Cyber blind spots: The war room needs constant data, not a daily scorecard

The Speed Problem

Imagine a battlefield commander who receives situation reports only once a day. By the time the picture reaches the operations center, it is already history. Decisions made on that data are not strategy, they are archaeology.

This is not a hypothetical failure mode. In one form or another, it describes how much of the Department of Defense currently manages its cyber intelligence: collecting activity data across its vast enterprise, then analyzing it on a schedule that no longer matches the pace of the adversary.

The time between discovering a digital vulnerability and when an adversary begins exploiting it has collapsed from weeks to days. Defense cyber teams are being asked to make rapid decisions with infrastructure designed for deliberate, scheduled review. The tools exist. The people are skilled. But the underlying architecture, the way data is gathered, stored, and made available for analysis, was built for a more methodical threat environment that no longer exists.

How the Current Architecture Was Built and Why It Made Sense

The event-centric approach that anchors most of DOD’s cyber monitoring infrastructure was a genuine leap forward when it was designed. It gave the department something it never had before: a centralized place to collect activity signals from across its networks, user behavior, system alerts, and traffic patterns, and correlate them into an actionable threat picture. Before that, defending a complex enterprise meant reconciling dozens of disconnected data streams that never quite talked to each other.

For some time, the cyber monitoring architecture worked. It was accredited at the impact levels required for classified environments, integrated into major procurement vehicles, and proven at operational scale across the services and combatant commands.

But the architecture reflects the assumptions of its era. Threats moved relatively slowly. Data volumes were manageable. And the design made a foundational choice: it coupled storage directly to cost. The more activity data an organization kept, the more it paid. For years, that tradeoff was acceptable.

That era is over. Today’s enterprise environments generate activity data at a scale that makes selective retention not just a budget decision, but a mission-critical gamble.

What “Seeing Less” Actually Costs the Mission

More often than not, decisions about what data to discard are not made by analysts weighing mission risk, but by pricing consumption models. Consequently, when a cyber team is forced to filter what it retains because full retention is too expensive, it creates blind spots. Adversaries who carefully study our architecture know exactly how to exploit these blind spots.

Modern threat actors do not announce themselves. They move laterally through defended networks over weeks or months leaving faint traces across systems that are often watching different things at different times. Reconstructing an intrusion, which is how most significant breaches are ultimately understood, requires historical, full-fidelity data that today’s architecture penalizes organizations for keeping. By the time an investigation begins, the evidence may already be gone.

The Emerging Alternative: A Data Fabric Architecture

A data fabric architecture is a fundamentally different approach that has gained traction in the commercial sector because it inverts the economics of the current model. Instead of paying to store activity data and then analyzing whatever survived the budget filter, this approach separates storage from computation entirely. Organizations can afford to retain everything because the cost of analysis scales with findings, not with the volume of data stored.

In a C2 environment, that synthesis is not merely a backend function, it is operational. A data fabric transforms retained telemetry into a low-latency decision pipeline, converting raw activity data into mission-ready intelligence at the speed commanders actually need. Unlike architectures that analyze data only after it has been stored, a data fabric incorporates streaming compute layers capable of processing high-velocity data in flight, surfacing threats and anomalies the moment they emerge, not in after-action reports or scorecards.

For DOD, the implications extend well beyond cost savings.

The most valuable data for cyber defense is often not traditional security data at all. It is the combination of network activity, user behavior, operational tempo signals, and mission system telemetry that together reveal anomalies that no single data stream would surface in isolation. A data fabric architecture makes that synthesis possible. Current event-centric architectures, built around siloed collection points and proprietary analytical systems, make such analysis structurally difficult.

Another benefit of the data fabric architecture is that it keeps data in open, interoperable formats; DOD owns its data and its analytical methods, not the vendor. When the mission evolves, when contracts recompete, when threats change shape, that ownership is what allows the DOD to adapt without starting over. Further, governance and access controls are built in from the ground up, mapping naturally onto existing Zero Trust requirements and the Risk Management Framework. 

This foundation also operationalizes AI at mission scale. Structured data pipelines, labeled datasets, and continuous model lifecycle management give commanders not just reactive detection, but predictive analytics: the ability to anticipate adversary behavior before an attack materializes, not reconstruct it afterward. Autonomous agents monitor, triage, and escalate continuously without waiting for a human query.

The Market Is Already Moving

The shift is not theoretical. It is measurable in contract types, vendor consolidation, and the movement of technical talent. Major federal civilian agencies have recently committed nine-figure investments specifically requiring data fabric architecture for enterprise modernization. Defense-adjacent agencies are actively evaluating it. Acquisition vehicles are being structured around it.

Perhaps most telling is that the engineers who built the intellectual foundations of the current generation of event-centric monitoring tools are now designing their successors on the data fabric model. When the architects of a platform move on to build its replacement, that is not a market signal. It is a verdict.

The Pragmatic Path: Architect Forward, Don’t Abandon What Works

None of this is an argument for tearing out accredited, operational infrastructure. Any advisor who tells a program executive to discard a functioning, authorized system in favor of an emerging model is prioritizing ideology over mission continuity. The responsible path is not replacement, but rather an architect-forward posture.

Treat net-new programs, greenfield Security Operations Center builds, and major contract recompetes as the insertion points for data fabric architecture. Maintain existing event-centric investments in currently accredited environments while building the integration layer that bridges the two. The goal is a deliberate migration sequenced to the natural rhythm of the acquisition cycle.

Acquisition language matters enormously. Program offices that require open-format data portability, vendor-agnostic analytical frameworks, and AI-ready architectures as pass/fail evaluation criteria will shape the market before a contract is ever awarded.

A Call to Architectural Leadership

The Department of Defense does not lack cyber tools or talented analysts. What it lacks is an architectural framework that matches the speed and sophistication of the adversary it faces.

A data fabric is not a product to be purchased off a schedule. It is a strategic design decision that determines what DOD can see, how fast it can act, and whether its analytical investments compound into lasting capability or degrade in isolation.

The adversary has already made its architectural choices. The question is whether we do this on our terms, or whether a future breach makes the decision for us.

The war room needs a data fabric. The architecture exists. The mission demands it.

Collin Lee is the chief information officer of Omni Federal.