AI is moving into predictive maintenance, quality inspection, engineering, scheduling and supply-chain operations. As AI connects business data with operational environments, manipulated inputs and unsafe AI actions can create real operational consequences.
Cygeniq helps organizations discover AI that touches operations, test it against AI-specific attacks, protect it at runtime, quantify risk and detect AI-native threats through one Runtime AI Trust Platform.
One platform across IT, OT and supply-chain AI
Industrial cybersecurity protects assets, networks and operational systems. AI adds prompts, retrieval systems, agents and changing model behavior to that environment.
Supplier documents can introduce malicious instructions. Engineering copilots can expose sensitive IP. AI with write-back authority can turn a manipulated recommendation into an operational action.
AI sits alongside IT and OT systems, so existing industrial cybersecurity controls cover it. Model and prompt changes look like ordinary software updates.
AI recommendations reach engineers and operators making real decisions, and AI with write-back authority can turn a manipulated input into a physical operational action without triggering conventional security alarms.
This creates risks including:
Industrial AI security needs to consider what each AI system can access, influence or change.
Manipulated recommendations can influence production processes.
Adversarial manipulation or model drift can affect quality decisions.
AI retrieval can expose sensitive designs, recipes, tolerances and supplier information.
Untrusted supplier content can manipulate AI-supported planning and scheduling.
Locally adopted AI can remain outside established IT and OT asset inventories.
Agents that re-route fleets, re-sequence loads or instruct warehouse robots act on live operations. A manipulated instruction or poisoned telemetry feed becomes a physical action.
One connected sequence, from discovery through defense, mapped to the modules that deliver each step.
Inventory models, agents, corpora and APIs with ownership, data classification and operational authority.
Run adversarial testing for prompt injection, engineering-data extraction and manipulation of AI recommendations.
Monitor prompts, outputs and agent actions while AI systems operate and retest them as components change.
Assess risk based on operational consequence and write-back authority.
Connect AI assets with risk, controls and evidence alongside existing industrial governance processes.
Detect threats such as AI agent misuse, poisoned decision inputs and data exfiltration through AI outputs.
AI phishing of suppliers and plant staff, deepfake vendor-payment fraud, AI-authored polymorphic malware, and autonomous machine-speed intrusions moving toward OT, detected and contained, with attack simulation to prove readiness before the next one.
Book a walkthrough of discovery, adversarial testing, runtime protection and AI-native detection for industrial AI.
| AI Use Case | Key AI Security Risk |
|---|---|
| Predictive Maintenance | Manipulated or poisoned operational inputs |
| Quality Inspection | Adversarial manipulation and model drift |
| Engineering Copilots | Sensitive IP exposure |
| Production Optimization | Unsafe recommendations and write-back actions |
| Supply Chain AI | Prompt injection through supplier content |
| Scheduling & Logistics Agents | Manipulated decisions and excessive permissions |
| Plant-Level AI | Shadow AI outside established inventories |
Controls and evidence map once, in GRCortex AI, to the regimes an industrial operator answers to: IEC 62443, NIS2, the EU AI Act (AI as a safety component of machinery and vehicles is high-risk), the EU Machinery Regulation, UNECE R155/R156 for connected vehicles, ISO/IEC 42001 and the NIST AI RMF, with technical testing aligned to the OWASP LLM and Agentic Top 10 and MITRE ATLAS.
Continuous adversarial red teaming from a 1B+ scenario library, model validation and drift monitoring for every model, copilot and agent in scope, with findings flowing into GRCortex AI as evidence and into CyberTix AI as detection context.
Dynamic AI risk register fed by runtime findings, controls mapped once across every framework in scope, continuous control monitoring, audit-ready evidence and board-ready AI assurance.
The runtime security layer for enterprise AI. CyberTix Secure discovers every LLM, RAG pipeline and agent, blocks AI-native attacks inline and contains threats in real time. CyberTix Defend stops AI phishing and BEC, deepfakes, AI malware and autonomous intrusions, one backbone, one AI-risk picture.
Discover → Understand → Validate → Protect → Govern → Monitor → Assure → Improve
A manufacturing, transportation and logistics customer runs Cygeniq across predictive-maintenance AI, quality-inspection AI and supply-chain scheduling AI with adversarial testing and runtime guardrails in place, operational continuity maintained, no critical AI breaches during the engagement, compliance evidenced continuously.
Reference calls available under NDA.OT security protects industrial infrastructure and communications. AI security adds protection around models, prompts, agents, retrieval systems and AI behavior.
Yes. AI consuming supplier documents, maintenance information and other untrusted content can be exposed to indirect prompt injection.
Agents may have permissions to access data or influence systems, making excessive authority and manipulated instructions important risks.
Cygeniq combines AI discovery, adversarial testing, runtime controls, risk governance and AI-native threat detection.
Yes. Hexashield AI red-teams engineering and PLM copilots for IP extraction, and CyberTix AI's egress guard blocks designs, recipes, tolerances and supplier terms from leaving through prompts or outputs at runtime.
Protect AI from engineering and production through transportation, logistics and supply-chain operations.
Cygeniq helps you secure AI systems, govern AI risk and prove control.
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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