{"id":6515,"date":"2025-09-26T15:52:21","date_gmt":"2025-09-26T19:52:21","guid":{"rendered":"https:\/\/solutionsreview.com\/endpoint-security\/?p=6515"},"modified":"2025-09-26T15:52:41","modified_gmt":"2025-09-26T19:52:41","slug":"ai-as-a-double-edged-sword-for-ot-ics-cybersecurity","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/endpoint-security\/ai-as-a-double-edged-sword-for-ot-ics-cybersecurity\/","title":{"rendered":"AI As a Double-Edged Sword for OT\/ICS Cybersecurity"},"content":{"rendered":"

\"AI<\/p>\n

Vicky Bruce, Global Capability Manager of Cybersecurity Services at <\/strong>Rockwell Automation<\/a>, explains why AI can be a double-edged sword for OT\/ICS cybersecurity. <\/strong><\/em>This article originally appeared in Insight Jam<\/a>, an enterprise IT community that enables human conversation on AI.<\/span><\/strong><\/em><\/p>\n

\"\"<\/a>Artificial intelligence (AI) is quickly transforming how industrial organizations think about cybersecurity. On one hand, it helps security teams spot threats earlier, automate responses, and reduce downtime. On the other hand, it gives cyber attackers tools to launch more targeted, convincing, and damaging attacks\u2014often in seconds.<\/p>\n

Cybersecurity threats are evolving<\/a> as fast as the technologies meant to stop them. For cybersecurity teams tasked with protecting operational technology (OT) and industrial control systems (ICS), this is both a leap forward and a growing risk. In the field, the same AI model that helps prevent downtime one day can trigger a false positive \u2014or worse, be manipulated\u2014on another. Security teams face the challenge of tapping into AI’s potential without introducing new vulnerabilities.<\/p>\n

The Expanding Cyber Risk Landscape\u00a0<\/strong><\/h3>\n

Industrial networks today look nothing like they did a decade ago. What were isolated, largely air-gapped, industrial networks are now interconnected ecosystems where OT and information technology (IT) converge. Meanwhile, cyber threats<\/a> are growing in scale and complexity, and the convergence of IT and OT is increasing the attack surface. According to the SANS 2024 ICS\/OT Cybersecurity Report, the cybersecurity risks in OT are growing, with 19 percent of organizations reporting one or more security incidents in just a year.<\/p>\n

AI is accelerating progress on both sides of the cybersecurity equation. A recent survey of manufacturing leaders found that\u00a049 percent plan to use AI and machine learning (ML) for cybersecurity<\/a> in the next 12 months. But the same tools are also being used by threat actors to automate intrusions and evade detection. The challenge is to harness AI’s potential while keeping it from being weaponized against the systems it was designed to protect.<\/p>\n

New Frontiers in Protecting Operational Technology<\/strong><\/h4>\n

AI’s strength lies in its ability to process and act on vast amounts of data. When applied to industrial environments, it can recognize subtle changes before they become major disruptions or threats. Here’s where it\u2019s making a difference:<\/p>\n

Smarter Anomaly Detection \u00a0<\/strong><\/p>\n

Traditional threat detection tools look for known signatures, but many of today’s most damaging threats don’t come with a fingerprint. AI-driven threat detection systems can flag subtle behavioral anomalies, such as a robotic arm cycling 0.4 seconds too fast or a PLC issuing a command slightly out of sequence. Even an unusual pattern in equipment startup time can signal misconfiguration caused by a compromised vendor laptop.<\/p>\n

Predictive Maintenance as a Security Layer \u00a0<\/strong><\/p>\n

AI-powered predictive maintenance can serve as an added layer of a strong cybersecurity strategy. A piece of equipment acting “off schedule” could be more than just wear and tear. It might be a symptom of malware or unauthorized configuration changes. Continuously monitoring maintenance data to flag irregularities can help teams identify potential failures before they happen.<\/p>\n

AI-Assisted Network Segmentation \u00a0<\/strong><\/p>\n

When a breach happens, the difference between a minor incident and a catastrophic shutdown comes down to speed. Seconds can determine whether a threat jumps to another cell or stays isolated. In a food and beverage plant, this could mean stopping a ransomware attack before it locks down a batching system. Instead of waiting for IT teams to intervene manually, AI confirms threats are contained in real-time.<\/p>\n

When Cyber Defenses Become a Target<\/strong><\/h3>\n

Of course, the same technology deployed to defend operations can be weaponized. Attackers are using AI to design malware that adapts, evades, and even changes itself, rendering traditional security technology relying on fixed threat databases increasingly less useful. At the same time, AI-generated deepfakes make phishing attempts more realistic than ever. Take a manager on the plant floor receiving a voicemail from their “CEO” authorizing a key system modification, then later learning that it was entirely AI-generated.<\/p>\n

Attackers are also testing how far they can manipulate AI systems directly. By feeding adversarial data into detection models, they can suppress alerts or train systems to ignore certain behaviors. Without proper validation, a security model might learn the wrong lessons from the wrong data.<\/p>\n

Recent high-profile ransomware incidents reinforce how quickly tactics are evolving. For example, a\u00a0ransomware attack<\/a>\u00a0disrupted operations for thousands of U.S. car dealerships and led to a reported $25 million ransom payment. This example demonstrates how threat actors are employing more advanced tactics to cripple businesses and shut down entire industries. These are no longer isolated events, but industry-shaping moments.<\/p>\n

Best Practices for AI in OT Cybersecurity\u00a0<\/strong><\/h3>\n

To safely deploy AI in critical infrastructure, organizations need more than just good intentions. They need good governance. This includes:<\/p>\n