The odds are against today's defenders Today the odds remain stacked against cybersecurity professionals. Too often, they fight an asymmetric battle against prolific, relentless and sophisticated attackers. To protect their organizations, defenders must respond to threats that are often hidden among noise. Compounding this challenge is a global shortage of skilled security professionals, leading to an estimated 3.4 million openings in the field. The volume and velocity of attacks requires us to continually create new technologies that can tip the scales in favor of defenders.

Outlines how augmented analytics changes the way security, operations and risk professionals navigate or prevent potential risks before they happen. Seerist Inc., the leading augmented analytics solution for threat and security professionals, today announced the availability of its white paper, Turning Infinite Data into Insightful Threat and Risk Strategies. This white paper was written to demonstrate how leaders can better leverage global data to make more informed, strategic decisions by combining the power of machine learning, human analysis, and natural language capabilities. "Data continues to grow at an accelerated rate every year with 89 percent of big data created in the last two years. It is simply impossible for humans to adequately access and evaluate the vast quantum of information available, yet the value it provides can be life changing and should not be ignored," said Jim Brooks, Seerist's CEO.

Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown. In this paper, we study the speed-robust scheduling problem where the speeds of the machines, instead of the processing times of the jobs, are unknown and augment this problem with predictions. Our main result is an algorithm that achieves a $\min\{\eta^2(1+\alpha), (2 + 2/\alpha)\}$ approximation, for any $\alpha \in (0,1)$, where $\eta \geq 1$ is the prediction error. When the predictions are accurate, this approximation outperforms the best known approximation for speed-robust scheduling without predictions of $2-1/m$, where $m$ is the number of machines, while simultaneously maintaining a worst-case approximation of $2 + 2/\alpha$ even when the predictions are arbitrarily wrong. In addition, we obtain improved approximations for three special cases: equal job sizes, infinitesimal job sizes, and binary machine speeds. We also complement our algorithmic results with lower bounds. Finally, we empirically evaluate our algorithm against existing algorithms for speed-robust scheduling.

Today, cybersecurity is in a state of continuous growth and improvement. In this on-demand webinar, learn how two organizations use a continuous AI feedback loop to identify vulnerabilities, harden defenses and improve the outcomes of their cybersecurity programs. The security risk landscape is in tremendous flux, and the traditional on-premises approach to cybersecurity is no longer enough. Remote work has become the norm, and outside the office walls, employees are letting down their personal security defenses. Cyber risks introduced by the supply chain via third parties are still a major vulnerability, so organizations need to think about not only their defenses but those of their suppliers to protect their priority assets and information from infiltration and exploitation.

Granted, much of this won't get done until mid-century or after many of us retire. Still, those just entering the workforce will likely see these automated advancements as their way of life and regular retraining as a significant part of their future careers. These efforts will lower costs, increase productivity, and make the related firms far more competitive if these advancements are done right. Done wrong, they'll put companies under at machine speeds, so make sure you have the right partner, a partner that understands both the technology and your operations. Developing the internal skills to understand the technology and avoiding being played by a vendor become far more critical, given the ability for a poorly trained or implemented AI to do massive amounts of damage at machine speeds.

Three decades after a US university student called Robert Tappan Morris was convicted of launching the first widely known malware attack on the internet, cybercrime has become big business, costing the global economy an estimated £2.1m a minute. Internet service provider Beaming reports that cybercriminals are launching increasingly sophisticated attacks on an "unprecedented scale". The pandemic has exacerbated the situation because it has prompted a sharp rise in remote working, which has enabled them to target vulnerabilities in domestic internet connections to attack corporate systems. In 2020, the average UK business faced 686,961 attempts to breach its systems – 20% up on the previous year's figure – according to Beaming. That equates to an attack every 46 seconds.

The cybersecurity skills shortage continues to plague organizations across regions, markets and sectors, and the government sector is no exception. According to (ISC)2, there are only enough cybersecurity pros to fill about 60% of the jobs that are currently open -- which means the workforce will need to grow by roughly 145% to just meet the current global demand. The Government Accountability Office states that the federal government needs a qualified, well-trained cybersecurity workforce to protect vital IT systems, and one senior cybersecurity official at the Department of Homeland Security has described the talent gap as a national security issue. The scarcity of such workers is one reason why securing federal systems is on GAO's High Risk list. Given this situation, chief information security officers who are looking for ways to make their existing resources more effective can make great use of automation and artificial intelligence to supplement and enhance their workforce.

Darktrace, the world's leading cyber AI company, has today announced the launch of the Cyber AI Analyst, a new technology that emulates human thought processes to continuously investigate cyber-threats at machine speed. With the power to transform the security industry, early adopters of this technology reported a 92% reduction in the time required to investigate threats and provide conclusions to executives. This ground-breaking innovation is the culmination of over three years of research at the Darktrace R&D Center in Cambridge, UK. Using various forms of machine learning, including unsupervised, supervised, and deep learning, the technology learned human intuition and trade craft from more than 100 world-class cyber analysts across thousands of customer deployments. Typically, a human analyst will spend anywhere between half an hour and half a day investigating a single suspicious security incident.

This is Part 2 of a 3-part series. Security teams today are under-staffed, over-worked, under-funded and struggling to stay abreast of the ever-changing threat landscape. Many security analysts work long hours poring over millions of security events to protect systems and fix vulnerabilities. Simply put, there is too much information and not enough analysts. Fortunately, humans are not the only answer for solving the cybersecurity crisis.

Pedestrians walk under a surveillance camera, which is part of a facial recognition technology test in Berlin, Germany. Machine learning shines at tasks like this because it can recognize patterns and predict threats in massive data sets, all at machine speed. There is a temptation to hail major advances in technology as cure-alls for the challenges facing organizations and society today. The fanfare usually ends in disappointment, as the latest superhero technology doesn't live up to its expectations. Not surprisingly, machine learning, a domain within the broader field of artificial intelligence, has been hailed as the current be-all end-all answer in cybersecurity. As a result, it is currently at the peak of inflated expectations in Gartner's most recent Hype Cycle for Emerging Technologies.