How AI Is Rewiring OT Security From Alerts To Answers
The increasing complexity of operational technology (OT) environments, which encompass industrial control systems vital for factories, grids, and pipelines, is leading to a significant challenge: alert fatigue within security teams. Regulatory frameworks, such as the EU’s NIS2 directive and CISA guidance in the U.S., mandate stricter cyber-risk management and incident reporting. This has led organizations to deploy more sensors and systems, inadvertently generating an overwhelming volume of security alerts that often lack context and clear priority, stretching security teams thin.
Ilan Barda, CEO of Radiflow, highlights that mid-sized organizations, in particular, frequently lack the necessary preparations to harden their devices and networks, resulting in a deluge of alerts that less-qualified teams struggle to handle effectively. This creates a paradox where increased visibility does not translate into better clarity, as defenders spend considerable time on low-priority notifications, potentially missing critical threats.
Artificial intelligence offers a solution by improving the relevancy and prioritization of these alerts. AI tools can be trained on a facility's unique operational processes and correlated with external threat intelligence to discern which anomalies represent genuine risks. Radiflow's new Radiflow360 platform exemplifies this approach, integrating asset discovery, risk analysis, and anomaly detection with an AI analyst assistant. This shift aims to make OT security more scalable and sustainable by transforming raw alerts into actionable insights.
However, AI is a double-edged sword. While it enhances threat detection, automates responses, and reduces downtime, it also empowers cyber attackers with advanced tools for more targeted and damaging assaults. Martin Hill, a cybersecurity product manager at Fujitsu, points out that many legacy OT systems were not built with internet connectivity in mind, making them inherently vulnerable when integrated into IT networks. He notes a transition from reactive to proactive security strategies, involving network segmentation, 24/7 monitoring, and compliance with standards like NIS2 and IEC 62443. Yet, a fundamental challenge remains: the cultural and linguistic divide between IT and OT teams, with IT focusing on data and OT prioritizing uptime and safety.
Adoption of AI in OT security is progressing but is uneven. Some organizations actively embrace AI to stay ahead of threats, while others only implement advanced solutions after experiencing an incident. A critical barrier to effective AI implementation is the lack of a comprehensive OT asset inventory across most sectors, as highlighted by Tatyana Bolton of the Operational Technology Cyber Coalition. Without a clear baseline of assets, even the most sophisticated AI algorithms can only make educated guesses. Therefore, human oversight remains essential for validating AI findings and ensuring their accuracy. The long-term goal is for AI to bridge the IT/OT gap, helping IT professionals understand OT threats and improve network security, but human expertise will continue to be the ultimate arbiter in maintaining resilience.
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