SIEM detection gaps are a routing problem, not a data problem

At a glance

What this is: This analysis argues that SIEM detection gaps stem from routing, normalization, and enrichment failures rather than insufficient log volume.

Why it matters: It matters to IAM and security teams because identity, cloud, and endpoint telemetry decisions determine whether access abuse, credential misuse, and lateral movement are visible at all.

By the numbers:

• The organizations in that sample were already ingesting enough telemetry to potentially cover 90% of MITRE ATT&CK techniques.
• Their actual coverage was 22%.
• The same study found 10% of deployed rules were broken and would never fire under any circumstances.
• In the SANS 2025 Detection and Response Survey, false positives were the top detection challenge at 73%.

👉 Read Gurucul's analysis of why SIEM detection gaps are a routing problem

Context

SIEM coverage gaps are often treated as an ingestion problem, but the deeper issue is operational: raw telemetry has to be routed, normalized, enriched, and mapped to actual detections before it becomes useful. For identity teams, that same logic applies to authentication logs, privilege events, and workload telemetry, where volume alone does not create visibility.

The article’s central claim is that most enterprises already collect enough data to support far more detections than they realize. The governance gap is in how security data is filtered and contextualised, which is why the identity angle matters for NHI, access control, and account abuse monitoring.

Key questions

Q: How should security teams improve SIEM coverage without simply ingesting more data?

A: Start by mapping each data source to the detections it actually enables. Then route low-value telemetry to cheaper storage, deduplicate repetitive events, and enrich identity and cloud logs before analytics. Coverage improves when the pipeline is aligned to detection content, not when volume increases for its own sake.

Q: Why do organisations still miss attacks even when they collect plenty of telemetry?

A: Because collection is not the same as detection readiness. Raw events often arrive unnormalized, under-enriched, or disconnected from the rules that need them. If the command line, identity context, or cloud semantics are missing, the SOC can pay for the log and still not see the attack pattern.

Q: What do security teams get wrong about log management?

A: Teams often treat logging as a data plumbing task and overlook the identity controls around it. The common mistake is focusing on volume and format while ignoring who can read, purge, or export logs. That turns the log platform into a high-value target with weak governance.

Q: How can SOC teams measure whether SIEM modernisation is working?

A: Use operational measures, not just deployment status. Track time to search, time to correlate, number of identity-relevant detections, and whether the platform can support investigations without workarounds. If analysts still export data into side tools to understand access abuse, modernisation has not yet delivered the intended value.

Technical breakdown

Why SIEM ingestion volume does not equal detection coverage

Detection coverage depends on whether the right fields survive collection, parsing, normalization, and enrichment. A log source can be counted in ingest totals while still being unusable for a rule because the command line, identity context, or event semantics never arrive in a queryable form. That is why organisations can pay for more telemetry and still miss common ATT&CK techniques. The practical problem is not just data volume. It is the mismatch between what is collected and what detection content actually consumes.

Practical implication: map each high-volume source to the detections that depend on it before asking for more ingestion capacity.

How routing and filtering change the economics of SIEM data

Routing is different from dropping data. A mature pipeline can keep high-value telemetry in the analytics path, send low-signal repeats to lower-cost storage, and preserve searchability for hunting and forensics. That matters because many environments overpay for data that no rule uses, while underfunding the sources that actually support execution, credential access, and cloud abuse detections. The technical point is simple: the collection layer should decide destination by source value, not by a one-size-fits-all retention policy.

Practical implication: separate route, deduplication, and drop logic so expensive analytics only receive telemetry with detection value.

Why enrichment is the control that makes identity telemetry usable

Identity events become more valuable when they carry context such as user, role, asset class, and threat reputation. Without enrichment, an authentication success or cloud API call is just a record. With enrichment, it becomes a signal that can support behavioural baselines, privilege anomaly detection, and NHI abuse monitoring. This is especially important for IAM and NHI governance, because service accounts, tokens, and API keys often generate technically valid events that still represent unacceptable access patterns.

Practical implication: enrich identity and cloud logs before analytics so detections can distinguish expected machine activity from abuse.

Threat narrative

Attacker objective: The attacker’s objective is to operate inside telemetry blind spots long enough to conduct credential abuse, privilege escalation, and lateral movement without triggering effective detection.

1. Entry begins with high-volume telemetry sources that are left unfiltered or misrouted, allowing attackers to hide activity inside data the SOC cannot meaningfully query.
2. Escalation follows when detections depend on fields that were not normalized or enriched, so credential abuse, privilege misuse, and execution activity do not match the intended rules.
3. Impact occurs when broken rules and blind spots leave the organisation with costly telemetry but limited ability to detect ATT&CK-aligned intrusions in time.

NHI Mgmt Group analysis

Coverage without usable routing is a false sense of control. Organisations often interpret ingest volume as maturity, but this article shows that data can exist in abundance while detections remain sparse or broken. That gap is especially relevant in identity-heavy environments where authentication, access, and workload logs must be normalized before they can support monitoring. The practitioner lesson is to measure detection readiness by source usability, not by raw terabytes.

Identity telemetry is only valuable when it is enriched before analysis. IAM and NHI logs are noisy by default, but the answer is not blanket reduction. Identity, privilege, and cloud context are what turn valid-looking events into actionable signals about abuse or anomaly. In practice, this means treating enrichment as part of the control plane, not as a downstream reporting step.

Detection engineering backlog is now a governance issue. The article exposes a control gap that sits between security architecture and operational reality: teams can own the logs, yet still fail to operationalise them. That is a governance failure because the organisation cannot demonstrate that collected telemetry supports the threats it claims to monitor. Practitioners should treat unused telemetry as an assurance problem, not a storage problem.

Telemetry pipelines are becoming policy enforcement points. Once routing decisions determine what reaches SIEM, the pipeline effectively shapes the organisation’s detection surface. That creates a need for explicit policy, review, and evidence of what was routed, deduplicated, or dropped. For IAM and NHI programmes, the same logic should apply to authentication and privilege logs because blind spots there are indistinguishable from control failure.

ATT&CK coverage should be validated against the sources that actually matter. A coverage percentage is meaningless if the underlying sources are low value or if critical identity and cloud events are filtered away. The more useful approach is to tie detection content to the log classes that support credential access, discovery, lateral movement, and impact techniques. The practical conclusion is to test coverage against real adversary paths, not marketing dashboards.

What this signals

Detection coverage is now a pipeline governance issue. Security teams should expect more scrutiny over how telemetry is routed, normalized, and enriched before it ever reaches SIEM or data lake analytics. The operational question is no longer whether more data exists, but whether the programme can prove that collected data supports the detections it claims to run.

Identity telemetry needs context to become defensible signal. IAM, NHI, and cloud access logs should be treated as high-value inputs that earn enrichment, not as raw records to be retained by default. Practitioners should align pipeline policy with ATT&CK coverage, then validate that service-account, API key, and cloud control plane events still support hunts after optimisation.

ATT&CK mapping is only useful when it matches the sources you keep. Teams should review whether their highest-volume logs are also their highest-value logs, then move low-signal noise out of the analytics tier without losing searchability. A resilient programme proves that reduction did not create blind spots in credential access, execution, or lateral movement detection.

For practitioners

• Audit detections against source usability Inventory the top log sources, then map each one to the rules and ATT&CK techniques that depend on it. Remove sources that contribute cost but no detection value, and preserve sources that support credential access, execution, and cloud abuse detections.
• Separate routing from dropping Use deduplication and conditional routing to move low-signal telemetry into lower-cost storage while keeping a searchable copy for hunts and investigations. Reserve true drop actions for repetitive scanner, heartbeat, or health-check traffic.
• Enrich identity logs before analytics Attach identity, asset, entitlement, and threat context at the pipeline layer so authentication and cloud events arrive ready for behavioural analysis. This is especially important for service accounts, API keys, and other NHI telemetry that looks legitimate until context is added.
• Measure broken detections continuously Track the percentage of rules that never fire, the sources they depend on, and the volume that reaches each destination. Per-source reduction reporting is more useful than aggregate savings because it shows whether coverage improved or simply became cheaper.

Key takeaways

• SIEM gaps often reflect routing and enrichment failures, not a lack of logs.
• Enterprises can spend heavily on telemetry and still leave critical ATT&CK techniques under-covered or broken.
• The practical fix is selective routing, identity enrichment, and continuous validation of what the detections actually consume.

Key terms

• Telemetry pipeline: A telemetry pipeline is the path security data follows from collection to analysis and retention. In mature environments it must preserve context, maintain throughput, and avoid introducing blind spots as sources, formats, and volumes change over time.
• Detection Coverage Analysis: The process of mapping which attacker techniques are well covered, thinly covered, or completely uncovered by current detections. In practice, it turns detection engineering into a measurable input for hunting, letting teams rank what to investigate next instead of guessing.
• Enrichment: Enrichment adds contextual data to raw logs, such as asset ownership, identity attributes, geography, or threat intelligence. It reduces analyst effort by turning isolated events into more complete investigation records and helps automation make better decisions from the same underlying telemetry.
• Deduplication: Deduplication is the process of identifying repeated applicants or identities across programmes so the same person or entity is not approved multiple times without detection. It is a fraud and governance control that helps expose synthetic identity patterns, reuse, and hidden overlap across customer populations.

What's in the full article

Gurucul's full blog covers the operational detail this post intentionally leaves for the source:

• Per-source reduction logic for common telemetry classes, including cloud control plane, Windows Security, DNS, firewall, and proxy logs
• The six-stage pipeline sequence showing where optimization sits before normalization and enrichment
• Examples of how routing, deduplication, and drop behave differently in live log streams
• Source-level statistics that show which telemetry classes contribute most to cost reduction and preserved coverage

👉 The full Gurucul article includes the source-level reduction logic and log routing examples behind the coverage argument.

Deepen your knowledge

NHI Mgmt Group's 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 controls to broader security operations and programme assurance.