Artificial intelligence is fundamentally transforming operational technology (OT) security by shifting the focus from reactive alerts to actionable insights that strengthen industrial resilience and efficiency.
OT environments—such as those in manufacturing, energy, and utilities—were historically designed for reliability, not security. As they become interconnected with IT networks, they face a surge of cyber vulnerabilities and overwhelming alert volumes. Analysts often struggle to distinguish critical threats from noise, leading to alert fatigue and delayed responses.
AI’s role in contextual intelligence
The adoption of AI is helping bridge this gap. According to Radiflow’s CEO Ilan Barda, the key lies in teaching AI to understand industrial context—assessing the relevance and priority of alerts within specific environments.
Radiflow’s new Radiflow360 platform, launched at the IT-SA Expo, integrates AI-powered asset discovery, risk assessment, and anomaly detection. By correlating local operational data with public threat intelligence, it enables focused incident management while cutting alert overload dramatically—improving resource efficiency by up to tenfold.
While AI enhances responsiveness, experts warn against overreliance. Barda highlights that AI “hallucinations” or inaccuracies from incomplete data still require human validation.
Fujitsu’s product manager Hill reinforces this, noting that many organizations remain cautious about automation due to IT-OT communication gaps. Despite progress, widespread adoption of AI in OT security remains uneven; some firms use predictive tools, while others still react post-incident.
Double-edged nature of AI
AI’s dual nature poses both promise and peril. It boosts defenses through faster detection and automation but also enables adversaries to launch more precise attacks. Incomplete asset inventories further limit visibility—without knowing what devices exist, even the most advanced AI models operate with partial awareness. Experts agree that comprehensive visibility is foundational to AI success in OT.
Ultimately, the real evolution is philosophical: from detecting every alert to discerning what truly matters. AI is bridging the IT-OT divide, enabling analysts to interpret complex industrial signals and focus on risk-based priorities. The goal is not to replace human expertise but to amplify it—creating security ecosystems that are scalable, sustainable, and increasingly proactive.
