Splunk is the backbone of security analytics for many of the world’s most advanced enterprises. Its ability to unify, correlate, and analyze massive volumes of telemetry data has made it the platform of choice for SOC teams worldwide. But as security data grows exponentially in volume and complexity, many organizations face a different challenge: getting the right data into Splunk, in the right shape, without straining infrastructure resources or missing critical signals.

That’s where Observo AI comes in.

Observo AI is proud to be a valued Splunk partner, offering advanced data pipelines that optimize log ingestion, onboard complex or custom data sources, and improve visibility without compromising performance. Whether you’re a Splunk Enterprise Security (ES) customer, using ingest actions, or building around Splunk’s Common Information Model (CIM), Observo AI can help you get more out of your existing investment—starting at the source.

Expand Coverage. Improve Data Quality. Ease Analyst Workloads.

Unstructured logs from cloud services or custom apps can be difficult to onboard or normalize into CIM. Many modern telemetry sources—like SaaS tools, containerized workloads, and custom applications—generate logs in inconsistent or proprietary formats. Without automated parsing and normalization, these logs require manual effort to make them usable in Splunk. This not only delays onboarding but also prevents teams from mapping events to Splunk’s Common Information Model (CIM), which is critical for powering correlation searches, risk-based alerting, and prebuilt dashboards in Enterprise Security. As a result, valuable data sits unused or misaligned, reducing its security value.

Redundant, noisy events clutter dashboards and burn compute cycles. Security teams often ingest massive volumes of low-signal data—like repeated firewall blocks, heartbeat messages, or system-level debug logs. These events can overwhelm Splunk indexes and inflate infrastructure usage, leading to slower search performance and less responsive dashboards. More importantly, they increase the cognitive load on analysts, who must sift through noise to find truly meaningful alerts. This makes it harder to detect threats quickly and contributes to alert fatigue in already overwhelmed SOC environments.

Blind spots emerge when critical telemetry sources are excluded due to size, complexity, or cost concerns. Ingesting high-volume or hard-to-parse data often becomes cost-prohibitive, especially when infrastructure limits or other considerations force organizations to be selective. Teams may choose to leave out telemetry from lower-priority environments, edge devices, or unstructured logs—despite the fact that those sources may contain early indicators of compromise or useful signals for threat hunting. This creates dangerous gaps in coverage and reduces the overall effectiveness of Splunk as a comprehensive security analytics platform.

Solving These Challenges with Observo AI

Observo AI solves these challenges with intelligent data pipelines that work before logs ever reach Splunk. By optimizing data upstream, our platform ensures that only high-value, well-structured, and context-rich telemetry reaches your Splunk environment. This reduces infrastructure strain and improves visibility, enabling security teams to move faster and make better decisions with cleaner, more actionable data.

Our platform parses and transforms logs into Splunk’s CIM format, making them instantly usable for detection rules, dashboards, and correlation searches. Whether logs originate from cloud-native services, third-party SaaS platforms, or custom-built applications, Observo AI automatically identifies and converts them into the fields and structure expected by Splunk’s Common Information Model. This eliminates the need for time-consuming custom field extractions or brittle parsing rules, ensuring consistent behavior across detection content, dashboards, and analytics.

We filter out low-value events like heartbeat messages or debug logs—reducing noise and increasing performance. These types of logs, while necessary in some contexts, often have limited security value and can account for a disproportionate share of ingestion and storage resources. Observo AI pipelines intelligently suppress or summarize such events upstream, so Splunk doesn’t waste compute cycles or index space on logs that don’t help you detect, investigate, or respond to threats. The result: faster searches, clearer dashboards, and less analyst fatigue.

We also add enrichment like threat intel or geo-IP data to give Splunk-native tools more to work with, without the need for manual joins or lookups. By tagging logs with critical metadata as they pass through the pipeline—such as geolocation, user identity, asset criticality, or known threat indicators—Observo AI enables Splunk to deliver more precise detections and correlations. This reduces the need for post-ingest enrichment steps, simplifies investigation workflows, and accelerates time to insight across the SOC.

Observo Orion: Your Agentic AI Data Engineer

A key partner in your security projects is Observo Orion, our intelligent AI assistant purpose-built to help security and observability teams accelerate pipeline creation, improve data quality, and unlock the full potential of Splunk. Acting like a virtual data engineer, Orion enables teams to go from data ingestion to actionable insight in a fraction of the time—without writing complex code or taxing already scarce engineering resources.

Orion simplifies the onboarding of complex data sources—like custom application logs or third-party SaaS tools—using natural language prompts. Instead of struggling with documentation, sample events, or inconsistent field structures, Splunk users can describe the source in plain English, and Orion will auto-generate the necessary parsing and routing configurations. This dramatically speeds up time to visibility, especially for sources that traditionally require manual intervention or bespoke engineering.

Orion automatically generates Grok patterns to parse unstructured or unfamiliar log formats. This feature is particularly valuable when dealing with proprietary data, inconsistent schemas, or sources that don't adhere to standard logging conventions. By identifying patterns, extracting fields, and structuring the output for Splunk’s Common Information Model (CIM), Orion removes one of the most time-consuming and error-prone parts of the onboarding process.

Orion enables real-time telemetry routing, enrichment, and transformation through intuitive, no-code workflows. Security and DevOps teams can build logic-driven pipelines visually—choosing how logs should be filtered, where they should be sent, and what contextual enrichment should be applied. This allows analysts and engineers to experiment, deploy, and iterate without waiting on backend changes or writing custom scripts.

Orion provides AI-suggested optimizations to help reduce infrastructure strain while expanding telemetry visibility. Whether it's identifying redundant log streams, proposing routing rules for tiered storage, or recommending field drops for low-signal data, Orion proactively looks for ways to improve performance and reduce storage and compute overhead—without sacrificing the fidelity or coverage that Splunk needs to be effective.

Together, Observo AI and Orion empower Splunk users to work smarter with their data—streamlining operations, speeding query results, improving threat detection, and achieving better outcomes with less manual effort.

Complementing Ingest Actions and Driving CIM Compliance

Splunk’s Ingest Actions offer powerful controls for filtering and routing data, and Observo AI builds on that capability with pre-ingest intelligence. While Ingest Actions are highly effective at shaping data as it enters Splunk, Observo AI extends those benefits further upstream—before the data even reaches the ingestion layer. By applying intelligent filtering, enrichment, and normalization in transit, Observo ensures that only the most valuable and usable events are delivered to Splunk for indexing and analysis.

Our pipelines pre-filter and transform telemetry upstream, so Splunk only receives high-value, CIM-compliant events. This reduces unnecessary overhead and accelerates the effectiveness of Splunk-native content. Logs are evaluated in real time, with redundant, low-signal, or irrelevant entries removed before they reach your Splunk infrastructure. The remaining events are transformed to align with Splunk’s CIM, enabling seamless use across correlation searches, prebuilt detection rules, and risk-based alerting frameworks.

We also normalize even complex or proprietary log formats into Splunk-ready structure—no custom scripts required. Whether dealing with unstructured app logs, edge device telemetry, or niche SaaS data, Observo AI can automatically detect field patterns, extract meaningful values, and restructure the data into standardized formats. This eliminates the need for hand-coded extractions or brittle regex configurations that require constant maintenance.

Additionally, our platform integrates with threat intelligence feeds to enrich data with real-time context before it enters the Splunk index. Indicators such as known malicious IPs, risky geolocations, and asset criticality are applied to events as metadata, giving Splunk Enterprise Security (ES) users more context out of the box. This makes searches faster and investigations more precise, without requiring analysts to manually correlate logs with external data sources after ingestion.

The result is a smarter, leaner, and more effective Splunk environment. Correlation searches run faster, dashboards load with greater accuracy, and false positives are reduced—because the data powering them is cleaner, enriched, and better aligned with the structure Splunk was designed to work with. Best of all, these benefits are achieved without increasing egress, compute, or storage strain—allowing teams to scale their visibility without scaling their infrastructure expenditure.

Use AI to Reduce Infrastructure Strain—Not Coverage

Many Splunk customers face tough tradeoffs between retaining fidelity and controlling cost. Observo AI changes that equation.

Rather than collecting and storing every raw log, our platform intelligently summarizes redundant events and performs real-time deduplication. Instead of flooding Splunk with thousands of nearly identical entries, we deliver structured, enriched data that’s ready for immediate use—boosting performance, reducing compute overhead, and improving analyst productivity.

Examples include summarizing failed login attempts by source, username, and count. Instead of ingesting thousands of near-duplicate log entries for every failed login event, Observo AI intelligently aggregates these into a single, enriched record. This summary still preserves critical information—such as the frequency of attempts, the user being targeted, and the originating IP address—while drastically reducing the volume of data sent to Splunk. It’s a more efficient way to retain visibility into brute force activity without overloading your indexers or dashboards.

Another example is aggregating network flow logs by protocol, destination, and volume. Network telemetry often generates immense volumes of granular data, which can quickly consume indexing and storage capacity. Observo AI reduces this footprint by summarizing traffic into digestible insights—such as total bytes transferred per protocol or connection patterns by destination. These summaries are still actionable and valuable for security analytics but are far less burdensome to store, query, and analyze.

We also support masking sensitive personally identifiable information (PII) to meet privacy requirements without sacrificing security context. Logs that include usernames, email addresses, IPs, or other identifiers can be obfuscated or anonymized in real time—ensuring compliance with regulations like GDPR, CCPA, or HIPAA. Crucially, Observo AI enables teams to retain the structure and utility of the data, so analysts can still detect patterns, investigate threats, and correlate activity across systems—without exposing sensitive personal data.

Together, these capabilities ease the burden on indexing, querying, and storage systems—so you can scale Splunk usage without scaling infrastructure costs in parallel. By pre-processing logs with AI and intelligent logic before they ever reach Splunk, Observo AI helps teams make the most of their security data—maximizing coverage and fidelity while keeping performance high and overhead low.

Real-Time Anomaly Detection at the Source

Observo AI also provides ML-based anomaly detection before logs reach Splunk, giving security teams a powerful way to prioritize the data that truly matters. By analyzing log streams in real time at the source, Observo AI identifies deviations from expected behavior—such as spikes in failed login attempts, unusual authentication patterns, or unexpected outbound traffic—before the data ever hits your SIEM. This upstream intelligence empowers SOC teams to streamline what gets ingested and focus their attention on what needs deeper investigation using Splunk.

One powerful use case is the ability to flag and forward only anomalous events from low-risk systems. Instead of ingesting every log from test environments, internal tools, or low-priority endpoints, Observo AI can detect when something unusual occurs—such as a deviation from baseline behavior—and send only those outliers to Splunk. This ensures important anomalies are not overlooked, while also dramatically reducing the ingestion of routine or irrelevant data.

At the same time, teams can prioritize high-fidelity data from production environments, where the risk of impact is much greater. Logs from critical systems—such as identity providers, databases, or external-facing applications—can be passed through in full, ensuring that Splunk receives a complete and enriched picture of what's happening in your most sensitive environments. This selective prioritization strengthens visibility where it matters most, without flooding your index with noise.

Routine or lower-risk logs can be routed to less expensive storage, like AWS S3 or Google Cloud Storage (GCS), while retaining full-fidelity logs in Splunk for real-time analytics. Observo AI makes it easy to implement dynamic routing based on log type, severity, or source. This approach allows organizations to build a tiered storage strategy that maintains comprehensive coverage while dramatically reducing infrastructure overhead. Critical data remains hot and searchable in Splunk, while less urgent data is retained affordably for compliance, audit, or future investigation.

Think of it as intelligent data tiering for the modern SOC—automated, secure, and fully auditable. With Observo AI, every event is evaluated in context, and each routing decision is logged for transparency. This not only reduces infrastructure cost and noise but also gives SOC teams greater control over their telemetry strategy—ensuring Splunk receives the right data at the right time, every time.

A Smarter Way to Get the Most from Your Splunk Investment

Observo AI helps security and observability teams unlock the full potential of Splunk by delivering deeper visibility across custom, cloud, and unstructured sources; AI-powered onboarding of challenging log formats and schemas; real-time enrichment and CIM compliance for better detection; and infrastructure cost control through data filtering and smart routing.

With Observo AI, your data is Splunk-ready—cleaner, smarter, and faster to act on. That means stronger detection, faster resolution, and more confident decisions.

Splunk is one of the most respected platforms in the industry. Together, Splunk and Observo AI deliver the next generation of telemetry optimization—so you can protect more, spend less, and act faster.

Want a deeper dive into how AI-powered pipelines can strengthen your security posture? Download the CISO Field Guide to AI Security Data Pipelines for expert insights and practical frameworks.