How AI Solves SCADA System Questions Around Remote Security

Security is a massive global industry and it is extremely diverse. Whether it is the protection of prize racehorses, or guarding utility infrastructures, the ‘threatscape’ is increasingly complex and requires a convergence of cybersecurity and the more traditional, physical forms of preventing theft and other forms of criminal activity.

Artificial Intelligence (AI) is the most promising tool for deploying digital security, but as Louis Columbus remarks in a recent Forbes article, “What needs to be the top priority is improving the accuracy, insight, and speed of response to remote threats that AI-based video recognition systems provide.”

Remote security monitoring is the way forward. Companies want video cameras capable of night vision, infrared, and thermal imaging that provide 24/7 monitoring of equipment, sites, and valuable assets.

One such company is Twenty20 Solutions, which is integrating machine learning with real-time video data feeds of remote sites. Its system is also able to identify any activity anomalies in real time, something that the market is clamouring for. Columbus says, “What’s noteworthy about this approach is the need many industries have to integrate cyber and physical security systems and get a 360-degree view of remote location threats.”

Twenty20 Solutions uses a SCADA system, which is the acronym for “Supervisory control and data acquisition.” This type of system has become the gold standard in industries such as telecommunications, water and waste control, energy, oil and gas refining, and transportation. Typically a SCADA system “integrates sensors, gauges, and devices to provide data telemetry and real-time information.”

Research company IMARC has predicted worldwide growth in the SCADA market, assessing its value in 2024 at around $26 billion – a CAGR of 5.7% between 2019 and 2024.

One of the most important things to remember about AI and machine learning technology is that both are excellent at picking out visual anomalies via video recognition systems. There are three types of machine learning algorithms that support this ‘talent’, and will form a part of future-generation video recognition systems.

One is ‘Supervised Learning Algorithms’. Here the algorithms are trained via data sets “to identify correct images of objectives, so when an anomalous image appears, they can identify it.” For example, supervised machine learning algorithms can identify, track, and monitor vehicle, machinery, asset, and remote location use patterns.

Another is ‘Unsupervised Learning Algorithms’. These identify new patterns in images, and the oil and gas industry find it extremely useful for monitoring infrared and thermal data from remote equipment and assets.

Lastly, there are ‘Reinforcement Learning Algorithms’. With this format, AI-based video recognition systems use algorithms to correct how they identify and update known images. In the case of industries that are heavily dependent on expensive equipment, this type of algorithm “helps to ensure the consistency, compliance levels, and safety of remote equipment located in diverse geographical locations.”

The future of AI-led security is also important from the perspective of the Internet of Things, with video emerging as the most powerful sensor, and as Columbus remarks, “Agriculture, construction, oil & gas, utilities, and critical infrastructure industries need to consider how their IoT platforms can be integrated into a broader cybersecurity strategy to gain a 360-degree view of multifaceted threats being launched at their locations with increasing frequency.”

Ultimately, it is the ability to identify threats in real-time that will boost the use of AI in this sector. It will close the gap between the traditional physical security measures and cybersecurity, thwarting criminal activity in ways the criminals will have to work much harder to find a way around them, if there is indeed a way to get around them at all.