Energy companies leverage AI and ML to strengthen cyber defenses
Fortunately for energy and utility companies, automation offers several new and innovative ways to better protect assets in a changing threat landscape. forbes predicts that artificial intelligence and machine learning will play a key role in both cyberattacks and cybersecurity by 2024.
“It is expected that cybercriminals will use Automate and enhance capabilities with AI and ML, attacks become more sophisticated and adaptive. “Cybersecurity professionals must harness the power of AI themselves to stay one step ahead of evolving threats,” the article states.
“Rapid advances in AI will both: Opportunities and challenges in cybersecurity; The same tools that provide advanced capabilities to attackers can also help with cyber defense. Effectively applying AI to cybersecurity and ensuring it addresses specific issues within the technology stack requires a dedicated approach. ”
According to a recent blog post: Nvidiathe Department of Energy has already done some things. Interesting use cases for AI. “In one project, the department used AI to Automate and optimize security vulnerability and patch management In energy supply systems.Another project of Artificial diversity and defense security Increase situational awareness of energy supply systems using software-defined networks to ensure uninterrupted energy flow. ”
The industry is likely to see more AI use cases emerge over time. “To respond to the evolving threat landscape and ensure physical, energy, and data security, public agencies will continue to integrate AI to achieve a dynamic, proactive, and pervasive cyber defense posture. ”he wrote NVIDIA.
Discover: Learn how to improve your ransomware resiliency.
Data analytics brings multiple benefits to the E&U sector
According to , beyond cybersecurity, AI and ML are poised to become powerful tools for the industry. Recent articles from the International Energy Agency.
AI and ML are “uniquely positioned to support the simultaneous growth of smart grids and the vast amounts of data they generate,” the agency wrote. “Smart meters generate and transmit thousands of times more data points to the utility company than previous analog meters. New Device for monitoring grid power flow It funnels orders of magnitude more data to carriers than the technology it replaces. And wind turbines around the world 400 billion data points per year. This volume is a key reason why energy companies see AI as an increasingly important resource. ”
The IEA predicts that “the potential applications of AI across power systems are likely to proliferate in the coming years.”
The agency notes that in addition to improving energy supply and demand forecasting, AI can be used to improve predictive maintenance of physical assets, manage and control the power grid, facilitate demand response, and improve or enhance energy use through the use of “AI or machine learning processes.” It can also be used to provide consumer services. Improve your customers’ billing experience with apps and online chatbots. ”
Related: Cisco supports sustainable buildings and helps customers reduce energy costs.
Industry will rely on technology to meet renewable energy demand in 2024
As 2023 draws to a close, White House publishes blog Call for further development of renewable energy efforts to support policies sought by President Biden Investing in America Agenda.
In its 2024 Renewable Energy Industry Outlook, Deloitte says, “The influx of federal investment in clean energy and the cascading momentum of public and private decarbonization demand are stronger than ever. Looking ahead to 2024. , these forces could help renewable energy overcome the hurdles posed by tectonic shifts needed to meet the nation’s climate goals.”
The IEA has highlighted AI as a key tool to address demand. “One of the most common uses of AI in the energy sector is to improve demand and supply forecasting. It is important. Next generation power system.But this can get complicated renewable technology, because the sun doesn’t always shine and the wind doesn’t always blow. This is where machine learning can play a role. It helps adjust fluctuating supply to increases and decreases in demand. ”