Security data pipelines can cut Splunk ingest costs and lock-in

At a glance

What this is: This is an analysis of how security data pipelines can reduce SIEM ingestion costs, preserve detection value, and avoid schema lock-in.

Why it matters: It matters because IAM, NHI, and broader security teams all depend on telemetry quality, retention discipline, and stable data models to investigate access, privilege, and abuse patterns effectively.

By the numbers:

• 30-50% reduction

👉 Read Axoflow's analysis of Splunk ingestion costs and security data pipeline design

Context

Security data pipelines exist because most organisations still push every log source directly into the SIEM and then pay to normalise, store, and search that noise at SIEM prices. In practice, the problem is not just volume but control: without an upstream model for collection, classification, and schema ownership, teams turn a detection platform into an expensive archive, and that weakens the quality of investigations across IAM, NHI, and cloud activity.

The core issue here is telemetry governance. When data is transformed only inside the SIEM, the organisation inherits vendor-specific schema rules, expensive retention decisions, and repeated parsing work whenever sources change. That creates technical debt in the security programme itself, which is why the schema question is not just a SOC concern but also a broader identity and access governance issue where auditability depends on clean, reliable event data.

Key questions

Q: How should security teams reduce SIEM ingestion costs without losing detection value?

A: Teams should move collection, classification, and normalisation upstream so the SIEM receives only the data needed for detection and investigation. That means defining retention tiers, dropping low-value noise, and preserving full-fidelity events for cases that support forensics, compliance, or identity investigations. Cost control works when it is a governance decision, not a storage cleanup exercise.

Q: When does schema normalisation become a lock-in risk for security teams?

A: Schema normalisation becomes lock-in risk when detection logic, parser rules, and dashboards are built around a SIEM-specific model and cannot move without major rework. The safest approach is to keep the canonical schema outside the SIEM and treat the analytics platform as a consumer, not the owner, of the data model.

Q: What do security teams get wrong about log reduction tools?

A: They often expect automatic savings without first understanding the source data. Real reduction requires knowing which fields have forensic value, which events are repetitive, and which sources are already covered elsewhere. If those decisions are made blindly, teams save storage but also strip away the evidence they need later.

Q: What should teams do first before extending log retention for years?

A: They should calculate the full storage footprint, then decide which event classes truly justify long-term retention. For identity and security teams, the key question is whether the data will be searched, correlated, or only preserved for compliance. If it will not support a real use case, it should not be stored at premium cost.

Technical breakdown

Why upstream normalisation reduces SIEM cost pressure

A security data pipeline sits between sources and the SIEM to collect, classify, reduce, and normalise events before they become expensive downstream storage. The architectural point is that the pipeline can decide what belongs in hot search, what should be retained in cheaper tiers, and what is noise. That shifts cost control left. If every source feeds raw logs directly into the SIEM, the SIEM becomes both the transformation engine and the archive, which is inefficient and difficult to govern over time.

Practical implication: establish upstream filtering and tiering rules before data reaches the SIEM.

How schema lock-in happens with CIM, ECS, and similar models

Schema lock-in occurs when detection logic, parsers, and field mappings are built around one vendor's data model, such as CIM or ECS, and then become hard to move elsewhere. The issue is not schema use itself, but letting the SIEM own the organisation's canonical security data shape. Once that happens, changing analytics platforms means rebuilding ingest logic, correlation rules, and dashboards. A vendor-agnostic format like OCSF helps separate data structure from analytics tooling.

Practical implication: define a canonical security schema outside the SIEM and map vendors to it.

Why reduction workflows still need human knowledge

Data reduction is not a switch you flip. The team must understand which fields, sources, and event types carry forensic value and which are operational noise. That is why reduction tools often deliver their savings only after engineers spend time learning source behaviour and tuning filters. The hidden cost is not just tooling, but the knowledge work needed to avoid dropping useful evidence. Good pipelines encode that knowledge into defaults and policy, rather than leaving every organisation to rediscover it manually.

Practical implication: tune reduction policies by source risk and investigative value, not by volume alone.

NHI Mgmt Group analysis

Security telemetry is now a governance problem, not just a storage problem. When organisations treat the SIEM as the place where data is fixed, shaped, and retained, they also turn telemetry quality into a downstream tax. That tax affects visibility into identity abuse, privilege misuse, and NHI behaviour because investigation quality depends on the structure of the data before it reaches the search layer. Practitioners should treat telemetry governance as part of security architecture, not a back-office logging task.

Schema lock-in is a form of security technical debt. Once detection content depends on proprietary field models, teams inherit switching costs that outlive the original procurement decision. That matters for identity programmes because audit trails, access reviews, and privilege investigations all rely on consistent event semantics. A canonical upstream schema such as OCSF reduces the chance that analytics tooling becomes the owner of the data model. Practitioners should separate security data semantics from SIEM product choice.

Selective ingestion is the only sustainable response to retention pressure. The article's storage math shows why compliance retention cannot be solved by brute force collection. If every source is retained at full fidelity for years, organisations pay for scale they rarely query and often cannot operationalise. That is especially relevant for identity and access data, where high-value events need precision more than volume. Practitioners should align retention tiers to investigative value, not to an assumption that more logs are always safer.

Operational observability and security evidence are not the same thing. Many teams collect far more than they can use because they have not defined which events support detection, which support forensics, and which support compliance. That distinction becomes critical when IAM and NHI telemetry feed incident response, because undifferentiated data inflates cost while still failing to answer the questions investigators actually ask. Practitioners should classify telemetry by purpose before deciding where it belongs.

What this signals

Security teams should expect log economics to keep pushing architecture decisions upward into governance. The organisations that separate telemetry semantics from SIEM procurement will have more flexibility to change platforms, reduce cost, and keep investigations usable as sources evolve.

Telemetry control plane: this is the operating model where collection, classification, and retention rules are defined before data reaches the SIEM. For identity-rich environments, that model matters because access and privilege evidence is only useful when the underlying event structure is stable enough to trust.

For programmes that rely on IAM and NHI investigations, the key shift is to treat logs as curated evidence rather than universal exhaust. That aligns better with NIST Cybersecurity Framework 2.0 and with the practical need to preserve the specific signals that matter most in incident response.

For practitioners

• Implement upstream schema ownership Define a canonical security schema outside the SIEM and map source systems into it before data reaches analytics. This reduces product dependence and preserves portability when you change platforms.
• Tier retention by investigative value Separate hot, warm, and cold retention based on how often identity, privilege, and detection teams actually query the data. Keep only the high-value event classes at full fidelity for long periods.
• Tune reduction rules by source risk For each log source, document which fields are essential for investigations and which can be dropped safely. Reassess those rules whenever source behaviour or detection requirements change.
• Reduce parser maintenance debt Track repeated re-parsing as an explicit operational burden and prioritise tools or workflows that absorb schema drift without constant manual regex work.

Key takeaways

• The central problem is governance of telemetry, not merely the price of storage.
• Upstream schema ownership and selective ingestion reduce lock-in while preserving investigative value.
• Teams that classify logs by purpose will spend less on noise and more on evidence that actually supports security decisions.

Key terms

• Security data pipeline: A security data pipeline is the chain that ingests, filters, enriches, normalises, and routes telemetry before it reaches storage or analytics. In practice, it determines which evidence survives into detection, investigation, and compliance workflows, so it is part of the control environment, not just infrastructure plumbing.
• Vendor Lock-In: A dependency state where business processes, technical integrations, and identity controls become difficult to move away from without disruption. It is not only a commercial constraint. It also creates governance friction when credentials, APIs, and monitoring workflows are tied too tightly to one provider.
• Telemetry Tiering: Telemetry tiering is the practice of assigning different retention or storage levels to events after assessing their investigative value. It helps organisations preserve important evidence in high-fidelity form while reducing cost and noise for routine or low-value data.

What's in the full article

Axoflow's full article covers the operational detail this post intentionally leaves for the source:

• Concrete examples of how to move from raw log ingestion to upstream classification and normalisation
• Practical guidance on reducing ingestion volume without losing the events needed for investigations
• Discussion of schema portability and how vendor-specific models create long-term rework
• Operational trade-offs involved in long-term retention, cold storage, and queryability

👉 Axoflow's full post covers the storage math, schema lock-in, and reduction trade-offs in more detail

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It is designed for practitioners who need to connect identity control with broader security operations and risk management.