Cloudfabrix Launches Low Code Bots Based Composable In-Place Search and Log Intelligence to Reduce Your SIEM Costs and Enable Faster Time to Insights

Building on its release of the Composable Analytics for Observability and AIOps, CloudFabrix, the Data-centric AIOps Leader, and inventor of Robotic Data Automation Fabric Platform,  announced the availability of Composable Search and Log Intelligence as a service (LIaaS) to reduce Security Information and Event Management(SIEM) costs and Mean Time to Resolve(MTTR). This furthers our vision of unifying Observability, AIOps, and Automation.

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The rise of Edge Computing and Multi-cloud deployment has exacerbated the challenges around the 4V’s of the data – Volume, Velocity, Veracity, and Variety. The traditional approach of Collect ->Store ->Search results in data stores results in data swamps, due to repetitive and redundant data, without many actionable insights. Traditional search tools need data to be ingested, indexed, and then searched using proprietary query languages, creating data silos. These tools are complex and fall short when collecting data across disparate data sources, data formats, and data types as needed by Composable Decision boards, Composable Services, and Composable Pipelines.

A  Composable In-place Search is a transformational approach that enables a new Search -> Collect and -> Store paradigm from the traditional Collect-> Store and -> Search. Some of the benefits Composable Search provides are

• Faster Time to Insights and actions – The right data is discovered, searched, visualized, and then either presented as Composable decision boards or alert notifications.
• Reduce Complexity and Cost – associated with collecting, moving, indexing, storing, and then searching the data, increasing the TCO.
• Remove data silos – Low Code / No Code bots invoke a universal query language that can “In-place search” at the edge, across an observability data lake, any time-series database, or custom search tools like Splunk, Elastic, and others at the same time and aggregate data.
• Ease of use – Low Code / No Code bots make it easy for any Citizen developer to use search. Users can create search pipelines using RDA Studio, RDA Pipeline builder or simply using CLI commands.
• Work with any data type – Leverages patent-pending Robotic Data Automation Fabric Platform, which enables Data Integration and Ingestion, Data Filtering, and Transformation on Datasets, Dataframes, Dependency Mappings, Service tickets, Persistent Streams, and more using Low Code/No Code bots.

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Log Intelligence as a Service, is very effective in implementing cyber security mandates for log retention, and preventing security breaches by optimizing Security Incident and Event Management (SIEM), predictive business analytics, incident response, cloud automation, and orchestration.

Common practice is to collect and analyze logs to make a system observable, as log files contain most of the data from full-stack alerts and events. Existing Log Analytics solutions ingest repetitive and redundant data which drives licensing TCO exorbitantly high, drives compute and storage infrastructure costs, and most importantly results in poor MTTI and MTTR.

Log Intelligence overcomes these challenges, by reducing TCO by 40-80%, improving MTTI/MTTR by over 60% and productivity by over 40% as follows

• Edge IoT, In-place Search – Composable Search compliments Log Intelligence service. Collect and stores only valuable data as a full-fidelity copy in Observability Data Lake and In-place search as needed on security breaches and compliance needs
• Log Ingestion – Bring your own Log Tool (BYOL) and ingest data in pull/push/batch modes
• Log Reduction and Replay – Up to 40-80% log volume reduction using correlation techniques and replay using UTC timestamps, IP addresses, and certain patterns, to your choice of stream
• Log Routing – Aggregate logs, normalize, transform, enrich, and route to multiple locations – Data Lakes, log stores, analytic platforms, Composable dashboards, and more.
• Log Enrichment – Enrich logs using Geo-IP or DNS lookups from Infoblox, CVE (Common Vulnerability and Exposure) feeds, TIP (Threat Intelligent Platform) feeds
• Log Predictive Analytics – Convert logs into metrics and use a number of regression AI/ML models for anomaly detection.

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[To share your insights with us, please write to sghosh@martechseries.com]