Data security for industrial AI
AI Is Only as Good as Its Information Base
Artificial intelligence is increasingly becoming an essential tool in many industrial companies. A reliable information base is essential for AI to reach its full potential. This article shows how companies can secure their data.
Today, industrial companies can collect data from a wide variety of sensors, thereby obtaining a range of information for example, on vibrations, temperatures, or energy consumption. Artificial intelligence can analyze this data in a matter of seconds, enrich it with contextual data, and thus deliver valuable insights for example, for predictive maintenance or optimizing energy consumption.
Data plays a decisive role in determining whether AI projects will be successful: the data must be complete and accurate. A consistent, high-quality dataset is essential for AI to correctly identify correlations and provide reliable recommendations for action.
In practice, a reliable data set is not always a matter of course. Even a single calibration error in a sensor, for example, is enough to generate incorrect data. Communication failures or inconsistent data formats can also compromise the data set. In addition, in increasingly interconnected industrial environments, the attack surface for cyberattacks is constantly expanding. If criminals manage to gain access to the networks, they could inject false information, which can compromise the accuracy and reliability of the AI model.
In addition to defining the purpose of AI deployment and selecting an appropriate model, data must be the focus of every AI project. But how can a robust data foundation be created? In fact, several steps are required here. It is important to secure the data throughout its entire journey from collection at the machine, through on-site analysis, to transmission to the cloud.
How can data for AI models be protected?
This is precisely the approach supported by the Endian Secure Digital Platform: Data is collected at the machine or system via Endian 4i gateways, which were developed specifically for industrial environments. They are equipped with various connectivity options and can handle different machine protocols. This creates the conditions necessary to integrate them into the heterogeneous environments that are standard in many industrial companies today.
In addition, the Endian 4i gateways feature several finely tuned security functions. These include, for example, a firewall and an intrusion detection and prevention system (IDS/IPS) that automatically detects and blocks unusual activity. This allows communication to be monitored in order to block malicious applications in real time.
Network segmentation is another step toward securing the database. This involves dividing large networks into several small segments and isolating them using an Endian 4i gateway. The goal of network segmentation is to prevent the lateral spread of malware in the event that an attacker bypasses the firewall. In terms of data integrity, this means that any corrupted data is limited to a specific area and does not affect the entire system.
Why is granular access management important?
Consistent access restrictions are another step toward enhancing data security. Companies must carefully plan who is allowed to access which systems and what actions they can perform there. With the Endian Secure Digital Platform, granular and role-based authorization models can be implemented according to the “need-to-know” principle: Via a central dashboard, administrators can grant individual users or user groups access to exactly the machines and data that are essential for them to perform their tasks. The Endian Secure Digital Platform enables the management of both internal and remote access, for example, for external service providers. When remote access requests are made, a VPN tunnel is also established to protect the data from tampering.
In addition, it is possible to create strict authentication rules. Unique identification is required for every access attempt, regardless of whether the access occurs from within or outside the network. Both measures reduce the risk of unauthorized access and help ensure data integrity.
Edge computing creates a resilient data foundation
With the increasing performance of modern edge hardware, data can now be processed right where it is generated. Endian 4i gateways also provide the necessary infrastructure for local data processing. This reduces the number of processing steps and interfaces where information can be lost or manipulated.
Companies can filter, validate, or enrich measurement values with contextual information directly at the network edge. AI thus accesses a consistent data foundation without having to first transfer all raw data to external systems. This reduces the attack surface, protects sensitive production data, and simultaneously reduces bandwidth requirements. Since less data needs to be transferred and processed centrally, operating costs are also reduced.
Conclusion
The success of industrial AI applications depends on data quality. Complete, accurate, and readily available information forms the foundation for reliable analyses and automated decisions. A multi-tiered security strategy can help create the necessary conditions for this.
Frequently Asked Questions
Why is data quality critical for AI in industry?
AI only delivers reliable results when the underlying data is complete and correct. Faulty or incomplete data leads to wrong analyses and recommendations. A consistently secured data foundation is therefore the basis of every successful AI project.
Can attackers manipulate the data behind AI models?
Yes. If criminals gain access to an industrial network, they can deliberately feed in faulty data and distort the accuracy of the AI model. Even a miscalibrated sensor or a communication failure is enough to compromise the data foundation. That is why data must be secured along its entire path.
How can industrial data for AI be protected against cyberattacks?
It is important to protect data across its whole journey: from capture at the machine, through local analysis, to transfer into the cloud. The Endian 4i Gateways secure data collection with a firewall and intrusion detection and prevention, which automatically detect and block unusual activity.
Why is network segmentation important in OT?
Network segmentation divides a large network into smaller areas, separated by an Endian 4i Gateway. If an attacker gets past the firewall, the damage stays limited to one segment instead of spreading across the entire plant. Faulty data therefore affects only a contained area.
What benefits does edge computing offer for the data foundation of AI?
Edge computing processes data right where it is created, before it is transferred. The Endian 4i Gateways filter and validate readings at the network edge. This reduces the attack surface, protects sensitive production data, and lowers bandwidth needs and operating costs at the same time.