Artificial intelligence is the most powerful technology in generations with the potential to impact U.S. security, welfare and global leadership. U.S. national security agencies must develop and integrate AI-enabled capabilities to compete and defend in the AI era. However, standard methods and AI technologies fall short for the high-consequence and specialized missions of national security. The U.S. Department of Energy's (DOE) National Nuclear Security Administration (NNSA) and National Laboratories are developing the Next-Generation of AI -- innovative methods and technologies designed for national security challenges and operational concepts. National security agencies should leverage NNSA's Next-Generation AI research and development to accelerate AI innovation and enable an AI-ready force.
CHINESE fighter pilots have been going up against aircraft piloted by artificial intelligence that fare "better than humans" and can shoot them down in simulated dogfights. The Air Force has been testing out AI systems that have been "sharpening the sword" for the country's pilots, Chinese media reported. A People's Liberation Army Air Force brigade flight team leader and recognized fighter ace, Fang Guoyu, was recently "shot down" by one of the advanced aircraft. The AI adversary proved triumphant during an air-to-air combat simulation, according to the Chinese military's official newspaper, PLA Daily. Guoyo explained that although it was easy to defeat the AI aircraft in the early stages of training, the AI learned from its human opponent with each battle.
The Chinese PLA Central Theater Command Air Force simulated a dogfight in which a highly experienced pilot was shot down by an artificial intelligence (AI)-driven aircraft. China's state media Global Times cited a report by PLA Daily, Army's official newsletter, on the simulation exercise. It does not mention which aircraft was used in this exercise though. There has been an increasing application of artificial intelligence (AI) and machine learning in military combat training with major powers including the US, China, and Russia joining the race. A mock combat exercise was held in which AI-enabled opponents outperformed many of the PLA Air Force pilots. According to the GT report, China has been investing heavily in AI and machine learning.
Before diving into cybersecurity and how the industry is using AI at this point, let's define the term AI first. Artificial intelligence (AI), as the term is used today, is the overarching concept covering machine learning (supervised, including deep learning, and unsupervised), as well as other algorithmic approaches that are more than just simple statistics. These other algorithms include the fields of natural language processing (NLP), natural language understanding (NLU), reinforcement learning, and knowledge representation. These are the most relevant approaches in cybersecurity. Given this definition, how evolved are cybersecurity products when it comes to using AI and ML?
In this article, we're going to discuss machine learning and artificial intelligence in cybersecurity. We'll look at the benefits and challenges of AI, their role in cybersecurity, and how criminals can abuse this technology. Cyberattacks have been rising in frequency and scale for a few years now. We saw a sharp jump since the start of the notorious pandemic. With data security in more danger than ever, it's no surprise that more and more companies are turning to artificial intelligence in the hope of getting more powerful digital protection from hackers, phishers, and other cyber criminals.
In a 2017 Deloitte survey, only 42% of respondents considered their institutions to be extremely or very effective at managing cybersecurity risk. The pandemic has certainly done nothing to alleviate these concerns. Despite increased IT security investments companies made in 2020 to deal with distributed IT and work-from-home challenges, nearly 80% of senior IT workers and IT security leaders believe their organizations lack sufficient defenses against cyberattacks, according to IDG. Unfortunately, the cybersecurity landscape is poised to become more treacherous with the emergence of AI-powered cyberattacks, which could enable cybercriminals to fly under the radar of conventional, rules-based detection tools. For example, when AI is thrown into the mix, "fake email" could become nearly indistinguishable from trusted contact messages.
The company is a late-stage cybersecurity startup that helps organizations secure their data using AI and machine learning. In an S-1 filing, the security company revealed that for the three months ending April 30, its revenues increased by 108% year-on-year to $37.4 million. Furthermore, its customer base grew to 4,700, up from 2,700 a year prior. However, SentinelOne's net losses were more than double from $26.6 million in 2020 to $62.6 million. We also expect our operating expenses to increase in the future as we continue to invest for our future growth. Including expanding our research and development function to drive further development of our platform.
A new report suggest machine learning could help in the fight against cyberattacks, but cautions that AI is far from a panacea. Why it matters: Attacks, including ransomware, have been on the rise across a variety of industries and institutions. Several factors have led to the increase in attacks, including the digitization of more of the economy, the growing role of cyber attacks as part of international politics and a lack of security experts, according to the report from the Center for Security and Emerging Technology. "Machine learning can help defenders more accurately detect and triage potential attacks," CSET said in its report. "However, in many cases these technologies are elaborations on long-standing methods -- not fundamentally new approaches --that bring new attack surfaces of their own."
"Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs). In the GAN process, two ML systems called neural networks are trained in competition with each other. The first network, or the generator, is tasked with creating counterfeit data--such as photos, audio recordings, or video footage--that replicate the properties of the original data set.