By Serge Thibault, VP Information Security at Poka
Key takeaways:
- AI adoption on the plant floor is outpacing security: 88% of food and beverage leaders say AI increases cyber exposure (AON), and connected devices, robots, and worker tools all expand the attack surface.
- A breach isn’t just a data problem; it’s a food safety problem. Altered systems can compromise safety even without any data being stolen, which raises the stakes above typical IT risk.
- Governance has to be built into the AI itself. Data kept out of external model training, human review for critical outputs, and RAG-grounded responses that return “no answer” over a guess. Manufacturers should expect this from any SaaS/AI provider, not treat it as optional.
Although the integration of AI is optimizing practices on the food and beverage factory floor, it is also opening up new breeding grounds for cyberattacks. According to a recent AON Global Risk Management survey, 88% of food and beverage leaders say AI increases their cyber exposure. This means AI security in food and beverage manufacturing has never been more on the agenda.
Cyberattacks or data breaches cost millions to resolve. They significantly diminish brand reputation, stakeholder and consumer trust, and put entire supply chains at risk. It is no surprise that properly governed AI for the frontline is crucial. This is especially important where there is notable reputational risk for manufacturers if cyberattacks or data breaches cause issues with food safety and quality. In a post for the Cybersecurity Association of the Food Industry, Scott Alldridge, CSAFI Board Advisor says: “If a malicious actor, or even an unintentional change, alters these systems, food safety is compromised even if no data is stolen.”
As food and beverage manufacturers race to adopt AI, they must also prioritize robust cybersecurity strategies to protect their systems, ensure operational continuity, and maintain trust across the F&B plant.
Smarter tools open up the digital world of technology vulnerability
With today’s food and beverage manufacturing facilities being more complex than ever, legacy systems are not advanced enough to fight today’s modern hacker. Food and beverage companies are increasingly using AI, from drones using image recognition to pick fruit, to cutting-edge technology that digitally reformulates a product’s ingredients without compromising flavor. This brings opportunities, but also new risks. To make matters worse, the introduction of AI tools makes manufacturing companies more dispersed and raises a raft of new threats.
AI tools have begun to touch many facets of the manufacturing process. Whether it’s for workforce training, safety monitoring, data collection, or even AI robots on production lines down on the factory floor, the inner workings of food and beverage manufacturing organizations may have become more connected and intelligent, but also require tighter and more governed data handling, human oversight, and safe outputs.
Take deploying connected worker technology, for example. While AI-driven applications streamline access to crucial information, enhance global communications, and accelerate time to value with automated digital content conversion, there are key security considerations that must be addressed to protect the data that feeds these systems.
The need to protect and fortify food and beverage manufacturing data
Food and beverage manufacturing data is highly sensitive, involving trade secrets, detailed production information, and masses of consumer data. A critical concern when implementing AI technologies is whether manufacturing data is ever shared with external AI providers. It is clear that for food and beverage companies, proper AI governance is non-negotiable.
Customer data is not used to train AI models and should only be processed by the SaaS provider and handled under tight controls, never sent to external AI model providers. All inputs, outputs, and embeddings must remain sealed within secure infrastructure — operated, monitored, and audited by the SaaS provider to guarantee full data sovereignty, privacy, and compliance. Advanced connected work addresses this by processing all data within secure environments and complying with strict data residency laws. With prompts and responses also processed entirely within the cloud environment, it enables food and beverage manufacturers to tap into powerful AI functionalities on the factory floor, while maintaining strict privacy, control, and compliance.
Keeping AI responses in check: the playbook for hazard-prevention
Safety and accuracy of AI outputs are paramount in food and beverage manufacturing settings, where errors can lead to real-world hazards. Manufacturers should confirm that AI responses are validated for safety and correctness, with outputs professionally phrased and aligned with customer-specific context.
To minimize the risk of unsafe or incorrect AI outputs in manufacturing settings, organizations should implement a layered set of guardrails and validation controls:
- Human-in-the-Loop (HITL) verification: For the most critical outputs, such as safety protocols or complex work instructions, manufacturers should implement a workflow where a qualified human expert must review and approve the AI-generated content before it is finalized. This provides a final layer of verification, serving as the ultimate safety net to catch subtle errors or contextual nuances that automated systems might miss.
- Content filtering at ingress: Ensure AI guardrail filters are established to block unsafe inputs before they reach the model.
- Prompt injection and adversarial input detection: Pre-assess inputs to identify malicious intent or system prompt leaks.
- Secure prompt and response handling: Process all AI interactions within a secure, customer-dedicated environment; encrypt logs at rest and in transit; and enforce strict access controls so that prompts, responses, and telemetry can be audited but never exfiltrated for model training.
- Retrieval-Augmented Generation (RAG) for output grounding: Anchor every AI response in verified, customer-specific source content. When no relevant context exists, configure the model to return “no answer” rather than risk hallucinations.
- Bias, profanity and scope-drift prevention: Include output-screening mechanisms that check for inappropriate or biased language, ensure responses remain scoped to the customer’s own data, and enforce professional phrasing.
- Multilingual and cultural safety: Automatically match the response language to the input, and apply localization or translation when contexts differ, preserving clarity and cultural relevance.
- Regular adversarial testing of AI: Dedicated adversarial test suites are regularly executed to evaluate and improve prompt injection protections.
SaaS providers in the spotlight for compliance and clarity
While AI governance is a shared burden between customer and SaaS provider, manufacturers in high-stakes environments such as food and beverage manufacturing expect more than powerful features. They demand safe, compliant, and trustworthy AI. This responsibility begins with a provable foundation of security and data integrity, validated through rigorous, independent audits and adherence to industry-best practices.
However, true AI governance extends deep into the product itself. It is the SaaS provider’s duty to build in the technical guardrails that ensure transparency, fairness, and alignment with established operational and safety standards. For example, systems that use Retrieval-Augmented Generation (RAG) to ground AI responses exclusively in a client’s verified database prevent dangerous “hallucinations” and ensure outputs are contextually accurate.
For a software provider, embracing this responsibility is a strategic mandate. Proactively embedding ethical controls and robust governance transforms a product from a simple tool into a trusted, strategic asset. By doing so, SaaS providers not only mitigate their customers’ legal and reputational risks but also build the essential trust needed to drive safe, sustainable adoption and long-term operational excellence.
Secure a safer foundation for AI in food and beverage manufacturing
AI is a significant optimizer of factory floor operations in the food and beverage industries. While the integration of AI opens new doors and opportunities for significant productivity gains, it also creates opportunities for security breaches or data inaccuracies within AI security on the food and beverage frontline.
As factories increase in connectivity and upgrade to smarter AI-based initiatives, AI connectivity must be their focus to drive data security initiatives. Factory connectivity will enable them to autonomously implement cybersecurity standards and verify compliant AI responses. Not only this, but proper AI governance can protect plant data.
Only this way will the window of opportunity open up for food and beverage manufacturers to safely invest in AI in manufacturing without compromising operational security.
Thibault leads information security and IT for a global B2B SaaS platform serving industrial and manufacturing organizations. At Poka, he plays a key role in the secure adoption and governance of AI capabilities within the platform, ensuring responsible use, data protection, and risk management while enabling innovation.
