Metals sector security question

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By Tarun Mathur  LinkedIn
ABB Metals Digital Lead
About the author
Tarun Mathur graduated from the Indian Institute of Technology, Mumbai and holds a Master’s degree in mathematical modeling and process control.

He has held several positions in R&D focusing on the development of advanced model-based solutions for process industries.

Tarun is currently focused on projects applying new digital technologies to optimize metals plant performance, processes and quality.

With an integrated steel plant and interconnected devices discussed in my previous blog, comes the real threat of data compromise having an impact on multiple fronts. Robust data security systems, processes, philosophy and infrastructure are important for mitigating this very key risk associated with IIoT adoption

A question I frequently receive as part of digitalization rollouts is how data is managed and secured. The presence of big data has necessitated a more modern approach to data handling. Without doubt, data is the most valuable asset in the digital economy – it is competitive advantage and any data breach can have a far-reaching impact. This can be in the form of erosion of competitive advantage, impact on commercial operations, customer confidence and brand reputation. Data resting in an external resource like the cloud can cause further concerns about data compromise and its consequences. 

 

The good news: With our ABB Ability™ platform and other robust IIoT offerings we have thought through security as a key issue. Today, such platforms proactively address data security and IP protection as a basic requisite.  

Six factors that drive data security

Measures proactively addressing data security are a basic requisite of ABB's digital offerings

In my opinion, six parameters drive data security and data architecture in the IIoT framework. These are management, quality, governance, integration, analytics and privacy. 

 

Management  

This step defines standards for data aggregation and for enabling right access across different levels. There are multiple layers and sources of data that are addressed to avoid information chaos. 

 

Quality  

The quality of the data is determined by its source, further dependent on how updated and automated the machines are. Good data quality helps provide insights which are based on relevant analytics, in real-time. Legacy systems without necessary upgrades may not provide data that can be successfully used in the IIoT framework. This step may also reveal areas where data quality is poor or non-existent.  

 

Governance  

Data is now amongst the most valuable company assets and needs stringent access controls. Data governance determines where the feed comes from, how it is analyzed, and who can access what data. The extension of this is finalizing insights that are helpful and relevant for diverse stakeholders to create a comprehensive platform. 

 

Integration  

The entire management-quality-governance data cycle influences the choice of protocols, data centers and edge gateways that are deployed as part of the IIoT architecture.  

 

Analytics  

The key to analytics lies in knowing what data from aggregated sets are to be used, and the insights that should be delivered. Assets have to be optimized in a digitalized environment to enable predictive models. It is important, therefore, to get the choice of IIoT architecture right based on the plant and its readiness for Industry 4.0. 

 

Privacy  

The volume of data and impact on business mean privacy requirements that are greater than ever before. Remote access to systems and data increases the probability of unauthorized access and cyberattacks. Privacy extends from personal data and data security to IT security.  Processes must, therefore, be defined at the planning stage itself.   

In the smart plant both technology and trained personnel are key to increasing vigilence and ensuring cyber security

Phishing, malware attack and cyber resilience

Security in a smart plant is more than just physical; and more than just about data protection. Smart plants may be more vulnerable to cyberattacks, which include unauthorized access, malicious sensors for data theft, system attacks, malware, and software failure.  

I recommend creating a complete security plan that relies on technology and personnel as key to cyber vigilance.

The route to cyber resilience

Increase your vigilence

The ABB Data Security manifesto

In digitalization solutions implemented by us for all industries, including metals, we use the ABB Data Security manifesto as our approach to data security. This manifesto accounts for most concerns around deploying IIoT. Key principles include: 

  • Your data remains yours: all data is owned by the client and access to it requires authorization 

  • You know what we do with your data: transparency at every step of the way in how data is used 

  • We will not disclose your data without your consent: or without having compelling, clearly defined reasons to do so 

  • Ensuring data/IP is not shared with or used for the benefit of competitors: through an approach which ensures that data from no two customers is co-located 

 

So, as process industries adopt Industry 4.0 and IIoT, they can rest assured that their data is in secure hands and always acts as an asset in progress rather than being a source of risk.  

Sneak peek

New digital tools enable new solutions to old problems as well as radically new approaches. In my next blog I will explore the ways to go beyond general efficiency enhancements and how to use digitalization for addressing the key issues unique to the metals industry...

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Learn more

Integrated steel plant and the path to interoperability in metals industry
Cyber security services from ABB
Digital transformation in metals and steel industries
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