The role of the chief information officer will change significantly in the next few years, driven by the growing adoption of artificial intelligence and by demands from cybersecurity. IT leaders are rapidly scaling their digital businesses, making the remainder of this year and 2018 a defining moment for CIOs that don't want to be left behind, according to research firm Gartner Inc.
Discussions around AI cyber defence have traditionally focused on the ability of advanced machine learning to detect the earliest signs of an unfolding attack, including sophisticated, never-seen-before threats. This real-time threat detection overcomes the shortcomings of legacy tools and cuts through the noise in live, complex networks to accurately identify threatening anomalies, including'unknown unknowns'. But while the capability to identify the entire spectrum of threats in their nascent stages before a problem becomes a crisis is incredibly powerful in its own right, it also serves as a fundamental enabler for autonomous response measures, which truly deliver on the promise of artificial intelligence in cyber defense. Before the advent of AI cyber defense, the principal obstacle to achieving autonomous response was determining the exact action that is needed to stop an infection from spreading, while keeping the business operational. By their very nature and definition, traditional approaches to cyber security cannot make the jump from detection to response.
Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.
Artificial Intelligence (AI) presents a significant opportunity to solve problems previously either not easy to solve or worse, not possible to solve. The combination of AI along with today's Graphics Processing Unit (GPU) technology provides an added boost to those leveraging sophisticated algorithms in their deep learning solutions. These sophisticated systems are able to train deep learning models and ultimately lead to predictive insights. The objective is to move from reactive to proactive and finally to predictive insights. The breadth of opportunities that AI presents is wide, however, a significant opportunity is in the Cybersecurity space.
The AI systems will be able to learn over time through analysing how human lawyers complete the tasks and will ultimately be able to process the cases much faster – freeing up time for lawyers to focus on the more complex and cognitive parts of the case. One of the first steps for a business looking to integrate AI into their workforce is to identify processes that would benefit from integrating with the technology – there's no use bringing AI systems into the office if they're not going to help anyone. Business leaders then need to explore all of the options available for applying a pre-built learning system to handle those identified tasks, and reap the improvements in scalability and throughput that AI enables. Now is the time for businesses to start investigating and experimenting with AI, to reap the scalability and efficiency benefits and stay ahead of the competition.
In this special guest feature, Kumar Saurabh, CEO and co-founder of Logichub, observes how the correlation between data volumes and IT security seems straightforward, but in reality it's complex and at times paradoxical. He provides 7 surprising facts about big data and artificial intelligence (AI) as they are used in cybersecurity. He has a passion for helping organizations improve the efficacy of their security operations, and personally witnessed the limitations of existing solutions in helping SOC analysts detect threats buried deep within mountains of alerts and events. Here are 7 surprising facts about big data and artificial intelligence (AI) as they are used in cybersecurity.
In the last 12 months, 60% of Australian organisations experienced a ransomware attack. A significant challenge for businesses is that legacy antivirus technology is too slow to stop cyber-attacks in time. Throughout the last year, 24 percent of Australian organisations experienced a ransomware incident on at least a monthly basis and it took five hours or more to recover. As we reflect on the way organisations around the world have been impacted by breaches this year, it's clear that traditional approaches to security have failed.
North Korea had plans to direct a cyber attack against power grids in the United States and successfully launched an attack directed at South Korea's Ministry of Defense, NBC News reported. While the campaign may have failed, the attempts of North Korean hackers to target utility companies presents a growing risk for American companies that are responsible for keeping the lights on for millions of homes across the country. Many power grids operate on a network separate from the public internet, insulating the systems that control the grid from attackers. North Korean hackers were able to successfully infiltrate South Korea's defense ministry and stole a large collection of military documents that purport to detail wartime contingency plans developed by South Korea and the U.S. A total of 235 gigabytes of military documents were reported to be stolen from South Korea's Defense Integrated Data Centre in a breach that took place in September 2016, and 80 percent of those stolen files have yet to be identified.
CloudAI uses artificial intelligence to detect advanced threats that employ unknown attacks and unknown methods and provide security teams immediate visibility into emerging and active user-based threats. CloudAI's machine learning-driven approach automates the detection of advanced threats through self-evolving, cloud-based analytics. This approach enables security personnel to efficiently and effectively defeat the rapidly growing volume of threats targeting their organisations daily -- even as attackers rapidly modify their methods and enterprise attack surfaces continue to expand. Ultimately, CloudAI's high-accuracy threat detection is designed to reduce false positives and associated alarm fatigue, enabling security personnel to focus on prioritised risks and drive greater efficiency in the security operations center (SOC).
Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S.