Cybereason, a Tel Aviv-and Boston, Massachusetts-based cybersecurity company providing endpoint prevention, detection, and response, has secured a $50 million investment from Google Cloud, VentureBeat has learned. It extends the series F round that Cybereason announced in July from $275 million to $325 million, making Cybereason one of the best-funded startups in the cybersecurity industry with over $713 million in the capital.

We reached out to a Google Cloud spokesperson, but they didn’t respond by press time.

The infusion of cash comes after Cybereason and Google Cloud entered into a strategic partnership to bring to market a platform — Cybereason XDR, powered by Chronicle — that can ingest and analyze “petabyte-scale” telemetry from endpoints, networks, containers, apps, profiles, and cloud infrastructure. Combining technology from Cybereason, Google Cloud, and Chronicle, the platform scans more than 23 trillion security-related events per week and applies AI to help reveal, mitigate, and predict cyberattacks correlated across devices, users, apps, and cloud deployments.

There are 40.3 million victims of human trafficking globally, according to the International Labor Organization. Marinus Analytics, a startup based in Pittsburgh, Pennsylvania, hopes to make a dent in that number. The company’s mission is to “serve those working on the frontlines of public safety by developing technology for them to disrupt human trafficking, child abuse, and cyber fraud.” For its achievements, Marinus won $500,000 as part of its third-place ranking in the 2021 IBM Watson AI XPRIZE competition. The startup is the brainchild of three co-founders: Cara Jones, Emily Kennedy, and Artur Dubrawski, who launched it out of the Robotics Institute at Carnegie Mellon University in 2014.

Marinus implements its mission primarily through its set of AI-based tools called Traffic Jam, whose goal is “to find missing persons, stop human trafficking and fight organized crime.”

Traditionally, finding a missing person would involve taping a picture of the person on the computer and then manually combing through thousands, if not millions, of online ads on adult services websites to see if any of the posted pictures match. Such a process is time-consuming and tiring. A human detective’s attention can start flagging after long hours at the computer doing the same task endlessly.

Autonomous machine expected to pick more than 25,000 raspberries a day, outpacing human workers.

Cloud-based content management provider Box has announced a new “deep scan” functionality that checks files as they are uploaded to identify sophisticated malware and avert attacks.

The new capabilities constitute part of Box Shield, which uses machine learning to prevent data leaks, detect threats, and spot any kind of abnormal behavior. In April of last year, Box added a slew of automated malware detection features to the mix, allowing Box Shield customers to spot malicious content that may already have been uploaded to a Box account. However, so far this has leaned heavily on “known” threats from external intelligence databases. Moving forward, Box said it will mesh deep learning technology with external threat intelligence capabilities to analyze files for malicious scripts, macros, and executables to protect companies from zero-day (unknown) vulnerabilities.

When a user uploads an infected file, Box will quarantine it for inspection but will still allow the user to view a preview of the file and continue working.