesses shift towards decentralized networks and edge computing, the need for AI-driven cybersecurity solutions is accelerating. Edge AI reduces latency and enhances data security by processing information closer to the source rather than relying solely on cloud-based systems.
Regional Insights
The United States accounted for a significant portion of North America’s growth, with the market reaching USD 8.93 billion in 2024. The country is expected to maintain a robust CAGR of 33.5%, fueled by:
- Stringent regulatory frameworks for data protection
- Growing number of cyber threats and attacks on critical infrastructure
- Advancements in AI and machine learning technologies for cybersecurity
North America remained the leading regional market, contributing over 36.5% of total revenue, amounting to approximately USD 11.1 billion. The region’s dominance is driven by:
- High cybersecurity investments by enterprises and government agencies
- Strong presence of AI and cybersecurity firms
- Widespread adoption of edge computing and IoT security solutions
You May Also Like To Read
Driver
Enhanced Security and Privacy
One of the primary drivers of Edge AI in cybersecurity is the enhancement of security and privacy. By processing data locally on the device rather than sending it to the cloud, Edge AI minimizes the exposure of sensitive information to potential intercepts and breaches. This local processing capability enables rapid, real-time decision-making and responses, crucial for maintaining robust cybersecurity defenses.
Restraint
Scalability Challenges
A significant restraint for Edge AI in cybersecurity is scalability. Edge AI systems must manage increasing amounts of data and interactions among a growing number of devices, which can strain computational resources and storage capacities. These scalability challenges are compounded by the need to maintain performance and reliability across distributed networks, making it difficult to implement Edge AI broadly without substantial infrastructural enhancements.
Opportunity
Predictive Analytics for Proactive Security
Edge AI presents a substantial opportunity in cybersecurity through predictive analytics. By utilizing AI algorithms, systems can proactively identify and respond to potential security threats before they materialize. This proactive approach not only enhances security but also optimizes resource use, focusing efforts where they are most needed and likely to be effective.
Challenge
Latency Issues
Edge AI systems often struggle with various forms of latency, from input to processing delays. These can hinder the effectiveness of cybersecurity measures, as timely threat detection and response are critical. Overcoming latency issues is essential for Edge AI systems to deliver their intended benefits, particularly in environments where split-second decisions are required to thwart cyber threats.
Future Outlook
The Edge AI for cybersecurity market is set for unprecedented growth, driven by technological advancements, increasing cyber risks, and enterprise digitalization. North America will remain a key player, while rapid adoption in Europe and Asia-Pacific is expected to boost global market expansion. As organizations continue to prioritize AI-powered security solutions, the market’s momentum will only strengthen in the years ahead.
Sources:
- https://www.weforum.org/stories/2023/06/cybersecurity-and-ai-challenges-opportunities/
- https://www.wevolver.com/article/2023-edge-ai-technology-report-chapter-ix-challenges-of-edge-ai
- https://hacknjill.com/cybersecurity/advanced-cybersecurity/ai-and-cybersecurity-2023/
ABOUT AUTHOR
Yogesh Shinde is a passionate writer, researcher, and content creator with a keen interest in technology, innovation and industry research. With a background in computer engineering and years of experience in the tech industry. He is committed to delivering accurate and well-researched articles that resonate with readers and provide valuable insights. When not writing, I enjoy reading and can often be found exploring new teaching methods and strategies.
