When considering implementation of machine learning software for production process intensification, a choice must be made: either developing a suitable solution in-house or starting cooperation with a service provider.
For organizations that prefer to focus their resources on production rather than software development, selecting the right machine learning partner involves evaluating technical capabilities, ways of working, and data security practices. This article focuses on data security considerations when implementing machine learning solutions in process industry environments.
As more companies adopt machine learning in production environments, secure handling of production data has become a critical success factor for implementation projects.
Machine Learning in Production Requires Strong Data Security
Data security is the foundation of fruitful cooperation between customer and ML providers. If it hasn’t been taken seriously, the systems are left vulnerable to unauthorized access, theft, and malicious attacks. This compromises production safety and efficiency and decreases competitiveness in customers’ own domain. Consumer trust can be lost only once, so if even once the customer data is exposed, it will destroy brand credibility, and businesses will take their money somewhere else. Process industry is no exception in this matter, as in creation of suitable machine learning solutions the data used contains production and laboratory data, information about operational technology, ICS/SCADA information, and other intellectual property, all of which may reveal valuable business information even when viewed separately.
Data Management Challenges for Machine Learning in Production Projects
All the data categories mentioned are very business-sensitive, so handling them requires highlevel precautions on what type of exposure might occur even if seemingly data security protocols are being followed. Even though there is a saying “there is no data like more data”, data minimization is a core step in protecting data used in machine learning in production projects. These are among the most common data management challenges in production machine learning projects, where sensitive operational data must be shared while maintaining strict security requirements. A close co-operation with ML provider enables a safe way to iterate used data without sending excessive amounts of it or concealing essential data fearing that it will leak.
Access Control as a Core Security Measure
Access control optimization is a key security measure, as it identifies and limits who can view, modify and interact with confidential data. What looks like a trivial way of securing the data from extra pair of eyes, limits blast radius of data spill. Following a principle of least privilege, even if one user account is compromised, strict access control limits how far an attacker can move within an environment containing sensitive information. It also buys time for corrective measures if and when an unwanted party has accessed the system. No wonder all of us have heard the advice to tell immediately if there is the slightest suspicion of breach in the system. Early detection and reporting remain critical for minimizing the impact of a potential security incident.
Why Data Anonymization Matters When Working with ML Providers?
Whenever production data is shared with external services or third-party tools, data anonymization should be considered a mandatory security measure. but it has a significant impact on organizations implementing machine learning in production environments. Model leakage is a true risk involved in compiling machine learning algorithms when third parties are involved, and it is rather surprising how little start information is needed for extracting valuable business secrets. Let’s take for example these widely used large language models. As they compare given information to literally everything found online, giving them production data with anonymized units and measurement names, it is still likely that just by knowing the context the ways of operating can be deduced. Even more compromising is giving the raw material data without properly masking it, in worst case the secret recipe of market-winning production leaks to common servers endangering future business opportunities.
How ISO 27001, AI Act and CRA Reduce Risk?
To define whether the data security has been implemented to workflow on a satisfactory level, several security certifications and standards indicating responsible data handling procedures have been put into use. Starting from the most essential that is ISO27001, providing a standard for information security risk management and acting as an indicator that vendor takes data security seriously. If a company has mentioned certificate, it means it has built an information security management system (ISMS), done its risk assessments involving data handling and passed external audit by accredited body.
Together, these frameworks help organizations assess whether a machine learning provider has the processes, controls, and governance needed to handle sensitive production data responsibly.
NIS2 and Supply Chain Security in Manufacturing
As we are exploring data security from the standpoint of process industry, NIS2 directive must be also discussed. NIS2 itself doesn’t directly govern machine learning software vendors, but as manufacturing, chemical production, water treatment and energy sector fall under its governance, requirements for being directive compliant extend all the way to machine learning software providers.
Machine learning projects often require access to production systems, operational data, and OT-related information. Once data can be shared securely, the next challenge is identifying which process factors actually influence production performance. We discuss this in our article on identifying and controlling unknown factors in manufacturing. As organizations strengthen their supplier risk management practices, security certifications and compliance frameworks become increasingly important when evaluating technology partners.
Choosing a Secure Machine Learning Partner
When satisfactory level of data security has been established, the discussion shifts from compliance to execution. Security certifications, regulatory frameworks, and governance processes are useful indicators when evaluating potential machine learning partners. They demonstrate that information security is managed systematically and that risks are taken seriously.
However, when comparing providers that already meet these requirements, the question is no longer who has the longest list of accreditations. The focus shifts towards how effectively audited procedures are implemented in practice and how efficiently they support the delivery of customer value.
In process industries, there is little value in collecting certificates for their own sake. At some point the discussion moves away from compliance and towards solving actual production problems. Data security enables that discussion to happen. Without it, there is no access to the data. Without access to the data, there is no machine learning project. Everything that comes after that depends on people.
But that is another story, yet to be written through successful cooperation between process industry experts and machine learning professionals. If data security concerns have delayed your machine learning initiatives, perhaps the right question is not whether the data can be shared, but how it can be shared securely. And if that question remains unanswered, we are always happy to discuss it.
