OT Security Experts Warn: Your Team Isn’t the Problem. Your Architecture Is.
Live webinar reveals why manual rule creation is leaving industrial organizations exposed and how auto-learning protection is changing the equation.
Manual rule creation is the silent gap in most OT security programs. Detection tools are working, but writing enforcement policies by hand for constantly changing systems means the response always arrives late.
On April 21, 2026, TXOne Networks will host a live webinar titled, “Stop Writing Rules Manually: Auto-learning Protection Built for OT”, bringing OT security experts together to address why enforcement lags even when detection succeeds.
Part Two of a Three-Part Series
This webinar is the second installment of From Visibility to Protection, a three-part series guiding OT security leaders through the Discover → Assess → Protect framework. Building on Part 1’s examination of the 45-minute coordination gap, Part 2 explores how auto-learning protection can eliminate manual enforcement delays without sacrificing operator control or disrupting production.
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The Architecture Problem Detection Can’t Solve
According to the Honeywell 2025 Cyber Threat Report, only 2% of OT security alerts become actionable incidents. The rest create noise that trains teams to slow down before acting. Meanwhile, Splunk’s SURGe research found LockBit could encrypt nearly 100,000 files in as little as four minutes, long before most response workflows can complete.
Most OT security programs were built around detection, with enforcement handled separately by different tools and teams. Policy creation remains largely manual, with rules written from scratch or adapted from IT environments that don’t reflect how OT systems actually operate.
“Detection does its job. But its job ends at the alert,” said Quentin Kantaris, Principal Solutions Engineer at TXOne Networks. “Everything after, the tickets, the coordination, the firewall changes, that’s where the clock runs out. The shift we’re seeing is teams asking not how to generate better alerts, but how to move from an observed threat to an enforced response without that delay.”
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What the Webinar Will Cover
The session will introduce auto-rule learning – deriving security policies from actual OT network behavior rather than manual configuration. Kantaris will walk attendees through how policies are proposed, reviewed, and approved before enforcement begins, maintaining full operator control while compressing response time. A live demonstration will cover the full process from passive observation through inline enforcement.
Attendees will leave understanding:
- Why alert volume is a poor measure of OT security effectiveness
- What auto-rule learning looks like when purpose-built for OT environments
- How to evaluate whether current enforcement architecture can actually stop threats in time
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