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Mon, 10/12/2026 – 09:56
As manufacturers adopt agentic AI, autonomous systems are gaining access to sensitive data and critical operations – often faster than security controls can keep pace. The 2026 Thales Data Threat Report: Manufacturing Edition highlights the risks, from limited data visibility and rising credential theft to gaps in cloud encryption, and outlines practical steps to help secure AI adoption.
Data Security
Cloud Security
Encryption
Marcelo Delima | Senior Manager, Global Solutions Marketing
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Manufacturers have always depended on automation. From robotics on the factory floor to predictive maintenance, connected supply chains and digital twins, the industry has long understood the value of machines that can work faster, more consistently and more efficiently than humans alone. But the next wave of automation is different. Agentic AI systems do not simply analyze data or generate recommendations. They can reason, invoke tools, make decisions and act autonomously across business and operational systems. In manufacturing, where operational uptime, intellectual property, supply chain continuity and worker safety are all mission critical, that shift has profound data security implications.
The 2026 Thales Data Threat Report: Manufacturing Edition shows that manufacturing organizations are rapidly confronting this reality. Two-thirds of manufacturing respondents (67%) cited rapid change in AI ecosystems as their top AI security risk. That is not surprising. AI platforms, models, APIs, Model Context Protocol (MCP) servers and agent frameworks are evolving faster than many governance models, security tools and operational processes can adapt. The risk is no longer only that a human user might make a mistake with a chatbot. It is that an autonomous or semi-autonomous agent could access sensitive data, trigger workflows, interact with other systems and amplify mistakes at machine speed.
The Manufacturing Edition draws on a survey of 417 security and IT executives at durable and non-durable manufacturers in 20 countries, conducted by S&P Global 451 Research as part of the 3,120-respondent 2026 Thales Data Threat Report. Here are the five data security risks it surfaces for the agentic era.
Key Takeaways: Manufacturing Cybersecurity in the Agentic AI Era
- 67% of manufacturers rank rapid change in AI ecosystems as their top AI security risk.
- Only 31% have complete knowledge of where their data is stored, and only 37% can classify all of it.
- Just 42% of manufacturers' sensitive cloud data is encrypted, down from 57% in 2025.
- 57% report rising credential theft and misappropriated secrets; cloud storage (36%), cloud-delivered SaaS applications (29%) and cloud management infrastructure (26%) are the top attack targets.
- 83% run five or more data protection tools, an average of eight, yet only 35% are confident they understand their data security tools.
- 59% name harvest now, decrypt later as their top quantum concern, and 53% are refactoring application and data architectures to meet sovereignty objectives.
Agentic AI Is the New Insider Threat in Manufacturing
In many ways, AI is becoming the new insider threat. Traditional insider risk typically focuses on employees, contractors or compromised identities with legitimate access to systems. Agentic AI changes that model. An AI agent may be granted access to data repositories, engineering systems, customer records, supplier portals, code repositories, MES and ERP platforms or operational dashboards so it can complete a task. If that agent is misconfigured, manipulated or insufficiently governed, it may expose data, take unauthorized action or make decisions no human intended. The agent itself may not be malicious, but its access, autonomy and speed can turn it into a powerful pathway for risk.
Sensitive data disclosure is already one of the most visible warning signs. According to the 2026 Thales Data Threat Report, Manufacturing Edition, 59% of manufacturing respondents said sensitive data disclosure attacks against AI or LLM environments are increasing. Manufacturing organizations hold enormous volumes of high-value information: product designs, production data, supplier terms, customer information, quality data, maintenance records, software code and operational telemetry. As agentic systems gain access to more of this information, the question becomes less about whether AI can improve productivity and more about whether organizations can control what AI can see, use, infer and share.
Data Visibility: Only 31% of
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