Cribl Search is reshaping the data search paradigm, empowering users to uncover and analyze data directly from its source. Cribl Search can easily reach out and query data already collected in Amazon S3 (or S3 compatible), Amazon Security Lake, Azure Blob, Google Cloud Storage, and more. By searching data where it lives, you can dramatically speed up your search process by avoiding the need to move data before analyzing it.
I have worked as a helpdesk specialist, cyber security analyst, information systems security engineer, professional services consultant, etc. At this point in my career, I have seen enough to relate with anyone in the IT world. Let’s narrow our focus and chat about monitoring system health and troubleshooting. Tool sprawl is the standard.
To fully utilize the capabilities of Kubernetes, it’s crucial to have a reliable system for gathering and organizing logs, metrics, and events. With the complex nature of container orchestration, it’s crucial to understand the significance and process behind the data generated in a Kubernetes environment at scale. Cribl Edge works seamlessly with Kubernetes and can cater to various needs.
Today, Cribl surpassed $100 million in annual recurring revenue (ARR), becoming one of the fastest companies to ever reach this milestone in under four years––an incredible achievement on our journey to building a generational company. Reaching $100 million in ARR so quickly shows that our unique approach and steadfast focus on IT and Security continues to be validated by the market.
Cribl’s interface is Super Neato: Reactive, beautiful, and easy to use. But sometimes you need to access settings and configurations programmatically. The good news is that interactive API docs are baked into your Cribl instance. The better news is that everything that happens in the GUI is making API calls. With your browser’s developer mode, you can easily take a peak behind the curtain to see exactly how the API was called and what the payload looked like.
At Cribl, we have the privilege of helping our customers achieve their strategic data goals by giving them visibility and control over all of their observability data. The reality today is that data is commonly stored across many places. Whether intentional (such as using Cribl Stream to create a security data lake) or unintentional (because of silos and tool sprawl), organizations desire the ability to access and analyze all of this information at any time.
The way we handle massive volumes of data from multiple sources is about to change fundamentally. The traditional data processing systems don’t always fit into our budget (unless you have some pretty deep pockets). Our wallets constantly need to expand to keep up with the changing data veracity and volume, which isn’t always feasible. Yet we keep doing it because data is a commodity.
Navigating the realm of Windows observability often referred to as O11y (short for observability), can be a complicated journey. Windows environments are known for their complexity, with various services, applications, and workloads running on each host.
The cybersecurity industry is experiencing an explosion of innovative tools designed to tackle complex security challenges. However, the hype surrounding these tools has outpaced their actual capabilities, leading many teams to struggle with complexity and extracting value from their investment. In this conversation with Optiv‘s Randy Lariar, we explore the potential and dangers of bringing advanced data analytics and artificial intelligence tools to the cybersecurity space.
One of the things I really love about working for Cribl is the ability to help our customers optimize their data. Microsoft Windows Event Logs are something I have always looked to as a proverbial Rosetta Stone to help translate semi-structured, classic-style events into something more efficient and less resource-intensive to search. Extracting field values requires a large number of regular expressions to parse the events, which isn’t ideal.
Imagine sending logs to cost-effective storage, converting them into efficient metrics, and forwarding only essential data for analysis. This change can slash ingest and long-term storage expenses by an order of magnitude! Enter Cribl Search—an ingenious solution that skillfully navigates storage, transforms logs into actionable metrics, and seamlessly channels vital data to your analysis systems. The result? Over 99.94% reduction in volume, enhanced efficiency and substantial cost savings.
In today’s landscape, what’s considered security data has expanded to encompass more diverse data types like network data, behavioral analytics, and application metrics. These sources are now essential for a comprehensive security strategy, and visibility into all that data makes proactive threat detection possible. That said, organizations often struggle to process data from various vendors and merge telemetry sets to gain a complete view of their environments.
Traditional logging tools are struggling to keep up with the explosive pace of data growth. Data collection isn’t the most straightforward process — so deploying and configuring all the tools necessary to manage this growth is more difficult than ever, and navigating evolving logging and monitoring requirements only adds another layer of complexity to the situation.
Getting your organization to invest in a new tool requires telling a story that helps decision-makers understand its benefits. In a recent webinar, our experts discussed how to define an ROI for Cribl Stream. They also shared a sample proposal you can use to craft the story you’ll tell to leadership, and gave some tips and tricks for justifying the purchase of these key tools for your business. Engineers and architects understand core technical problems better than anyone.
In today’s world of relentless data growth, security-relevant logs represent a small snapshot of an organization’s overall environment. Teams are beset with a variety of data types, including performance metrics and traces, asset configuration and state, audit logs, and much more. On top of that, teams are expected to scan all of this to compare against industry best practices and join this data with logs and metrics for added context.
