A Familiar Pattern with a New Twist
Shadow IT was once an existential threat to enterprise security architecture. Employees and departments would adopt unsanctioned SaaS tools outside the purview of IT, creating blind spots and unmanaged risk. The industry responded with discovery tools, governance policies, and architectural patterns to bring these tools into the light.
Today, we face a similar phenomenon, but it doesn’t come from unauthorized adoption. It comes from features and capabilities that were delivered by design. We’re talking about Shadow SIEMs: the detection and alerting engines embedded across modern security products, often invisible to centralized workflows, and dramatically underutilized by security teams.
Unlike Shadow IT, Shadow SIEMs aren’t a sign of rogue behavior. They’re a sign of market evolution. Every major vendor in cloud, endpoint, identity, and network security is now building SIEM-like features into their platforms. These features are sitting in production environments, generating signals, and issuing alerts, but are rarely integrated into the broader security operations picture.
To understand how this happened, we have to revisit the history and purpose of SIEM itself.
What SIEM Was Supposed to Be
SIEM began as a model: Security Information and Event Management. It promised a centralized system where logs, alerts, and events from across the infrastructure could be correlated, triaged, and responded to.
But the model wasn’t just about technology. At its best, SIEM aligned people, process, and technology. It provided a hub for security analysts to make sense of what was happening. It supported repeatable workflows. And it gave organizations a common source of truth for threat detection and response.
Over time, however, SIEM became a product category. As vendor competition intensified, the focus shifted to ingestion rates, retention periods, and feature parity. The people and process side of SIEM took a backseat. And worse, the assumption grew that a single centralized system was the right place to send every piece of data, even though most teams centralized a small percentage of the total security-relevant data.
Today, large IT and security platform players are betting on SIEM features to drive adoption and grab more of each customer’s security budget.
Shadow SIEMs: The New Reality
A Shadow SIEM isn’t a rogue tool. It’s a detection, alerting, or analytics engine embedded inside a security or IT platform that mimics the capabilities of a traditional SIEM, but operates in isolation from the broader SOC workflow.
They emerge naturally from modern technology architectures:
- Microsoft Sentinel and Defender offer cloud-native detection and response for identity, email, and endpoint telemetry.
- CrowdStrike includes alerting, dashboards, and log ingestion under the banner of Next-Gen SIEM and CrowdStrike Falcon LogScale (formerly Humio).
- Palo Alto Networks’ Cortex and Prisma deliver detections and incident response views across network and cloud via Cortex xSIAM.
- AWS Security Lake, Google SecOps (formerly Google Chronicle), and Snowflake-powered solutions now offer log storage, correlation, and alerting capabilities traditionally associated with SIEMs.
These platforms often generate high-fidelity detections in real time. But those detections frequently remain siloed within their originating platforms. They are not correlated, enriched, or responded to in the same way as alerts from the primary SIEM.
This fragmentation creates operational, architectural, and strategic challenges.
The Hidden Costs of Shadow SIEM Sprawl
On the surface, Shadow SIEMs seem like a net benefit. More signal. More detections. More tools doing more work.
In reality, they introduce a host of new problems:
- Investigations fragment across tools: Analysts must pivot across multiple consoles, both the Shadow SIEM and disparate enrichment tooling, to investigate a single incident.
- Detection engineering becomes siloed: Teams must write and maintain separate rules in incompatible query languages.
- Response playbooks must be duplicated: SOAR workflows become brittle as they stretch to accommodate every new source.
- Visibility is inconsistent: Alert logic varies across platforms. Some sources are missed entirely.
- Context is incomplete: Alerts don’t come with asset, user, or threat intelligence enrichment unless manually added.
- AI and LLMs can’t help: Without unified access to context, AI copilots remain blind to large parts of the security stack.
The result is slow response, alert fatigue, detection blind spots, and wasted effort.
More frustrating still: the organization has already paid for these capabilities. The logs are being collected. The detections are firing. But the security team can’t use them efficiently.
Why Centralized SIEMs Can’t Solve This Alone
The traditional response to fragmentation was always the same: send everything to the central SIEM. But this approach is no longer viable.
- Ingestion costs are prohibitive: Petabyte-scale storage pricing can easily double every year, according to some, data is growing at a 28% CAGR.
- Latency is built in: Log ingestion pipelines introduce delays in detection and response.
- Data gravity is real: Moving logs across clouds or borders introduces compliance and sovereignty risks.
- Pipeline maintenance is painful: Each new data source requires normalization, mapping, and transformation.
- Vendor lock-in persists: Once your logic lives inside a single SIEM, migration becomes nearly impossible.
Even the SIEM vendors know this. That’s why they’re investing in federation, cloud-native search, and external connectors. But these are patches on an outdated architecture.
The Federated Security Model: Turning a Liability into Leverage
There is a better path forward. One that doesn’t require re-centralizing the world, but instead acknowledges that modern security is distributed by design.
Federated Security is an operating model that brings your search, detection, and response workflows to the data, wherever it lives. Instead of piping data into a central location, federated tools let analysts query distributed sources, normalize results on the fly, and work across systems as if they were one.
This model turns Shadow SIEMs from a burden into a strategic data advantage:
- You access more relevant data without ingesting more of it.
- You reduce analyst pivots and investigation time.
- You lower storage costs while expanding detection coverage.
- You normalize alerts from multiple platforms without rewriting every rule.
- You enable AI tools to operate on a full picture of activity, not just one slice.
In short, you stop duplicating work and start driving effective security outcomes.
What Happens When You Federate
We’ve helped enterprises cut Splunk storage by hundreds of terabytes (and realize significant savings) simply by shifting cold data to S3 and querying it in place. We’ve watched SOCs investigate alerts across Microsoft, Okta, AWS, and CrowdStrike Falcon, without waiting for new pipelines to be built. We’ve seen teams respond to zero-days in hours, not days, because they could correlate indicators across cloud and on-prem without missing context.
Federation doesn’t mean eliminating your SIEM. It means evolving your architecture to acknowledge reality: that the data you need is already in multiple places, and the only way to win is to make it usable, fast.
How to Make Shadow SIEMs Work for You
Shadow SIEMs aren’t going away. In fact, they’re going to multiply. Every new platform will continue to add its own detection and analytics engine.
The question is: will you control this sprawl, or will it control you?
Here’s where to start:
- Map your Shadow SIEM landscape: Inventory the platforms that generate alerts or contain searchable telemetry, and understand their data sources and volumes.
- Evaluate your pivots: Where do your analysts waste time switching tools?
- Audit duplicated logic: Where are rules, playbooks, or dashboards recreated across systems?
- Identify hidden costs: Storage, licensing, and operational overhead from ETL-heavy architectures.
- Build a federated foundation: Choose tools that bring search, enrichment, and response to your data, not the other way around.
It’s Time to Use What You Already Have
The average enterprise has made massive investments in telemetry, detections, and security tooling. But without a federated model, much of that investment sits idle, underused, under-integrated, and unavailable when needed most.
Shadow SIEMs are not the enemy. They are an opportunity.
Federated Security is how you seize it.
