CrowdStrike data scientists often explore novel approaches for creating machine learning pipelines especially when processing a large volume of data. The CrowdStrike Security Cloud stores more than 15 petabytes of data in the cloud and gathers data from trillions of security events per day, using it to secure millions of endpoints, cloud workloads and containers around the globe with the power of machine learning and indicators of attack.

The increase in attack sophistication coupled with the decline of skilled security staff continues to put pressure on organizations and their teams by minimizing their ability to effectively see and control risks within the enterprise. This is only made more difficult as teams find themselves patching together disparate solutions, resulting in labyrinthian security stacks and a heavily siloed environment.

Cloud-based services are augmenting business operations and being adopted at a record pace. In fact, ​Gartner® estimates “more than 85% of organizations will embrace a cloud-first principle by 2025 and will not be able to fully execute on their digital strategies without the use of cloud-native architectures and technologies.”

The CrowdStrike eBook, “Protectors of the Cloud: Combating the Rise in Threats to Cloud Environments,” reveals how adversaries target and infiltrate cloud environments and recommends best practices for defense. As organizations move critical applications and data to the cloud, these resources have come under increasing attack.

Today CrowdStrike sent the following Tech Alert to our customers: On July 8, 2022, CrowdStrike Intelligence identified a callback phishing campaign impersonating prominent cybersecurity companies, including CrowdStrike. The phishing email implies the recipient’s company has been breached and insists the victim call the included phone number.

At CrowdStrike, we combine cloud scale with machine learning expertise to improve the efficacy of our machine learning models. One method for achieving that involves scanning massive numbers of files that we may not even have in our sample collections before we release our machine learning models. This prerelease scan allows us to maximize the efficacy of our machine learning models while minimizing negative impact of new or updated model releases.