This post is co-authored by Lezgin Bakircioglu, Innovation and Security Manager at Daniel Wellington. In their own words, "Daniel Wellington (DW) is a Swedish fashion brand founded in 2011. Since its inception, it has sold over 11 million watches and established itself as one of the fastest-growing and most coveted brands in the industry." In this post, we share how DW saved 99% on translation costs with Amazon Translate and other AWS services. At DW, having the ability to respond to customers in their local language is critical to the customer journey.

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.

AT the heart of technological changes over time are core things like processing power, developments in network infrastructure, falls in price and re-imaginings of the user interface. But the next 5 years will deliver on some building trends that are going to need to be carefully managed. Artificial intelligence is one of these trends. It's been coming for decades but with many countries, including Australia, taking different layers of citizen ID biometric, and most manufacturers starting to deliver on past ROI promises, it's clear that AI is going to be central to the future of our systems. It will make them more efficient, more powerful and more frightening. The paradox of AI is that it will need to be kept on a short leash to achieve its full potential – whether we're already past that point of control remains to be seen.

Is machine learning a must-have for security analytics or is it window dressing that is irrelevant to a security manager's purchasing decision? The answer, much like the outputs derived through machine learning algorithms, is neither black nor white. The promise of machine learning in cybersecurity lies in its ability to detect as-yet-unknown threats, particularly those that may lurk in networks for long periods of time seeking their ultimate goals. Machine learning technology does this by distinguishing atypical from typical behavior, while noting and correlating a great number of simultaneous events and data points. But in order to know what constitutes typical activity on a website, endpoint or network at any given time, the machine learning algorithms must be trained on large volumes of data that have already been properly labelled, identified or categorized with distinguishing features that can be assigned and reassigned relative weights.