From insider threats to abuse of privileges and management to hackers, humans are important and diverse carriers of cyber risks. Therefore, Machine Learning help detect changes in the way users interact in the IT environment and describe their behavioral characteristics in the attack environment. Despite high marketing requirements, the reality is that the corporate security environment is a huge and dynamic network. And managers must constantly monitor, audit, and update based on continuous, unpredictable, internal, and external threat vectors. ML introduces various enhancements in the ability to detect, investigate, and respond to threats. But it is a combination of personnel and technology that can manage a full range of threats in the ever-evolving security environment.

While we continue to wait for news about the Mars copter's first test flight, Elon Musk and SpaceX closed out the week with a big win, scoring a contract from NASA to use Starship as a lander for the Artemis lunar program. The company beat out Blue Origin (which teamed up with key aerospace players like Lockheed Martin) and defense contractor Dynetics to secure the $2.9 billion contract. There are still funding hurdles for NASA to clear if it plans to fly as scheduled, but those missions are still years away at best. In the nearer future, Apple's Spring Loaded event is scheduled to take place on Tuesday and Chris Velazco has reminders of the rumors you should know about before it starts. New iPads and iMacs seem like safe bets, but we'll see if there are any big surprises in a few days.

Europe is already the world's tech privacy cop. Now it might become the AI cop too. Companies using artificial intelligence in the EU could soon be required to get audited first, under new rules set to be proposed by the European Union as soon as next week. The regulations were partly sketched out in an EU white paper last year and aim to ensure the responsible application of AI in high-stakes situations like autonomous driving, remote surgery or predictive policing. Officials want to ensure that such systems are trained on privacy-protecting and diverse data sets.

Poppy Gustafsson runs a cutting-edge and gender-diverse cybersecurity firm on the brink of a £3bn stock market debut, but she is happy to reference pop culture classic the Terminator to help describe what Darktrace actually does. Launched in Cambridge eight years ago by an unlikely alliance of mathematicians, former spies from GCHQ and the US and artificial intelligence (AI) experts, Darktrace provides protection, enabling businesses to stay one step ahead of increasingly smarter and dangerous hackers and viruses. Marketing its products as the digital equivalent of the human body's ability to fight illness, Darktrace's AI-security works as an "enterprise immune system", can "self-learn and self-heal" and has an "autonomous response capability" to tackle threats without instruction as they are detected. "It really does feel like we're in this new era of cybersecurity," says Gustafsson, the chief executive of Darktrace. "The arms race will absolutely continue, I really don't think it's very long until this [AI] innovation gets into the hands of attackers, and we will see these very highly targeted and specific attacks that humans won't necessarily be able to spot and defend themselves from. "It's not going to be these futuristic Terminator-style robots out shooting each other, it's going to be all these little pieces of code fighting in the background of our businesses.

The European Commission is about to release an important policy package, which will include a proposal for a "Regulation on a European Approach for Artificial intelligence". This will be the first attempt to define a comprehensive regulatory framework for AI, dealing with essential aspects such as the definition of high-risk applications, regulatory obligations for providers of AI systems, the post-market surveillance of AI, the conformity assessment of high-risk AI applications and the possible creation of a new AI Board. This will be a very ambitious proposal, which has been expected for several months also outside the EU, where several countries are considering regulating specific uses of AI. CEPS has therefore decided to invite the Director for AI and Digital Industry at the European Commission DG CONNECT, Lucilla Sioli, for an informal debate followed by a round of first impressions on the content of the proposal. AGENDA: 17.00 Welcome and introductory remarks – Andrea Renda, CEPS and EUI

Today, the U.S. Food and Drug Administration authorized marketing of the GI Genius, the first device that uses artificial intelligence (AI) based on machine learning to assist clinicians in detecting lesions (such as polyps or suspected tumors) in the colon in real time during a colonoscopy. "Artificial intelligence has the potential to transform health care to better assist health care providers and improve patient care. When AI is combined with traditional screenings or surveillance methods, it could help find problems early on, when they may be easier to treat," said Courtney H. Lias, Ph.D. acting director of the GastroRenal, ObGyn, General Hospital and Urology Devices Office in the FDA's Center for Devices and Radiological Health. "Studies show that during colorectal cancer screenings, missed lesions can be a problem even for well-trained clinicians. With the FDA's authorization of this device today, clinicians now have a tool that could help improve their ability to detect gastrointestinal lesions they may have missed otherwise."

Management forecast predicts the market will achieve an 8.3% Compound Annual Growth Rate (CAGR) growth rate from 2019 through 2024, reaching $211.4 billion. Bottom Line: In 2021, cybersecurity vendors will accelerate AI and machine learning app development to combine human and machine insights so they can out-innovate attackers intent on escalating an AI-based arms race. Attackers and cybercriminals capitalized on the chaotic year by attempting to breach a record number of enterprise systems in e-commerce, financial services, healthcare and many other industries. AI and machine learning-based cybersecurity apps and platforms combined with human expertise and insights make it more challenging for attackers to succeed in their efforts. Accustomed to endpoint security systems that rely on passwords alone, admin accounts that don't have fundamental security in place, including Multi-Factor Authentication (MFA) and more and attackers created a digital pandemic this year. Interested in what the leading cybersecurity experts are thinking will happen in 2021, I contacted twenty of them who are actively researching how AI can improve cybersecurity next year. Leading experts in the field include including Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, BJ Jenkins, President and CEO of Barracuda Networks, Ali Siddiqui, Chief Product Officer and Ram Chakravarti, Chief Technology Officer, both from BMC, Dr. Torsten George, Cybersecurity Evangelist at Centrify, Tej Redkar, Chief Product Officer at LogicMonitor, Bill Harrod, Vice President of Public Sector at Ivanti, Dr. Mike Lloyd, CTO at RedSeal and many others.

US President Joe Biden should not heed the advice of the National Security Commission on Artificial Intelligence (NSCAI) to reject calls for a global ban on autonomous weapons. Instead, Biden should work on an innovative approach to prevent humanity from relinquishing its judgment to algorithms during war. The NSCAI maintains that a global treaty that prohibits the development, deployment and use of artificial intelligence (AI) enabled weapons systems is not in the interests of the United States and would harm international security. It argues that Russia and China are unlikely to follow such a treaty. A global ban, it argues, would increase pressure on law-abiding nations and would enable others to utilise AI military systems in an unsafe and unethical manner.

The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports is very important to effectively identify and quickly respond to cyber threats. However, this knowledge is often embedded in large amounts of text, and therefore difficult to use effectively. To address this challenge, we propose a novel approach and tool called EXTRACTOR that allows precise automatic extraction of concise attack behaviors from CTI reports. EXTRACTOR makes no strong assumptions about the text and is capable of extracting attack behaviors as provenance graphs from unstructured text. We evaluate EXTRACTOR using real-world incident reports from various sources as well as reports of DARPA adversarial engagements that involve several attack campaigns on various OS platforms of Windows, Linux, and FreeBSD. Our evaluation results show that EXTRACTOR can extract concise provenance graphs from CTI reports and show that these graphs can successfully be used by cyber-analytics tools in threat-hunting.

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.