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Quick Hits


LOGIQ.AI TACKLES OBSERVABILITY PROBLEM WITH LOGFLOW

LogFlow addresses data risks associated with machine data pipelines.
Dark Reading Staff
Dark Reading
December 03, 2021
Source: D3Damon via iStock
PDF


Data management and analytics startup Logiq.ai has launched LogFlow, an
"observability data pipeline as a service," aimed at security teams.



A data pipeline refers to all the steps associated with moving data from
on-premises systems to the cloud - from copying data, moving it across systems,
and reformatting or joining with other data sources - in an automated and
reliable way. However, the complexity of technologies being used to collect,
store, aggregate, and visualize data has made it difficult to maintain data
quality and integrity. Data downtime, which refers to periods of time when data
is partial, erroneous, missing, or otherwise inaccurate, is a big problem for
organizations as data systems become more complex. Observability refers to the
monitoring, tracking, and triaging of incidents to prevent downtime.

Consider a scenario where the security team is looking over logs and security
events, and data is missing data that could point to a ransomware attack. Data
downtime can impact the ability for IT security teams to detect attacks or
identify gaps in controls.

LogFlow is designed to tackle the observability problem, optimize data volume,
and improve data quality, Logiq says. Its log management and SIEM capabilities
allow security event detection and tagging, and native support for open
standards makes it easy to collect machine data from any source, the company
says. With over 2,000 rules as part of built-in "Rule Packs," security teams
can filter, tag, extract, and rewrite data for popular customer environments and
workloads. LogFlow can also continue ingesting data and providing crucial
forensics even when upstream systems are unavailable, the company says.




There are a number of open source tools capable of routing data between various
sources and target systems, such as Fluent Bit and Logstash, Logiq.ai says.
Enterprises can use LogFlow to deal with other data pipeline challenges, such as
controlling data volume and sprawl, preventing data loss, ensuring data
reusability with fine-grained control, and ensuring business continuity during
upstream failures, the company says.

Read more here.


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