Whitepaper - Splunk + Observo AI, Better Together: Unleashing the Power of Data
Learn how Splunk + Observo AI can improve security and observability by onboarding new data types for a comprehensive understanding of threats, inefficiencies, and vulnerabilities. Discover how pre-processing data before it hits a Splunk index can dramatically improve efficiency and how shifting analytics left into the stream can uncover and resolve critical incidents making security teams far more productive.
Topics include:
Eliminate Blind Spots By Getting All of the Right Data Into Splunk - Blind spots are introduced when you omit data sources from your analysis. Neglecting a source like VPC flow logs or application logs could increase the risk of security breaches, compliance violations, reduced network visibility and control, and other blindspots. Trying to control the volume of these sources through random sampling only increases the chance that the event that could avoid a costly incident would be missed.
Transform Data from Any Source into Splunk CIM - Observo AI can transform data from almost any source into Splunk’s Common Information Model (CIM). Whether you are looking for insights from custom application logs, OTEL, network logs, third-party firewalls, or virtually any source that grants visibility into your security and observability stance, Observo AI can transform that data for ultra-fast indexing into Splunk. This helps DevOps and security teams get the whole picture by looking at the widest array of relevant sources.
The Value of Pre-Processing Telemetry Data in the Stream - Adding context to security events by enriching them with threat intelligence data, such as known malicious IP addresses, domains, file hashes, and URLs can help identify potential threats more quickly. Enrich log data analyzed by Splunk to boost the productivity of DevOps and Security teams.
Control Infrastructure Costs Related to Storage, Egress and Compute - By optimizing data before it hits a Splunk index and creating a fully searchable data lake for long-term retention, reduce customers’ infrastructure costs including storage, cloud egress, and computing. One of the ways to keep these costs under control is by shifting longer-term data retention from the index into a security or observability data lake.
Shifting Analytics Left - Using AI/ML in the Telemetry Stream - shifting analytics “left” into the telemetry stream allows you to learn what is normal for any given data type and can quickly surface anomalies in real time before this data is indexed. When this data does get to Splunk, it can be prioritized and addressed quickly.