AI adoption in OT environments creates dual challenges - protecting AI systems while defending against AI-powered threats. Deep Q network models replicate prompts across all AI engines, meaning detailed PLC configuration queries inadvertently reveal network architecture to untrusted third parties. Organizations non-maliciously expose their operational environments when practitioners query AI tools from unsegmented networks, feeding intelligence databases that threat actors exploit.

AI leverages the same linear regression and neural network foundations that have existed since the 1950s - computing power, not conceptual innovation, drives current capabilities.

The session will cover:

• How global firewall deployments create automated threat detection networks that verify and distribute protection updates;
• SOC automation strategies that address manufacturing cybersecurity skills gaps through AI-assisted threat correlation;
• Governance frameworks and guardrails essential for AI deployment in air-gapped and regulated OT environments.