Introduction
The rapid deployment of private 5G networks across various industries, especially in critical sectors such as energy [1], military [1] [4] [5], logistics [1] [4] [5], and healthcare [1], has heightened security concerns [5]. Organizations adopting AI security tools without adequate communications technology (CT) expertise risk exposing these networks to cyber threats [4]. This situation necessitates proactive attack surface management and a comprehensive understanding of technology and cyber risks to ensure effective security.
Description
A rapid increase in private 5G network deployments across various industries [5], particularly in critical sectors such as energy [1] [5], military [1] [4] [5], logistics [1] [4] [5], and healthcare [1], has raised significant security concerns [3]. As organizations adopt AI security tools without sufficient communications technology (CT) expertise [4], they may inadvertently expose these networks to cyber risks. This underscores the necessity for proactive attack surface management to prevent vulnerabilities. Effective security for private 5G environments requires a combination of AI-powered protection and a comprehensive understanding of technology and cyber risks [6] [7]. Rachel Jin [3], Chief Enterprise Platform Officer at Trend Micro [3], emphasizes this necessity, especially as private 5G networks gain traction.
A recent survey indicates that 100% of organizations are either utilizing private 5G or evaluating its deployment. Among these, 62% are currently using AI-powered security solutions, with an additional 35% planning to implement them [5]. Key AI capabilities deemed essential for protecting private 5G networks include predictive threat intelligence (58%) [5], continuous adaptive authentication (52%) [1] [2] [3] [4] [5] [6] [7], zero-trust enforcement (47%) [2] [3] [4] [5] [6] [7], and self-healing networks with AI automation (41%) [1] [2] [3] [4] [6] [7]. However, over 90% of organizations utilizing AI security face challenges in its deployment [2] [3], with significant obstacles such as high costs (47%), concerns over false positives and negatives (44%) [2] [3] [4] [5] [6] [7], and a lack of internal expertise (37%) [2] [3] [4] [5] [6] [7]. Alarmingly, only 20% of organizations have dedicated teams for securing their communications networks [3] [4] [6] [7], with responsibilities often falling to CTOs (43%) or CIOs (32%) [4] [6] [7].
As the use of mobile networks grows [4] [6] [7], specialized CT security capabilities are increasingly necessary [4] [6]. Organizations need comprehensive visibility and effective management of their attack surface risk [6]. Many may inadvertently expose themselves to cyber and compliance risks by not adequately safeguarding AI for traffic monitoring and analysis [2] [3] [4] [6] [7]. Approximately half or fewer respondents reported ensuring compliance with data privacy regulations like GDPR (54%) [3], encrypting data at rest and in transit (51%) [2] [3] [4], deploying strict access controls for AI models (50%) [2] [3] [4] [6] [7], and using data anonymization techniques (44%) [2] [3] [6] [7]. Proactive attack surface management is essential [2] [4], as any oversight can lead to vulnerabilities that compromise network security. Currently, less than 20% of security budgets are allocated to private 5G networks [4] [6] [7], despite the critical services they support and the sensitive data they handle. Organizations must ensure their Security Operations Centers (SOC) are equipped to monitor and safeguard this new technology [1], as cybersecurity vendors that implement proactive risk management and attack path prediction will be better positioned to secure private 5G and AI infrastructures [1].
Conclusion
The expansion of private 5G networks presents both opportunities and challenges. While these networks offer enhanced capabilities, they also introduce significant security risks, particularly when AI tools are deployed without sufficient expertise. To mitigate these risks, organizations must prioritize proactive attack surface management and allocate adequate resources to secure their networks. As the landscape evolves, the ability to predict and manage potential threats will be crucial in safeguarding critical infrastructure and sensitive data. Organizations that invest in comprehensive security strategies will be better equipped to navigate the complexities of private 5G and AI integration.
References
[1] https://www.organisator.ch/en/operational-excellence/2025-03-03/sicherheitsluecken-gefaehrden-private-5g-netze-inmitten-des-ki-booms/
[2] https://www.securityinfowatch.com/ai/press-release/55271972/trend-micro-security-gaps-imperil-private-5g-networks-amid-ai-boom
[3] https://newsroom.trendmicro.com/2025-03-03-Security-Gaps-Imperil-Private-5G-Networks-Amid-AI-Boom
[4] https://cioinfluence.com/security/security-gaps-imperil-private-5g-networks-amid-ai-boom/
[5] https://www.infosecurity-magazine.com/news/private-5g-networks-security-risks/
[6] https://www.prnewswire.com/news-releases/security-gaps-imperil-private-5g-networks-amid-ai-boom-302389324.html
[7] https://www.stocktitan.net/news/TMICY/security-gaps-imperil-private-5g-networks-amid-ai-tce7b3ta6u0f.html
