Once upon a time a mobile phone was something we used for talking. Today making a call ranks sixth on the list of most common uses for a mobile phone. Now there’s a new kid on the block that, in time, will push making a call even lower down the list. Mobile payment, or m-payment, is taking off. Early adopters like Starbucks already attribute significant revenue gains to their investment in mobile. Although overall mobile payments adoption and usage rates are still a fraction of standard credit/debit card transactions industry watchers expect this to change very quickly.
For many years industries like oil and gas, electricity, agriculture and utilities have relied on operational communications infrastructure outside the main corporate network to collect data and provide supervisory control. Known as Supervisory Control and Data Acquisition (SCADA) systems the data they collect leads to efficient allocation of resources, monitors safety conditions and improves operational decision-making. But now, with the emergence of Internet of Things (IoT) technology, industrial organizations are eager to deploy new wireless machine-to-machine (M2M) devices to collect even more data from field assets in remote, geographically dispersed locations. The number of sensors and data points in industrial networks looks set to multiply exponentially overnight. As a consequence, there will be more access points than ever before. Security, therefore, will be an important factor in determining the overall success of IoT deployment.
Organizations are being targeted by cybercriminals more than ever. According to the latest statistics from Symantec, 52.4% of phishing attacks in December 2015 were against small and medium-sized enterprises (SMEs). The month prior demonstrated an even bigger spike. The situation is forcing businesses of all sizes to augment their network and mobile security. Topping the list of improvements include the need for better threat intelligence and endpoint security.
Security information and event management (SIEM) systems provide a valuable tool to gather threat intelligence through activities logged from various applications and devices. The logs are then combined to create threat intelligence reports that can identify signs of unauthorized behavior. Because of their complexity, until recently SIEM systems were considered exclusive to those large enterprises with access to the sizeable budgets and resources required to maintain them.
Over the last few years, gleaning useful information from massive amounts of data has also become more difficult for IT security and approaches to Big Data and information analysis are a critical topic in this sector. The number of users, end devices, applications and log files are constantly on the rise. At the same time, attackers are becoming more sophisticated and professional while constantly adapting their strategies. Companies are now facing a completely new level of risks and challenges to their IT security operations.
Frequently companies have more than enough data on security events, including successful penetrations and potential vulnerabilities. Enormous volumes of data are generated by network components, storage systems or applications. Security threats buried among this data must be taken seriously, however attacks often remain unnoticed or they are not discovered in time due to a lack of structured data. Analyzing and interpreting this data and deploying a rapid response is almost impossible without specialist software.