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.
The challenge for every organization is gathering actionable observability information from all your systems, in a timely manner, without creating a substantial operational burden for the teams managing the collection tooling. While each observability solution has its unique benefits and challenges, the one common burden expressed by teams is the management of the metadata of the metrics, traces, and logs.
Data growth has significantly out-pacing budgets; the products we use, have to do more. This is where optimization comes into play. Generally, optimization is associated with reduction which may be intimidating…what if something important is reduced? How can you identify what should be reduced? Reduction isn’t about removing context, but about removing repetitive data, meaningless fields, or even flattening JSON.
