Lucian Fogoros (@fogoros) header
Lucian Fogoros (@fogoros) profile picture

Co-Founder https://xitter.gq/t.co/KwXjxubObe Advisory Board Cleveland State University Computer Eng. #IIoT #AI #ML #CxO #IndustrialTransformation #ICSCybersecurity #AR

United States
Joined January 2009
Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)Photo by Lucian Fogoros (@fogoros)
NERC CIP-007 requires documented assessment, testing evidence, and deployment timelines for bulk electric system changes. IEC 62443-2-3 requires authorization responsibilities for patches and configuration changes in industrial control systems. #opswat_ics Note: Video generated by AI.
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NIST SP 800-82 requires risk assessment, validation testing, and stakeholder coordination before deployment for ICS security changes. All three frameworks converge on the same requirement: documented human authorization before changes reach production in critical infrastructure.
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Corporate IT security platforms prioritize response speed. OT security teams face a different requirement because an automated remediation step during a production run affects physical processes, not just servers. Those physical processes cannot always be rolled back.
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One plant works. Scaling to hundreds breaks everything. Dean Sheehan from @InfluxDB, Nikhil Pal from Roche and other panelists will focus on the topic of escaping pilot purgatory with edge AI. Dec 8. Partner content with InfluxData. #IndustrialAISummit #influxdata_iiot
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Tag naming is among the first casualties when AI scales beyond one factory. The probability that sensors carry consistent identifiers across 20 sites is low. Partner content with @InfluxDB. #influxdata_iiot Note: Video generated by AI.
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Traditional historians designed for single-site visibility lack the architecture to feed real-time data across a factory network for AI inference and prediction, a gap that only becomes visible when a manufacturer attempts to replicate a working single-site model at enterprise scale.
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Manual fixes for edge cases become scaling liabilities when replicated across multiple sites, which is why data architecture must enforce consistency at the foundation rather than through ongoing manual correction.
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Merck treats AI agents the same way it treats human employees on documentation: both can produce different outputs when prompted again, but both must leave behind a defensible record at the time of the original decision. An AI that cannot do this fails the same standard a human employee would fail. Partner content with Adlib. #adlib_iiot
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The AI notebook concept captures prompt history, retrieval provenance, and model version at decision time. Without that snapshot, any explanation produced later is a new inference, not a record, and ALCOA's Contemporaneous requirement treats the two very differently.
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ALCOA's five criteria, Attributable, Legible, Contemporaneous, Original, and Accurate, were designed for human records. Applying them to AI-generated decisions requires agents to be architected differently from the ground up, not retrofitted with logging after deployment.
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At a plastics factory, connecting machines and having statistical process control running took just one week after deploying a Unified Namespace architecture. Partner content with @HighbyteInc. #highbyte_iiot Note: Video generated by AI.
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Implementation timelines vary based on each facility's existing processes, systems, and documentation. Much of the work involves understanding how equipment and processes operate and organizing that information so data is actually useful to applications.
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In 2021, the experience gap between teams just beginning to build an industrial data foundation and those leading the way could have been years. Today, as more organizations share what they learned, that gap has narrowed to months.
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AI could create extraordinary wealth. But telling a generation that abundance is coming while they can’t afford a home or start a family is how you lose their trust. I’m incredibly optimistic about what we can build. People need a meaningful stake in it.
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Applying HAZOP guidewords more, less, none, reverse, late, other than to every AI action in OT reframes how risk is assessed. Partner content with @skkynetinc. #skkynet_ai #everline_ai
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Gary Tillery of Skkynet notes that AI-native companies are at the forefront of managing organizations with both human and digital employees, and the broader industry can learn from how they are approaching this operating model rather than waiting for an HR-style policy to fill the gap.
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Tillery's model requires domain teams to maintain an evaluation suite that continuously defines whether an agent is still doing its job. Without that ongoing eval, a model upgrade can silently shift agent behavior and no one in the organization has a mechanism to detect the change.
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