Cyber Security is projected to become a $232 billion global market by 2022. Cyber Security is a rapidly evolving industry, projected to become a $232 billion global market by 2022. This estimated valuation reflects a significant rise from last year, in which the market value reached $137.8 billion worldwide in 2017. The emergence of mobile platforms and cloud-based enterprise apps, coupled with the increased adoption of advanced technologies such as fingerprint identification and biometrics have collectively fueled a notable spike in the space. Although cyber security is attracting greater attention across the globe, the United States stands as the dominant force leading the charge for innovation.

These days, 'machine learning' is a buzzword you can't avoid while reading about pretty much any industry. Its ability to "outthink" humans is touted as a magical ROI booster that can drastically maximize productivity while minimizing resource expenditure. The security industry is no different. With internet-scale attack campaigns overwhelming security teams that struggle to process alerts quickly enough amidst oceans of data, machine learning was supposed to be the silver bullet for any modern cybersecurity problem. However, with great hype often comes great disappointment and we're now experiencing the blowback from a growing number of people who believe it has not at all lived up to expectation.

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings – if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.

RiskIQ, a startup with a new kind of security technology, has raised $30.5 million in a third round of funding. Georgian Partners led the round, with participation from existing investors Summit Partners, Battery Ventures, and MassMutual Ventures. RiskIQ notes that threats outside the firewall are vast and dynamic, so the company provides clients with access to the widest range of security intelligence and applications necessary to understand exposures and how to take action. RiskIQ is one of many companies currently applying machine learning to security. The San Francisco-based company will use this capital to expand its platform, sales, and digital risk applications.