Silesian Catalysts
Data-Driven Solutions for Cybersecurity and Quality Control

It was 2010 when an unprecedentedly masterful computer worm, Stuxnet, made the headlines. This malicious worm was the first publicly-known threat to target industrial control systems (ICS). Stuxnet could compromise the programmable logic controllers (PLC), alter their programming, and grant control of specific systems to the attackers.

Since Stuxnet’s uncovering, there has been a surge in sophisticated cyberattacks that target operational technology (OT) networks. Industries, including power, petrochemical, and oil and gas, have constantly been at the receiving end of such attacks over the last few years, underlining the fact that their critical assets are no longer air-gapped.

Evidently, with these attacks on the rise, organisations are turning to information technology (IT) departments to fortify their critical assets. However, there are fundamental differences in infrastructure and security priorities of IT and OT networks that render such efforts half-baked. For instance, unlike IT networks that operate solely in a cyber-realm, OT networks cater to a cyber-physical world, which necessitates security of the physical infrastructure as well. As a result, IT cybersecurity tools have some blind spots when addressing the security requirements of OT networks. Explaining this gap, Pawel Niklewicz, an industrial cybersecurity expert and a member of the board at Silesian Catalysts, says, “While the IT tools can cater to security requirements of distributed control systems (DCS) or supervisory control and data acquisition (SCADA) systems, they do not tackle security challenges at the PLC or field equipment layer.”

It is indeed this very drawback in the industrial cybersecurity space that the team at Silesian Catalysts is addressing with their diagnostic tool – Advanced Process Sentinel. The tool ensures end-to-end security of critical assets in the PLC or field equipment layer by applying artificial intelligence (AI) on sensor data.

Comprehensive Cybersecurity at Level 0

Advanced Process Sentinel enables the detection of even the most subtle anomalies in sensor data to ascertain whether the industrial installations and their data are manipulated or compromised by any threat vectors.

The anomalies are detected at three phases to validate this. The first phase is the analysis of noise from the sensor signals. The control and measurement systems consist of thousands of sensors, and each of them generates a different measurement noise. The measurement noise is like a fingerprint that will be distinct for each sensor. Advanced Process Sentinel analyses each sensor signal and its corresponding noise. The tool then compares these values with the predicted values obtained from virtual sensors. Leveraging machine learning algorithms, Advanced Process Sentinel, finally, determine whether there has been any external interference on the system. Similarly, Advanced Process Sentinel detects anomalies at two other phases – the sensor signals’ control loops and the signals’ timestamps. The tool can detect even the smallest of changes in their measurements, thereby guaranteeing the integrity of sensor data.

“With the probability of insider attacks ever-increasing, it is therefore becoming imperative for organisations to validate the reliability of data at this physical infrastructure level,” states Radosław Łapczyński, CEO at Silesian Catalysts.

But what makes Silesian Catalysts’ Advanced Process Sentinel further appealing for organisations is its competency in utilising the sensor data for predictive maintenance of their critical assets. The tool monitors the operation of sensors and notifies the maintenance department if there is an impending sensor degradation or failure. “There have been many occasions where the whole industrial installations had to cease operations due to sensor failures,” remarks Niklewicz. “If they had predicted such failures early on, the organisations could have avoided incurring those huge losses,” he adds.

Silesian Catalysts’ competencies do not end there. By employing virtual sensors, the company also aids organisations in increasing their productivity. To illustrate this capacity, Niklewicz cites a recent client success story. A major European oil and gas company recently onboarded Silesian Catalysts to optimise their atmospheric distillation of crude oil. “By implementing a small temperature change (2 degree Celsius), we enhanced their distillation cuts and enabled them to make additional profit of around $3 million per year,” avers Niklewicz. The situation is an ideal testament to Silesian Catalysts’ capability, as the company boasts a wealth of experience operating as a chemical synthesis company in the space. This knowledge has rightly enabled them to comprehend the challenges of their oil and gas clients firsthand and leverage sensor data to enhance the operational efficiency of the client’s critical assets.

Backed by such proven cases, it is indeed clear that Silesian Catalysts’ efforts are pertinent in reinforcing OT cybersecurity and optimising quality control, and consequently equipping organisations with ideal tools to adequately deal with their collected data sets. “By bringing together cybersecurity, predictive maintenance, and data governance under one roof, we are putting forth an ideal solution for organisations to manage and optimise their critical assets securely and effectively,” affirms Niklewicz. As the small and large organisations alike, are finding themselves in a burgeoning OT threat landscape, Silesian Catalysts’ Advanced Process Sentinel is a sine qua non for them rather than a nice-to-have.