Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Analytics

The Democratization of Data: The Pros & Cons of All That Data

Try going one day without navigating today’s data landscape — accepting or declining cookie pop-ups, determining whether and how a company can use your information, and all the data you’re generating simply by browsing the web. Yes, we live in the Data Age. We know we generate mind-boggling amounts of data. The data we generate in a single day is an unfathomable amount (2.5 quintillion bytes if you can do that math). More formally, we say that data has been democratized.

A Deep Dive into Custom Spark Transformers for Machine Learning Pipelines

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.

Building Your Security Analytics Use Cases

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.

Cybersecurity Risk Management: Introduction to Security Analytics

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.

Using KPIs to generate results in Cybersecurity

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.

Adopt user analytics to accelerate security investigations

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.

Migrating Reports to Advanced Analytics

Learn how to import existing reports into Advanced Analytics. Netskope, the SASE leader, safely and quickly connects users directly to the internet, any application, and their infrastructure from any device, on or off the network. With CASB, SWG, and ZTNA built natively in a single platform, Netskope is fast everywhere, data-centric, and cloud smart, all while enabling good digital citizenship and providing a lower total-cost-of-ownership.

Introduction to Advanced Analytics (Part 2)

Start building dashboards and widgets using the tools in Explore - Part 2 Netskope, the SASE leader, safely and quickly connects users directly to the internet, any application, and their infrastructure from any device, on or off the network. With CASB, SWG, and ZTNA built natively in a single platform, Netskope is fast everywhere, data-centric, and cloud smart, all while enabling good digital citizenship and providing a lower total-cost-of-ownership.
Sponsored Post

Using Predictive Analytics Capability to Resolve Critical Incidents

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.