Most manufacturers have already run pilots. Fewer have built what comes after it.  An AI-powered manufacturing SOC proof of concept is designed to answer one question: can the model detect real threats in our environment?   It’s a narrow test – one production line, one asset class, a data set scoped

A manufacturing OT SOC that only detects threats is doing half the job. Detection tells you something happened. It doesn’t tell you whether that something is about to halt a production line, trip a safety system, or spoil a batch and in manufacturing, that distinction is the entire point.  Most

Manufacturing OT security needs detection that connects identity, engineering, network, and OT activity into an intelligible attack sequence, giving security and operations enough decision time to respond before their options narrow.   A manufacturing attack rarely announces itself as a production incident. An attacker can enter through compromised credentials, move

Manufacturing cannot secure OT effectively by extending an enterprise security architecture built around IT assumptions. The architecture must account for what production assets support, what depends on them, what a security action could disrupt, and who has authority to approve that action.  A manufacturing SOC can monitor an OT environment

At 2:13 a.m., an alert appears in the SOC.  A privileged account has authenticated from an unusual location.  The SOC investigates.  The endpoint looks clean. The identity is valid. The network connection is encrypted. Nothing appears obviously malicious.  The alert is closed.  Three hours later, a production line begins behaving

Your SOC is generating thousands of alerts daily, yet meaningful detections feel increasingly rare. Analysts toggle between double-digit consoles, piecing together fragmented context while attackers who are empowered by AI move in hours and not weeks. The boardroom questions grow sharper: “Why did this incident escalate?” “What is our true exposure?”  Meanwhile, compliance

Ravi Teja J leads the convergence of operational intelligence and enterprise cybersecurity, two disciplines that were once managed in silos, and are now inseparable in the age of AI. As Vice President – Ops Intelligence and Cybersecurity, he sits at the intersection where machine-generated data meets mission-critical decision-making, where automated

Artificial Intelligence (AI) is transforming business at unprecedented speed, but it is also reshaping the threat landscape faster than many organisations realise. What once required sophisticated criminal networks can now be executed by individuals using AI-powered tools capable of generating convincing voices, synthetic identities, hyper-personalised scams, and automated attacks at

Listen This Article Your utility AI pilot is likely to stall at scale because the pilot was designed to suppress the exact operational fragmentation that production inevitably restores, and most utilities never measure that gap.  Seventy‑four percent of utility AI pilots never reach production across more than one region. The ones that