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Key Takeaways
- Data ingestion is critical for advanced, data-driven Security Operations.
- Preprocessing and filtering reduces noise, costs, and false positives.
- Normalization into a common schema like UDM enables faster detection and better insights.
- Choosing the right ingestion method depends on your environment and security goals.
- A well-designed ingestion pipeline improves visibility, automation, and business-aligned response.
Data ingestion is the core of any advanced, data-driven Security Operations. Google SecOps ingests raw log data, alerts, and other information. Ingested information is normalized and indexed for rapid search, then context enriched with data available from other ingested sources including threat intelligence feeds. Configuring data ingestion is the first step in preparing SecOps to correlate security events for your team. Netenrich’s indexing and context enrichment will enable your SecOps analysts to respond rapidly with a comprehensive view of threats and events.
Whether you're working in analytics, observability, or cybersecurity, how you bring together diverse data from across your environment, cloud, on-prem, SaaS apps, or endpoints, into a centralized platform can impact your organization’s ability to make swift decisions and control operational costs.
Data ingestion is especially critical in security operations where blind spots can hinder threat detection and incident response. A well-designed data ingestion strategy will reduce noise, enhance threat detection, and help you effectively manage data storage costs.
“Standardizing data is the first step to leveraging any AI; messy data will struggle to produce meaningful results.”
- Netenrich CISO Roundtable, 2025
A reliable data ingestion pipeline is critical for visibility, reliability, and automation. In hybrid cloud environments, particularly when deploying Google SecOps, security teams must understand how each step in the ingestion process affects detection, response, and operational efficiency.
What is Data Ingestion?
Data ingestion is the process of collecting and transporting raw data from multiple sources into centralized databases or storage systems. It is the first step in a data pipeline that prepares data for further processing, making it readily accessible for analysis.
It involves extracting data from various sources, like third-party providers, IoT devices, on-premise applications, and SAAS apps. Once ingested, the data, which can be both structured and unstructured, can be stored in data warehouses, data lakes, lakehouses, or document storage systems.
Why Does Data Ingestion Matter?
Data ingestion determines the scope and quality of all subsequent security analysis and decision-making. Getting data ingestion right ensures security analysts have a holistic, real-time, high-fidelity view across the digital estate.
With the advent of agentic AI, the impact of data ingestion in cybersecurity is profound. AI models are only as good as the data they’re trained on and continuously fed. Poor data ingestion leads to incomplete, noisy or biased datasets that could result in excessive false positives or inaccurate predictions. Complete, context-rich telemetry from relevant resources is required for AI-powered SecOps to classify malware, identify complex attack patterns. Without this, AI-driven systems suffer from data drift as real world attack behaviors evolve, rendering previous remedies obsolete. A clean data foundation paves the way for effective adaptation to new threats and provides proactive, actionable insights.
- Unified visibility: A good ingestion process helps you normalize data from disparate sources into a common schema so your teams view and analyze data through a single pane of glass.
- Sharper analytics and insights: This unified view of data helps teams connect the dots between data, enrich context, and uncover meaningful insights that aren’t possible with a siloed view. This could include consumer insights, business trends, or threat alerts.
- Data consistency and reliability: A good ingestion process helps you retain relevant data, removing corrupted or duplicate data and tagging key attributes for enrichment. This helps you improve the quality of data and reduce the cost of data storage.
- Enables automation: Clean, well-structured data also unlocks intelligent automation, such as automated alert triage, behavior analytics, and dynamic risk scoring. Without high-quality input, automation cannot deliver meaningful outcomes.
For security operations, an effective, unified data ingestion strategy can give you a centralized view of threats, improve threat detection, and help you cost-effectively manage your logs. Understand what goes into building an effective data ingestion strategy and get a practical demo of how to do this in our bootcamp.
Types of Data Ingestion
Data ingestion is primarily categorized into two types, each addressing unique business requirements, data characteristics, and target outcomes from data analysis.
Batch Processing
Batch processing, also called batch ingestion, involves loading data in large batches at pre-scheduled intervals. Aggregating the data before processing minimizes computational resource consumption.
This cost-effective ingestion process is best when:
- Consistently collecting data from stable sources
- Gathering significant volumes of data
- When real-time data analysis is not of high priority
Real-Time (Streaming) Processing
In real-time processing, also known as streaming ingestion, you continuously stream data for ongoing analysis As a result, your business can identify and react quickly to emerging issues or data trends.
This process is best used when:
- Data is collected from high-velocity sources like IoT sensors, click-streams, social-media feeds, or financial transactions.
- You need Immediate analysis for actionable insights, like data monitoring, fraud detection, and real-time incident response recommendations.
4 Steps in the Data Ingestion Process
So, how do you build an ingestion pipeline that’s both reliable and cost-effective, especially in a complex hybrid environment? We recommend focusing on four key stages :data collection, data preprocessing, data transformation, and data loading.
Step 1: Data Collection
The first step is data collection, which has two sub-steps.
- Discovery: Start by identifying the right sources for your data, which could include cloud services, endpoints, network devices, or SaaS platforms.
- Acquisition: Next, establish secure and scalable methods for collecting logs from these sources, utilizing agents, APIs, or built-in integrations.
Data Collection for Security Operations
Security architects deploying Google Secops can choose from different data ingestion methods:
- Open Telemetry: A lightweight, flexible option that runs on Linux, Mac, or Windows. Useful for teams already using OpenTelemetry for observability.
- Forwarder: A lightweight container on Linux or Windows. Ideal for collecting data from on-prem and legacy systems.
- Cloud: A cloud-to-cloud service via Feed Management UI. Best for cloud-native environments with centralized storage.
- Ingestion API: Lets you send data into Google SecOps in UDM or unstructured format.Useful for custom pipelines or enriching data before ingestion.
- Direct Ingestion: Automatically pulls logs from GCP services like Cloud Audit, DNS, and Security Command Center into Google SecOps. Ideal for those operating within the Google Cloud ecosystem.
The method you choose depends on your IT environment, data sources, and how much control and customization you need.
Step 2: Data Preprocessing
Before you ingest data, clean it at the edge. Check for inconsistencies, errors, missing values, or duplication. Remove corrupt entries, correct time stamps, apply field mappings, and tag key attributes like source, asset type, and location.
This step is critical for ensuring you don't waste time parsing broken or irrelevant data downstream.
Data Preprocessing for Security Operations
Ensure you’re not filling up your Data Lake with data that’s not useful. Filter out unused logs, corrupt or duplicate data, and send low-value logs to cold storage. By being prescriptive about what data you really need, your security operations can significantly reduce noise and cut storage costs.
Some recommendations include
- Define your log use cases:
Align log sources with use cases (e.g., threat detection, compliance, forensic investigation) and categorize logs as ‘Critical’, ‘Useful’, or ‘Redundant’. This way, you can curate your data to focus on what’s truly important to your business. - Filter unused logs:
- Avoid ingesting high-volume, low-value logs (e.g., DNS noise, successful logins at scale).
- Disable verbose debug-level logging unless required temporarily.
- Ensure log deduplication and compression
- Remove duplicate log entries at the ingestion layer.
- Use agents or pipelines to compress data before it hits the SIEM.
Note: Google SecOps will automatically apply compression and deduplication for efficient storage.
- Apply pre-ingestion filtering and routing
- Use Log Shippers or agents to filter fields, mask PII (for regulatory compliance, especially under GDPR or PSI), and route only necessary events
- Edge processing
Normalize and enrich data before ingestion to reduce compute and storage overhead. Route low-value logs to cheaper storage (e.g., GCS, S3) and high-value logs to SIEM.
This is where many teams miss a huge opportunity to reduce costs and improve signal quality before the data even hits their SIEM.
While we've covered the foundational steps here, advanced filtering and routing can further reduce noise and cost. We explore these expert-level techniques in Module 2 of our bootcamp.
Step 3: Data Transformation
Next, you must standardize data from disparate sources into a common schema.This means converting logs from disparate sources into the Unified Data Model (UDM). This UDM powers GoogleSecOps built-in detection rules and analytics, which in turn builds the foundation for automation and advanced investigations.
This step may involve aggregation (data summarizing), normalization (eliminating redundancies), and standardization (ensuring consistency in formatting) to make the data easier to interpret and analyze.
For example, one of our customers, a large global software company, needed to ingest more than 2 TB of security telemetry daily into Google SecOps from over 40 diverse log sources, including cloud, on-prem, and legacy systems. The team struggled with normalization and missed alerts due to inconsistent formats.
By implementing Google’s Unified Data Model (UDM) and building custom parsers for eight high-priority sources, they were able to streamline ingestion, reduce false positives, and cut costs by 50%. Most importantly, they could identify threats 99% more accurately and reduce their mean time to threat detection by 70%.
Data transformation for Security Operations
Transform logs into Google’s Unified Data Model (UDM) to enable Google Secops’ built-in detections and multi-source investigations. Invest in validating parser output regularly, misaligned UDM fields can break detection rules or cause missed alerts.
Some useful tips at this stage include field reduction and custom parsers
- Drop unused fields. Avoid parsing and storing non-essential fields (e.g., user-agent strings, debug data).
- Custom parsing rules. Only extract fields needed for detection or compliance. Avoid generic regex-based parsing, which retains too much data.
4. Data Loading
This is the final step, where you place the transformed data in its designated location, generally a data lake or warehouse, where it will be readily accessible for analysis and reporting. This is what’s essentially known as ingestion, done in real-time or in batches, depending on the specific business needs.
Data loading completes the ingestion pipeline, where the data is prepped for decision-making and generating business intelligence.Once transformed, load the data into your chosen platform, data lake, warehouse, or SIEM. Monitor loading status, throughput, and latency to ensure continuity and completeness.
Data Loading for Security Operations
Set up Google SecOps’s ingestion health monitoring to track log freshness and gaps. Use the Feed Management UI for cloud-based sources and APIs for custom pipelines. Aim for near real-time ingestion for high-priority telemetry like auth logs and endpoint alerts.
Data ingestion is a strategic enabler, especially for SecOps
Data ingestion right can help you unlock the true potential of your data whether you’re looking for consolidated customer insights or improving your security posture with a data-driven approach.
The quality of your data pipeline also impacts your AI and automation efforts. Clean, normalized, and enriched data enables advanced analytics and automated investigations. Simply put, better data fuels better decisions, especially when AI is in the loop.
For security teams in particular, especially those deploying Google SecOps solutions in hybrid environments, ingestion enables visibility, situational awareness, and unlocks scalable automation.
For a hands-on look at how to ingest hybrid cloud data into Chronicle the right way, check out Netenrich's Google SecOps 101 virtual bootcamp.
Frequently Asked Questions
1. What is the data ingestion process in security operations?
In security operations, data ingestion is the process of collecting, preparing, and loading telemetry like logs and alerts from various sources into a centralized system like Google SecOps. It ensures data is clean, consistent, and context-rich so analysts can detect threats quickly and accurately.
2. How do I build an effective data ingestion process for hybrid cloud environments?
Begin by identifying the right data sources across cloud and on-prem environments. Use methods like forwarders, APIs, or direct integrations to collect data. Preprocess it at the edge to remove noise, then normalize it into Google’s Unified Data Model (UDM) for consistent analysis. Apply filters and routing rules to control cost and improve signal quality.
3. How does the data ingestion process impact threat detection in SecOps?
A strong ingestion process directly improves threat detection. Clean, normalized data enables Google SecOps to apply built-in detection rules effectively, reduces false positives, and helps analysts connect events across systems. Poor ingestion, on the other hand, can lead to missed alerts or irrelevant noise.
4. What challenges can arise during the data ingestion process, and how can they be resolved?
Common challenges include ingesting unnecessary logs, inconsistent data formats, and lack of context. These can be addressed by:
- Defining use cases up front (e.g., threat detection vs. compliance)
- Applying filtering and field-level routing
- Normalizing data early using UDM
- Monitoring ingestion health to catch gaps and errors
Learn more in our step-by-step guide to configuring data ingestion into Google SecOps.
