When security data volumes double every two to three years but budgets stay mostly flat, achieving a fast return on investment is the only way most security organizations can get new technologies approved. Teams can’t afford to wait months to see results—they need solutions that pay off starting on the first day of the proof-of-value period.

AI-powered data pipelines make that possible. By removing the manual work involved in ingesting, transforming, and routing telemetry data, these systems begin reducing costs and boosting team productivity from the moment data starts flowing. The sooner usable data gets into the right tools, the sooner teams can shift focus from moving and manipulating data to protecting their enterprise.

Automate Data Ingest and Normalization

The first major hurdle is making data usable across all of the tools security teams depend on. Security teams deal with highly diverse data from cloud services, firewalls, endpoints, and custom applications—each with different schemas and formats. Some logs are structured JSON, others are raw syslog or CSV, and many change format frequently. Traditional approaches required painstaking parser development for each source, resulting in slow onboarding and brittle pipelines.

AI-powered pipelines address this challenge through a combination of automation and built-in capabilities. Pre-built parsers handle widely used sources like CloudTrail, VPC Flow Logs, Windows Events, and common firewall logs without any manual configuration. Pre-defined transforms then clean, optimize, and restructure the data so it’s ready for downstream tools in the right format—whether that’s CIM, ECS, OCSF, or a custom schema.

For unstructured or custom logs, machine learning models can automatically detect log patterns and generate Grok expressions—extracting timestamps, IPs, methods, user agents, and other key fields without requiring teams to write regex manually. These systems learn from data samples, build patterns, and test them live—cutting onboarding time drastically. Using AI-driven Grok generation and pre-built transforms can get unique and difficult to understand schemas like those in custom application logs onboarded in minutes–not days of manually matching fields and writing rules-based filters.

Build and Deploy Pipelines with Simple Drag-and-Drop Interfaces

After getting data normalized, the next priority is control. Traditional pipelines are often built through code, config files, or painstakingly crafting rules-based logic (regex, filtering conditions, transform rules), making it hard to keep up with changes in the data over time and getting data optimization beyond the most obvious changes. Even the most gifted security engineers can’t find low-signal patterns in their telemetry that should be filtered.

AI-powered platforms give teams full visibility with drag-and-drop interfaces. Engineers can see how their data changes at every stage, compare input to output in real time, and observe exactly how volume is reduced. They can enrich, filter, and route logs without needing to write code—enabling more stakeholders to participate in pipeline design and optimization. For compliance teams, this visibility also makes it easier to review decisions and audit data handling practices.

Use Agentic AI to Automate Data Engineering

Getting quick payback on pipeline investments requires the automation of cumbersome data engineering tasks. Optimizing data by describing what they want in natural language lets users meet their goals and find optimizations they didn’t even know about instantly. Agentic AI enables users to say things like “summarize all consecutive firewall events marked ‘ALLOW’,” “Create a pipeline from source S3 of type syslog destination splunk,” or “apply PII masking to this pipeline,” and the system configures the necessary transformations behind the scenes.

These AI agents do more than execute instructions. They proactively suggest improvements, adapt to changing log patterns, detect potential schema issues like schema drift, or unexpected changes in ingest volume, and raise alerts for anomalies or sensitive data. Under the hood, multiple machine learning models handle different responsibilities—from understanding user intent, to executing transformations, to recommending optimizations based on live traffic. This architecture dramatically shortens the feedback loop between identifying a need and implementing a fix.

The result is fewer dependencies on senior engineers, less time spent writing scripts, and much faster iteration across the pipeline lifecycle.

Avoid Ingest Overage and Reduce Infrastructure Waste

Many organizations structure their workflows around avoiding ingest overages. They sample logs, drop sources, delay onboarding, or build elaborate workarounds just to avoid exceeding daily limits. These constraints reduce visibility and force security teams to make tradeoffs they shouldn’t have to make like not adding a new data source because there’s no room in the daily ingest budget.

AI-powered pipelines resolve this problem by reducing data volume before it ever hits downstream tools. Logs are filtered for redundancy, cleaned of unnecessary fields, and routed only where needed—whether that’s a SIEM, a SOAR, an XDR, or a low-cost data lake. That means ingest caps become irrelevant. Teams stop wasting hours trying to “fit” inside constraints and instead regain flexibility to onboard new data without creating budget headaches.

Even if SIEM or observability platform contracts are fixed until renewal, infrastructure savings start immediately. Smaller log volumes reduce egress, lower storage costs, and reduce compute strain. Leaner indexes also mean faster searches and improved query effectiveness.

Refocus Security Analysts on Security, Not Data Management 

Security analysts shouldn’t be spending their time managing ingestion logic or troubleshooting broken parsers. But too often, they do because keeping telemetry usable and relevant is an ongoing effort.

With AI pipelines automating schema translation, routing, and reduction, and data compliance, analysts are freed to spend more time investigating threats, triaging alerts, and hunting anomalies. These platforms also help speed detection itself by identifying unusual patterns and promoting high-signal events. Built-in anomaly detection can automatically surface issues that would otherwise get buried in noisy data.

This leads to significantly faster mean time to resolution (MTTR). With less noise and more focus, security teams can detect and respond to critical issues sooner—improving the organization’s overall security efforts.

Get to ROI Right Away

The biggest difference with AI-powered pipelines? They generate value immediately. In proof-of-value engagements, it’s common to see telemetry volume drop by 70-80%, storage and compute costs shrink accordingly, and MTTR improve by 40% or more—all before a purchase decision is even made.

This isn’t about promises or long-term ROI projections. The savings start on day one. And as SIEM and other tool contracts renew and more data sources are onboarded, the long-term benefits grow even further—flattening data growth curves and enabling sustainable, scalable security operations.

Find out for yourself. Schedule a demo to see how quickly your team could cut telemetry volume, reduce costs, and accelerate MTTR with Observo AI. Most teams eliminate up to 80% of unnecessary data and improve incident response times by 40% or more.

For a deeper look at how security leaders are using AI to modernize their telemetry strategy, download the CISO Field Guide to AI Security Data Pipelines.