Automation is reshaping industries, but its applications vary based on sector-specific challenges and objectives. Automation, artificial intelligence (AI) and cybersecurity play distinct roles in oil and gas, energy and factory automation.
AI and cybersecurity insights
- Automation and AI differ in their applications across oil and gas, energy and factory automation.
- Cybersecurity challenges and solutions impact industrial automation in critical sectors.
- Key technologies and trends are shaping the future of industrial automation.
Automation has become the backbone of industrial operations, reducing human error, improving efficiency and enabling predictive maintenance. However, different industries face distinct operational, regulatory and cybersecurity challenges, requiring unique automation strategies.
This article explores three industries — oil and gas, energy and factory automation — highlighting how artificial intelligence (AI), cybersecurity and networking are shaping their automation landscapes.
1. Oil and gas: Balancing efficiency and safety in harsh environments
The oil and gas industry operates in some of the most hazardous and remote locations. Automation plays a critical role in maintaining safety, ensuring equipment reliability and optimizing production.
Key emerging technologies in oil and gas automation
The industry uses Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) to enable remote monitoring and control of pipelines, refineries and offshore rigs. AI-driven predictive maintenance, which applies machine learning models to analyze sensor data and detect anomalies in drilling equipment and pipelines before failures occur, is an emerging use of AI in this field.
Cybersecurity challenges in oil and gas
Many oil facilities still use outdated systems vulnerable to cyberattacks. The industry has some specific vulnerabilities.
The ways in which those threats are addressed require some specific solutions.
Attacks on oil pipelines, such as the Colonial Pipeline cyberattack, highlight the importance of network segmentation, endpoint security, training and cyber hygiene.
AI-based threat detection systems monitor industrial networks for unusual activity, identifying and mitigating cyber threats before they impact operations. Better network visibility is a powerful tool for responding to and detecting incidents.
2. Energy sector: Smart grids and cyber-resilience
The energy sector is undergoing rapid digital transformation, with automation improving grid reliability, optimizing power generation and integrating renewable energy sources.
Key emerging technologies in energy automation
Energy automation relies on smart grids and AI-driven machine learning models to optimize power distribution and predict outages. Industrial Control Systems (ICS) automate energy transmission across substations, reducing downtime. An emerging application is AI-enhanced Distributed Energy Resource Management Systems (DERMS), which balance demand and supply across decentralized energy grids.
One of the most interesting — and somewhat paradoxical — elements of this relationship is that AI adoption is significantly increasing energy demand particularly in data centers. While AI is improving energy efficiency, it is also indirectly driving the need for more power.
Cybersecurity challenges in energy
Power grids are prime targets for cyber warfare, making a solid industrial cybersecurity plan essential for resilience.
And when it comes to regulatory compliance, companies not covered by the North American Electric Reliability Corp.’s Critical Infrastructure Protection (NERP CIP) regulations still struggle to develop customized security plans. While the National Institute of Standards and Technology (NIST) is the most widely used framework, International Society of Automation’s (ISA) 62443 provides additional guidance for securing industrial control systems.
Securing IoT and edge devices within substations and energy distribution networks is crucial for preventing cyber intrusions and minimizing the attack surface.
3. Factory automation: AI-powered smart manufacturing
Factories are adopting Industry 4.0 technologies, where AI, IoT and advanced robotics transform traditional manufacturing into intelligent, autonomous production systems.
Key emerging technologies in factory automation
Factories are implementing edge AI and machine vision to enable real-time defect detection and waste reduction. Collaborative robots (cobots) assist human workers in improving efficiency and accuracy. Industrial internet of things (IIoT) sensors continuously monitor machinery health, enabling predictive maintenance and minimizing downtime.
Cybersecurity challenges in factory automation
The integration of information technology and operational technology (IT/OT) networks requires security adaptations to prevent disruptions in manufacturing operations.
AI-powered anomaly detection systems enhance cybersecurity by monitoring and identifying threats within production environments.
This remains a significant issue in industrial cybersecurity, increasing the risk of lateral movement attacks.
The role of networking in industrial automation
A robust industrial network is the backbone of automation, enabling secure and reliable communication between devices, controllers and cloud-based systems. A well-designed network ensures low latency, high availability and cybersecurity protection in industrial environments.
Key characteristics of an effective industrial network
A reliable industrial network must incorporate redundancy and resilience, using architectures such as ring and mesh topologies to minimize downtime. Time-Sensitive Networking (TSN) enables low-latency data transmission, essential for factory automation.
Security measures such as segmentation and advanced switching security protections prevent unauthorized access. Furthermore, networks should support industrial protocols like Modbus, PROFINET and Ethernet/Internet Protocol (IP) to ensure interoperability. As industrial automation expands, scalability is necessary to accommodate the increasing number of IIoT devices and growing data volumes.
The future of industrial automation
AI adoption is increasing exponentially, with many applications still in the testing phase. However, as industrial control systems have serious consequences when incidents occur, many decisions are still reviewed by human operators. As AI systems become more precise and trustworthy, we may see a future where some operational decisions are fully automated.
On the other hand, cybersecurity remains a top priority. Implementing a ‘secure by design’ architecture to prevent unauthorized access and reduce exposure to cyber threats is essential. AI-based anomaly detection systems provide real-time threat intelligence, identifying irregular network activity before it escalates into a breach. In many cases, virtual patching is emerging as a creative solution to protect legacy equipment, allowing it to function securely despite the challenges of deploying regular updates and patching SCADA and Industrial Control Systems software.
Automation is no longer just about efficiency — it’s about intelligent decision-making, secure connectivity and resilience against cyber threats. Oil and gas industries must balance efficiency and safety while securing legacy systems. The Energy sector requires robust cybersecurity to protect critical infrastructure and meet new energy demands — being perhaps the only sector physically overloaded by AI adoption.
Industries that successfully integrate AI, secure networking and strong cybersecurity frameworks will gain a competitive advantage, ensuring operational efficiency, regulatory compliance and business continuity.
