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Operators are increasingly using existing offshore infrastructure for asset life extension, and developing new marginal stranded fields rather than develop new large greenfields. Subsea processing is an enabling technology in this goal. A cybersecurity director outlines the steps needed to adopt a risk-based cybersecurity program. He cautions that in many cases, process control systems’ confidentiality is mistakenly viewed as a lower priority than IT systems’. AUVs aren’t limited to inspections and pipeline surveys.
Intel and Cyberhawk released a case study outlining the successful inspection of a gas terminal near the coast of Scotland using commercially available drone technology. One of the defining features of the 21st century will undoubtedly be the changing relationship between humans and automated machines. In June, the United States Federal Aviation Administration (FAA) issued the first approval for the overland use of unmanned aerial vehicles (UAVs) in Alaska. Unmanned aircraft are finding their place in the oil and gas industry by providing aerial geologic modeling to address reservoir-related challenges and making inspections safer.
Operators need to take steps to protect their facilities from drone security breaches by outsiders. The costs an attacker incurs in developing tools to break into and control infrastructure is low compared to the costs an operator incurs in defending against those tools. More than 45% of energy companies fell victim to at least one cyberattack in 2014, a higher percentage than in any other corporate sector. With constant hacking threats, companies must develop strong cybersecurity strategies.
Ever since the September 2017 Equifax data breach that exposed the personal information of 147 million Americans, and the many other high-profile data breaches that have happened since, data security and data privacy have become pressing boardroom-level concerns. "The Equifax debacle is where a lot of the inherent [cybersecurity] issues really surfaced to the business level," said Aaron Shum, practice lead for security, privacy, risk, and compliance at Info-Tech Research Group. "It's where we discovered the level of incompetence that can exist in an organization." According to the 2019 Edelman Trust Barometer Special Report: In Brands We Trust?, 81% of consumers said that brand trustworthiness plays a major role in their buying decisions. In other words, data breaches today not only represent a bottom-line risk in the form of penalties but they also jeopardize an organization's brand and reputation, directly affecting its ability to attract new customers and retain existing ones.
In January 2017, a group of artificial-intelligence researchers gathered at the Asilomar Conference Grounds in California and developed 23 principles for artificial intelligence (AI), which was later dubbed the Asilomar AI Principles. The sixth principle states that “AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.” Thousands of people in both academia and the private sector have since signed on to these principles, but, more than 3 years after the Asilomar conference, many questions remain about what it means to make AI systems safe and secure. Verifying these features in the context of a rapidly developing field and highly complicated deployments in health care, financial trading, transportation, and translation, among others, complicates this endeavor. Much of the discussion to date has centered on how beneficial machine learning algorithms may be for identifying and defending against computer-based vulnerabilities and threats by automating the detection of and response to attempted attacks.