In July 2019, UK Information Commissioner’s Office (ICO) announced its intention to fine two companies for violating the European Union’s General Data Protection Regulation (GDPR). ICO began by disclosing its intention to penalize British Airways in the amount of £183 million (approximately $224 million) on 8 July.
The FBI has arrested a 33-year-old software engineer in Seattle as part of an investigation into a massive data breach at financial services company Capital One. Paige A. Thompson, also known by the online handle “erratic,” has been charged with one count of computer fraud and abuse, after an investigation uncovered that a hacker had broken into cloud servers run by Capital One and stole data related to over 100 million credit-card applications.
Data drives the life sciences. Data supports the development of new products and enables agile decision making. But for a field so completely reliant on data, the industry is struggling to find methods to adequately handle that data. Ideally, there would be centralized repositories where data is accessible, safe, and organized regardless of the format or size. Instead, there are numerous data silos spread among the different contributors to a specific project.
If you’re reading this, you likely know what a log is, and what a metric is. But sometimes there are questions on their differences, whether you really need both, and if you should use dedicated solutions to manage each type. The answers? Yes, you need both; yes, they should be unified. Logs and metrics, aka machine data, are complementary.
Data breaches happen daily, many of which go undetected for months and even years. In this environment, having visibility into assets across the enterprise is paramount. This critical security need is termed “enterprise visibility” and has become a household name across the industry. The concept can take on a variety of meanings depending on the stakeholder you may be dealing with across the enterprise.
Across the board, security teams of every industry, organization size, and maturity level share at least one goal: they need to manage risk. Managing risk is not the same as solving the problem of cybersecurity once and for all, because there is simply no way to solve the problem once and for all. Attackers are constantly adapting, developing new and advanced attacks, and discovering new vulnerabilities.
SIEM security that is equipped with Artificial Intelligence (AI) and user behavior analytics can deal with internal threats. AI capabilities in SIEM help security professionals to automate tasks that are otherwise manual and repetitive. Doing so can also help to swiftly detect threats and suspicious activities in network traffic and event logs.
