Physical security is the protection of people, property, and physical assets from actions and events that could cause damage or loss. Though often overlooked in favor of cybersecurity, physical security is equally important. And, indeed, it has grown into a $30 billion industry. All the firewalls in the world can't help you if an attacker removes your storage media from the storage room. The growing sophistication of physical security through technologies such as artificial intelligence (AI) and the internet of things (IoT) means IT and physical security are becoming more closely connected, and as a result security teams need to be working together to secure both the physical and digital assets.

We've all seen phishing attacks. We've actually not seen a large fraction of them, because spam filters have gotten good enough to weed out the majority. A quick glance at your junk mail folder will find a bunch of them. You'll find a range of Nigerian Princes, Lottery awards, alerts about the World of Warcraft account you don't have, and a bunch more. Those all use the classic "shotgun" or "driftnet" technique, where the scammer – or just a spammer – sends out a few million copies of the same email hoping someone will bite.

If an employee who recently gave two weeks' notice starts downloading large numbers of files from the company network and copying them to a thumb drive, it is entirely possible that he or she has no malicious intent. The employee could be saving innocuous files related to their employment record or examples of marketing pieces they created. However, in a small number of cases, the employee could be attempting to take confidential product designs, sensitive legal information, private employee data or trade secrets with them to a rival company. It can be difficult for a company to even spot these "insider risks," much less distinguish between routine behavior and the outlier that could destroy a company's competitive advantage or reputation. That's why Microsoft is offering a new Insider Risk Management solution within Microsoft 365 that uses machine learning to intelligently detect potentially risky behavior within a company.

Crypto-ML released R4 of our cryptocurrency machine learning models, which feature a breakthrough in anomaly detection. It is expected to provide highly reliable trade signals to Crypto-ML members. This post will cover a brief of the model and how to interpret the corresponding signal and graph on our Member Dashboard. There are many approaches to machine learning. While our models R1 through R3 have achieved an exceptional Trade History, our machine learning team has been able to develop an entirely new approach that seeks to identify unusual behavior in the markets that tends to precede price changes.

One major advantage of AI algorithms is their ability to rapidly find patterns across large data sets and to detect anomalies. At the simplest form, this involves creating a baseline model of what's normal in an environment, and then flagging and investigating anomalies to that baseline, which could potentially be IoT threats. This can be seen in various environments, from ones as small as an individual residence up to the largest businesses. For example, I have an increasing number of IoT devices in my home. My lightbulbs should only be able to talk to the lightbulb controller, and the controller should then talk to the app I use to control the lights.