For today’s IT and security professionals, threats come in many forms – from external actors attempting to breach your network defenses, to internal threats like rogue employees or insecure configurations. These threats, if left undetected, can lead to serious consequences such as data loss, system downtime, and reputational damage. However, detecting these threats can be challenging, due to the sheer volume and complexity of data generated by today’s IT systems.

Often it can be a challenge to collect data into a monitoring environment that does not natively support that data source. Bindplane can help solve this problem. As the Bindplane Agent is based on OpenTelemetry (and is also as freeform as possible), one can bring in data from disparate sources that are not easily supported by the Splunk Universal Forwarder.

As a leader in Security Analytics, we at Elastic are often asked for our recommendations for architectures for long-term data analysis. And more often than not, the concept of Limitless Data is a novel idea. Other security analytics vendors, struggling to support long-term data retention and analysis, are perpetuating a myth that organizations have no option but to deploy a slow and unwieldy data lake (or swamp) to store data for long periods of time. Let’s bust this myth.

In structured logging, log messages are broken down into key-value pairs, making it easier to search, filter, and analyze logs. This is in contrast to traditional logging, which usually consists of unstructured text that is difficult to parse and analyze.

Modern, data-driven enterprise SecOps teams use Security Information and Event Management (SIEM) software solutions to aggregate security logs, detect anomalies, hunt for security threats, and enable rapid response to security incidents. SIEMs enable accurate, near real-time detection of security threats, but today's SIEM solutions were never designed to handle the large amounts of security log data generated by modern organizations on a daily basis.

Personalized Service Health sends custom granular alerts about Google Cloud service disruptions, and integrates with incident management tooling.

Dark data is all of the unused, unknown and untapped data across an organization. This data is generated as a result of users’ daily interactions online with countless devices and systems — everything from machine data to server log files to unstructured data derived from social media. Organizations may consider this data too old to provide value, incomplete or redundant, or limited by a format that can’t be accessed with available tools.

A data lake is a data repository for terabytes or petabytes of raw data stored in its original format. The data can originate from a variety of data sources: IoT and sensor data, a simple file, or a binary large object (BLOB) such as a video, audio, image or multimedia file. Any manipulation of the data — to put it into a data pipeline and make it usable — is done when the data is extracted from the data lake.