1. Executive Summary

The security landscape is undergoing a fundamental shift. Organisations that built their detection and response capabilities around a single SIEM platform now face a difficult reality: legacy pricing models are becoming unsustainable, cloud-native alternatives are maturing rapidly, and the volume of security telemetry is growing exponentially. Migration is no longer a question of if, but when and how.

Yet SIEM migrations remain one of the most complex and risk-laden programmes a security team can undertake. The stakes are high: a poorly executed transition can leave an organisation blind to threats, breach compliance requirements, and consume months of engineering effort with little to show for it.

This whitepaper argues that the key to a successful SIEM migration lies not in the destination platform, but in the journey itself—and specifically, in the adoption of a data pipeline layer that decouples your security data from any single vendor. Drawing on real-world experience, including a case study of a leading UK financial institution, we present a four-phase framework that enables organisations to migrate with confidence, maintain security posture throughout, and reduce data costs in the process.

Key insight: Organisations that deploy a telemetry pipeline before migrating report 30–50% reductions in SIEM ingestion costs and near-zero downtime during transition.

2. The Shifting SIEM Landscape

2.1 The Cost Pressure

Traditional SIEM platforms were designed in an era when log volumes were measured in gigabytes per day. Today, a mid-sized enterprise can easily generate terabytes of security telemetry daily across endpoints, cloud workloads, identity providers, network devices, and SaaS applications. Legacy SIEM vendors that charge by ingestion volume have seen their customers’ bills grow far faster than their security budgets.

This cost pressure creates a perverse incentive: security teams are forced to decide which data sources to exclude from the SIEM, not based on security value, but on cost. Critical telemetry from cloud-native services or high-volume network flows is often the first to be dropped, creating dangerous blind spots in an organisation’s security posture.

 

2.2 The Rise of Security Data Pipelines

Industry analysts have identified security data pipelines as one of the most significant architectural shifts in modern security operations. Research from Software Analyst Cyber Research (SACR) highlights that traditional SIEMs are struggling to keep pace with the expanding volume of security telemetry, and that the solution is not to replace the SIEM itself, but to fundamentally change how we feed it. However, if other considerations commercial, one stack apply, of course Pipelines aide that migration, but choice is there, that was not before.  The goal, according to SACR’s 2025 Market Guide, is to ingest the right data through programmable pipelines that deduplicate, filter, enrich, and route telemetry before it ever reaches the analytics layer.

This market is maturing rapidly. Cribl, the category leader, has surpassed $200 million in annual recurring revenue and commands a valuation of $3.5 billion. The consolidation of smaller pipeline vendors by major security players—SentinelOne acquiring Observo AI, CrowdStrike acquiring Onum—signals that the industry views data pipelines not as a niche tool but as foundational infrastructure for modern SOCs.

SACR research insight: Without clean, well-routed telemetry, even the smartest AI-powered detection is starved of context. The data layer is now the most important layer in the SOC.

 

2.3 The Cloud-Native Wave

The emergence of cloud-native SIEM platforms, most notably Microsoft Sentinel, has fundamentally changed the economics and architecture of security monitoring. These platforms offer consumption-based pricing, native integration with cloud ecosystems, and modern query languages such as KQL (Kusto Query Language) that are purpose-built for large-scale log analysis.

However, moving to a cloud-native SIEM is not simply a lift-and-shift exercise. Detection rules written in SPL (Splunk’s Search Processing Language) cannot be directly ported to KQL. Data schemas differ between platforms. Integration points, automation playbooks, and analyst workflows all need to be rebuilt or adapted.

 

2.4 The Vendor Lock-In Problem

Perhaps the most insidious challenge is vendor lock-in. Traditional SIEMs act as monolithic systems: they ingest, store, index, and analyse data within a single proprietary stack. Moving away means migrating not just data flows, but years of accumulated detection logic, custom dashboards, and operational processes. This accumulated technical debt is what makes SIEM migrations so daunting—and why so many organisations delay the decision far longer than they should.

 

3. Why SIEM Migrations Fail

Understanding why SIEM migrations fail is essential to designing one that succeeds. Based on our experience supporting complex platform transitions, the most common failure modes fall into five categories.

 

3.1 The Big-Bang Approach

The most dangerous migration strategy is the “big bang”: switching from the old SIEM to the new on a fixed cutover date. This approach leaves no room for error. If the new platform misses an incident the old one would have caught, the organisation is exposed. If data mappings are incomplete, compliance requirements may be breached.  This ‘rush’ phenomena before ‘our licence expires’ never ends well.

 

3.2 Detection Rule Translation

Every mature SIEM deployment includes hundreds or thousands of detection rules tuned over years. Translating these to a new platform is not a straightforward conversion. Query languages have different capabilities, data schemas have different field names, and underlying search engines have different performance characteristics. A rule that performs well in SPL may need fundamental redesign for KQL.

 

3.3 Data Mapping & Compliance

Source data arrives in the format determined by the source, not the SIEM. Every SIEM normalises data into its own schema (Splunk uses CIM, Sentinel uses ASIM). When migrating, every data source must be re-mapped—a substantial undertaking routinely underestimated. Meanwhile, any gap in monitoring coverage, even brief, can have serious regulatory consequences in financial services, healthcare, and other regulated sectors.

 

3.4 Organisational Resistance

SOC analysts and security engineers often have years of platform-specific expertise. Migration means retraining and, in the short term, reduced productivity. Without careful change management, this resistance can undermine even a well-planned technical migration.

 

Migration Risks: Traditional vs Pipeline-Enabled

Risk	Traditional Approach	Pipeline-Enabled
Coverage gap	High—old SIEM off before new is validated	Eliminated—parallel run with cloned data
Detection parity	Untested until cutover day	Validated side-by-side on live data
Data cost	Double cost for parallel ingestion	Optimised—filter & reduce before ingestion
Vendor lock-in	Replaced with new lock-in	Pipeline provides ongoing flexibility
Rollback capability	Very difficult once committed	Trivial—re-route data back to old SIEM

4. The Data Pipeline Approach

A data pipeline—sometimes called a telemetry or observability pipeline—is an intermediary layer between data sources and downstream analytics platforms. Rather than sending raw logs directly into a SIEM, data flows first through the pipeline, where it can be collected, parsed, enriched, filtered, transformed, and routed to one or more destinations.

This architectural pattern, now standard in observability, is gaining rapid adoption in security operations. The reason is simple: it gives the security team control over their data independently of any SIEM vendor.

 

4.1 How a Telemetry Pipeline Works

At its core, a telemetry pipeline performs six key functions that fundamentally change how organisations manage security data:

• Collection: Ingests data from any source via a library of 80+ pre-built connectors covering firewalls, endpoints, cloud services, identity platforms, and custom applications.
• Parsing and enrichment: Adds contextual data—GeoIP lookups, threat intelligence indicators, asset metadata, user identity—to raw events before they reach the SIEM.
• Filtering and reduction: Removes null fields, duplicate events, verbose debug data, and other low-value telemetry that inflates SIEM costs without improving detection.
• Schema transformation: Maps source data to the destination platform’s expected schema (e.g., CIM to ASIM when moving from Splunk to Sentinel) or go OSCF or OTel!
• Cloning and routing: Creates copies of the same event for multiple destinations simultaneously—the capability that makes parallel SIEM operation possible.
• Replay: Stores full-fidelity copies in low-cost object storage (such as S3) and replays them into any platform on demand for retrospective investigation.

4.2 The Cost Equation

By filtering and reducing data before it reaches the SIEM, organisations typically achieve a 30–50% reduction in ingestion volume. For organisations spending six or seven figures annually on SIEM licensing, this translates to substantial savings that often pay for the pipeline infrastructure several times over.

The pipeline also enables data tiering. High-value security events route to the SIEM for real-time detection. High-volume, lower-priority data (verbose network flows, debug-level application logs) routes directly to cost-effective object storage, available for on-demand investigation via the pipeline’s replay capability.

 

4.3 Vendor Independence and the Composable SOC

When data sources connect to a pipeline rather than directly to a SIEM, switching analytics platforms becomes a routing decision rather than a re-integration project. This positions the organisation to take advantage of new platforms and pricing models without the pain of a full migration each time.

SACR research describes this as the pipeline becoming a control plane for the SOC. Rather than one monolithic SIEM, teams can adopt a composable architecture: a cloud-native SIEM for real-time detection, a data lake for long-term retention and hunting, and specialist tools for UEBA, SOAR, or compliance—all fed consistently by a single pipeline.

One of the most overlooked benefits of a pipeline-enabled migration is the opportunity to fundamentally redesign your data retention architecture at the same time. Too often, long-term storage is treated as an afterthought – bolted on after migration is complete. This is a missed opportunity. When a telemetry pipeline is already being deployed as the migration’s data plane, routing a copy of every event to low-cost object storage (such as Amazon S3, Azure Blob, or Google Cloud Storage) adds negligible complexity but delivers transformative value.

 

5.1 Why Migration is the Right Moment

During the discovery phase, teams are already auditing every data source, mapping volumes, and classifying data by security and compliance value. This is the natural point to also define a tiered retention strategy: which data needs to be in the SIEM for real-time detection (hot tier), which needs to be queryable within hours for investigation (warm tier), and which needs to be retained for months or years purely for compliance and forensic purposes (cold tier).

Designing this storage architecture during discovery—rather than retrofitting it later—means the pipeline can be configured once and route data to the right tier from the moment it goes live. There is no second migration, no re-integration, and no period where retention policies are out of compliance.

 

5.2 The Economics of Tiered Storage

The cost differential between SIEM ingestion and object storage is dramatic. SIEM platforms typically charge per gigabyte ingested and indexed, with costs ranging from several pounds to over ten pounds per GB/day depending on the vendor and contract. Object storage, by contrast, costs pennies per GB/month. For an organisation ingesting 1TB/day into a SIEM, routing even 40% of that volume directly to S3 instead can save hundreds of thousands of pounds annually – while retaining full access to the data via the pipeline’s replay capability.

This is not about losing visibility. High-value security events still flow to the SIEM in real time. But verbose telemetry – detailed network flow logs, raw DNS queries, debug-level application events, and authentication success logs – can be archived at a fraction of the cost and replayed into the SIEM on demand when an investigation requires them.  There are now many SIEM’s and other tooling offering federated search also.

 

5.3 Replay: Cold Storage That Isn’t Cold

The replay capability that modern pipelines like Cribl Stream provide is what makes this architecture practical. Unlike traditional cold storage that requires manual export and re-ingestion, pipeline replay allows analysts to select a time window and data source, then automatically re-route archived data into the SIEM or any other analytics tool. A forensic investigation that might have taken days of manual data wrangling can be completed in hours.

This capability also addresses a common compliance challenge: regulators may require organisations to retain certain log types for years, but querying that data in a SIEM for the entire retention period would be prohibitively expensive. Pipeline-to-S3 with replay provides compliant retention at object storage costs with on-demand queryability when needed.  As mentioned if replay is not favoured other federated in-situ offerings are available.

5.4 Incorporating Storage Design into Discovery

We recommend adding the following activities to the discovery phase of any pipeline-enabled migration:

• Data classification: For each source, determine whether events should route to the SIEM (real-time detection), object storage (long-term retention), or both (clone and route).
• Retention policy mapping: Align each data class with regulatory and internal retention requirements, from 90-day SOC investigation windows to 7-year compliance mandates.
• Volume and cost modeling: Calculate the cost impact of each tiering scenario to build the business case and quantify savings versus full SIEM ingestion.
• Replay workflow design: Define how and when archived data will be replayed, who can trigger replays, and which analytics platforms should receive replayed data.

 

Design principle: If you are deploying a pipeline for migration, you are already 90% of the way to a tiered storage architecture. The remaining 10% is discovery and configuration—do it now, not later.

 

7. Case Study: UK Financial Institution

A leading British bank had operated Splunk as its primary SIEM for several years. Rising ingestion costs and a strategic decision to consolidate on the Microsoft security stack led to evaluating a migration to Microsoft Sentinel. However, several constraints made a direct migration impractical:

• High-volume security telemetry across thousands of data sources.
• Regulatory requirements mandating continuous monitoring with no coverage gaps.
• Detection rules refined over years representing significant institutional knowledge.
• SOC team needing to maintain operational effectiveness throughout.

 

The Solution: Cribl Stream as the Migration Layer

Apto Solutions designed a migration architecture using Cribl Stream as the telemetry pipeline layer, deployed between the bank’s data sources and SIEM infrastructure.

The pipeline performed several critical functions simultaneously. It continued feeding Splunk with unmodified data, ensuring all existing detection rules, dashboards, and workflows operated without interruption. In parallel, the same data was cloned, transformed to Sentinel’s ASIM schema, and forwarded to the new workspace.

Cribl’s parsing and mapping capabilities handled schema translation between CIM and ASIM, eliminating manual re-integration for each source. Full-fidelity raw data was also forwarded to S3-compatible storage, providing a long-term archive and enabling Cribl’s replay capability for retrospective investigation.

 

The Outcome

• Zero-downtime migration: Full security monitoring maintained throughout with no detection coverage gap.
• Cost optimisation: Data filtering and reduction through the pipeline significantly reduced ingestion volumes into Sentinel.
• Detection parity: Parallel-run period enabled side-by-side validation of every detection rule before legacy decommission.
• Strategic flexibility: Pipeline layer remains post-migration, enabling future platform changes via routing decisions.
• Replay capability: Full-fidelity data archived in S3 supports forensic investigation and compliance audit on demand.

The pipeline-enabled approach transformed what could have been a high-risk, multi-month programme into a controlled, phased transition with continuous security coverage.

8. Building Your Migration Strategy

Start with Data, Not Platforms

The natural instinct is to start by evaluating destination platforms. We recommend the opposite: start by understanding your data. Audit every source, measure volumes, classify by security value, and map the flows. Only then evaluate which platform best serves your needs.

Deploy the Pipeline First

The pipeline should be deployed before the migration begins, not as part of it. This allows the team to gain operational experience, optimise data flows, and realise cost savings before adding the complexity of a platform transition.

Plan for Coexistence

The most successful migrations plan explicitly for a coexistence period where both platforms operate in parallel. This period should be long enough to validate detection parity, retrain the SOC team, and build confidence. Rushing through coexistence to save on dual licensing almost always creates problems.

Invest in Detection Engineering

Migration is an opportunity to rationalise detection content. Rather than blindly translating every legacy rule, review coverage against current threat models, retire rules that no longer add value, and build new detections that exploit the new platform’s capabilities.

Think Beyond the Migration

Treat the pipeline as permanent architecture, not a temporary migration tool. Post-migration, it provides data optimisation, vendor independence, and the flexibility to adopt new analytics capabilities as the market evolves.

9. Conclusion

SIEM migration is no longer optional for many organisations. Cost pressures, cloud transformation, and the evolving threat landscape are forcing security teams to modernise their monitoring infrastructure. But migration does not have to mean risk.

By adopting a data pipeline approach, organisations can decouple their security data from any single vendor, maintain continuous coverage throughout transition, reduce costs, and build an architecture that is resilient to future change. The four-phase framework presented in this whitepaper provides a proven path from legacy to modern SIEM infrastructure.

The question is no longer whether to migrate, but whether to do so with a safety net. A telemetry pipeline provides that safety net.

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About Apto Solutions

Apto Solutions is a specialist in security platform management and SIEM migration. Our Operate managed service provides expert-led platform management, migration support, and continuous optimisation of security data infrastructure across financial services, critical national infrastructure, and enterprise environments.