Managers at most big utility plants, refineries, and factories lack basic empirical data about the risks facing their industrial control systems and operational technology (OT). This limitation is due to a lack of technical data on OT cyber incidents overall, along with an inability to apply traditional actuarial methods to estimate the potential financial consequences of cyber events.
In the era before widespread artificial intelligence and machine learning, security experts had to dig into the data themselves to find correlations. Machine learning (ML) has provided a big boost by identifying anomalies that are rare or abnormal, and artificial intelligence (AI) has taken it even further by applying logic to discern patterns.
There is too much information out there today for humans to manually monitor all the connections between cyber-physical systems, networks, vulnerabilities, and more. AI-based systems are needed to identify and automate data processing from interconnected systems, continually analyzing the data to deliver updates. As machine learning engines ingest massive volumes of data, AI platforms can deliver greater levels of speed and accuracy to assess risk. AI and ML strategies include, among others, vulnerability detection, accelerated processing of complex security data relationships, and the impacts of cyber incidents on a network of interconnected critical infrastructure.