For oil and gas companies, digital transformation is a priority—not only as a way to modernize the enterprise, but also to secure the entire energy ecosystem. With that lens, the urgency of applying artificial intelligence (AI) and machine learning capabilities for optimization and cybersecurity becomes clear, especially as threat actors increasingly target connected devices and operating systems, putting the oil and gas industry in collective danger. The year-over-year explosion in industry-specific attacks underscores the need for meaningful advancements and maturity in cybersecurity programs.
However, most companies don’t have the resources to implement sophisticated AI programs to stay secure and advance digital capabilities on their own. Irrespective of size, available budget, and in-house personnel, all energy companies must manage operations and security fundamentals to ensure they have visibility and monitoring across powerful digital tools to remain resilient and competitive. The achievement of that goal is much more likely in partnership with the right experts.
MIT Technology Review Insights, in association with Siemens Energy, spoke to more than a dozen information technology (IT) and cybersecurity executives at oil and gas companies worldwide to gain insight about how AI is affecting their digital transformation and cybersecurity strategies in oil and gas operating environments. Here are the key findings:
Energy sector organizations are presented with a major opportunity to deploy AI and build out a data strategy that optimizes production and uncovers new business models, as well as secure operational technology. Oil and gas companies are faced with unprecedented uncertainty—depressed oil and gas prices due to the coronavirus pandemic, a multiyear glut in the market, and the drive to go green—and many are making a rapid transition to digitalization as a matter of survival. From moving to the cloud to sharing algorithms, the oil and gas industry is showing there is robust opportunity for organizations to evolve with technological changes.
In the oil and gas industry, the digital revolution has enabled companies to connect physical energy assets with hardware control systems and software programs, which improves operational efficiency, reduces costs, and cuts emissions. This trend is due to the convergence of energy assets connected to OT systems, which manage, monitor, and control energy assets and critical infrastructure, and IT networks that companies use to optimize data across their corporate environments.
With billions of OT and IT data points captured from physical assets each day, oil and gas companies are now turning to built-for-purpose AI tools to provide visibility and monitoring across their industrial operating environments—both to make technologies and operations more efficient, and for protection against cyberattacks in an expanded threat landscape. Because energy companies’ business models rely on the convergence of OT and IT data, companies see AI as an important tool to gain visibility into their digital ecosystems and understand the context of their operating environments. Enterprises that build cyber-first digital deployments similarly have to accommodate emerging technologies, such as AI and machine learning, but spend less time on strategic realignment or change management.
Importantly, for oil and gas companies, AI, which may have once been reserved for specialized applications, is now optimizing everyday operations and providing critical cybersecurity defense for OT assets. Leo Simonovich, vice president and global head of industrial cyber and digital security at Siemens Energy, argues, “Oil and gas companies are becoming digital companies, and there shouldn’t be a trade-off between security and digitalization.” Therefore, Simonovich continues, “security needs to be part of the digital strategy, and security needs to scale with digitalization.”
To navigate today’s volatile business landscape, oil and gas companies need to simultaneously identify optimization opportunities and cybersecurity gaps in their digitalization strategies. That means building AI and cybersecurity into digital deployments from the ground up, not bolting them on afterward.
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
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