Analytics

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.

It’s time again for another meeting with senior leadership. You know that they will ask you the hard questions, like “how do you know that your detection and response times are ‘good enough’?” You think you’re doing a good job securing the organization. You haven’t had a security incident yet. At the same time, you also know that you have no way to prove your approach to security is working. You’re reading your threat intelligence feeds.

It’s mid-morning. You’re scanning the daily news while enjoying a coffee break. You come across yet another headline broadcasting a supply chain data breach. Your heart skips a quick, almost undetectable, beat. You have the technology in the headline in your stack. You set aside your coffee and begin furiously scanning through the overwhelming number of alerts triggered across all your technologies.

Gaining investment from business leaders to create a mature cybersecurity program and fund initiatives is an imperative for success in enterprise risk mitigation. All too often, security and IT organizations struggle to capture the attention of executives needed to advance their priorities and build even basic cybersecurity capabilities.

Machine data analytics is the process of parsing data generated by software from a wide variety of sources including servers, networks, applications and financial records. These, and many other similar sources, produce massive amounts of data including from local operating systems, identity/access management tools, cloud consoles and their associated log files, alerts, scripts and profiles.

CloudFabrix solution provides a holistic approach for enterprises to implement proactive operations with the objective of eliminating/reducing critical incidents and improving customer satisfaction. The solution primarily relies on applying regression/forecasting models on any time-series data to detect and forecast anomalies. One of the unique features of the solution is the ability to convert unstructured data such as logs/incidents/alerts into time-series data to be used for running prediction models.

Operating an effective SOC requires overcoming a wide range of challenges. Often, security teams have too many disparate tools to manage, too many alerts to make sense of, and too many data sources that prevent the team from achieving full visibility. All these hurdles can make it difficult for your SOC analysts to identify and quickly respond to suspicious behavior and indicators of compromise.