• Written by Azhar Aliyar, Director

A procurement manager for a large chemicals manufacturer checks the purchase order table to ensure that the latest shipment of raw materials will meet the production schedule, but the variability in feedstock quality is causing yield drift that threatens to disrupt the entire process. This is a common challenge in the chemicals industry, where continuous processes with tight yield and safety envelopes require careful planning and management.

Chemicals industry complexities

The chemicals industry presents unique challenges for enterprise AI security, particularly in procurement. Yield drift, feedstock variability, and plant-to-plant recipe differences all contribute to a complex environment that requires careful consideration. For example, a shift supervisor at a chemicals plant must constantly monitor the production process to ensure that the yield remains within acceptable limits, while also adjusting for variations in feedstock quality. This can be a difficult task, especially when the recipes used in different plants are not identical.

Most people get wrong the idea that these complexities can be easily managed with standard enterprise AI security protocols. They are wrong, because the chemicals industry requires a tailored approach that takes into account the specific challenges of continuous processes and tight yield and safety envelopes. A one-size-fits-all solution will not work, and a more nuanced approach is needed to ensure the security and privacy of sensitive data.

Data processing and storage

When deploying an enterprise AI system in the chemicals industry, it is essential to consider where data is processed and stored, and whether it trains anything. The data may be stored in a cloud-based database, but it may also be processed on-premises, depending on the specific requirements of the plant. For instance, a category manager may need to compare data from different suppliers to ensure that the raw materials meet the required standards, and this data may need to be processed locally to ensure timely decision-making.

The AsscherAi platform provides a secure and scalable solution for data processing and storage, but it is crucial to understand the specific requirements of the chemicals industry and to configure the system accordingly. This includes ensuring that data is properly encrypted and access controls are in place to prevent unauthorized access.

Prompt injection as a data-boundary problem

Prompt injection is a potential security risk in enterprise AI systems, particularly in the chemicals industry where sensitive data is involved. It occurs when an attacker injects malicious prompts into the system, which can then be used to extract sensitive data or disrupt the production process. This is a data-boundary problem, as it involves the intersection of different data sources and systems.

To mitigate this risk, it is essential to implement robust access controls and data validation mechanisms. This includes ensuring that all prompts are properly sanitized and validated before they are processed by the system, and that access to sensitive data is strictly controlled. By taking a proactive approach to prompt injection, chemicals manufacturers can help to ensure the security and privacy of their data.

Least privilege for assistants

Enterprise AI assistants, such as those used in the chemicals industry, often require access to multiple systems and data sources to function effectively. However, this can create a security risk if the assistant has too much privilege, as it can potentially access sensitive data or disrupt the production process. To mitigate this risk, it is essential to implement a least privilege approach, where the assistant is granted only the minimum level of access necessary to perform its tasks.

This can be achieved through the use of role-based access controls, where the assistant is assigned a specific role that defines its level of access and privilege. For example, a procurement assistant may only need access to the purchase order table and supplier data, while a production assistant may need access to the production schedule and yield data. By implementing a least privilege approach, chemicals manufacturers can help to ensure the security and privacy of their data.

Limitations and exceptions

While enterprise AI can be a powerful tool for chemicals manufacturers, there are certain limitations and exceptions that need to be considered. For instance, enterprise AI may not be suitable for all types of production processes, particularly those that involve highly sensitive or proprietary information. In such cases, alternative solutions may be needed, such as manual data processing or specialized software.

If you are unsure about the suitability of enterprise AI for your specific use case, it is recommended that you contact us to discuss your options. Our team of experts can help you to determine the best approach for your needs and ensure that your data is properly secured and protected.