Detection engineers are some of the most skilled practitioners in cybersecurity. They understand attacker behavior, log pipelines, query languages, and the operational realities of a SOC. And yet, many of them are burning out, falling behind, and losing confidence in the work they produce.
This is not a talent problem but rather a structural one.
Detection engineering sits at the center of competing demands from every direction: threat intelligence, threat hunting, SOC operations, and red teams. Each function has legitimate needs. Each function points at detection engineering when something goes wrong. And none of them are coordinated with each other.
The result is a discipline being pulled apart from all sides, with no clear way to win.
The Detection Engineering Paradox
At its core, detection engineering has one job: ensure the organization can detect the threats it faces. Simple in theory. Devastating in practice.
The problem is not a lack of effort. Most detection engineering teams are working at or beyond capacity. The problem is that the demands placed on them are structurally incompatible with the resources and tooling available. Every stakeholder believes their request is the priority. Every gap feels urgent. And detection engineers are left building, validating, and maintaining a library of rules that is never complete, never fully trusted, and never quiet.
Threat Intelligence: The Coverage Demand Machine
Threat intelligence teams exist to understand what adversaries are doing and ensure the organization is prepared. In practice, this means one recurring question directed at detection engineering: do we have coverage for this?
When a new CVE drops, when a threat actor group is attributed to an attack in the same industry, when a new TTP surfaces in a threat intel feed, the question flows downstream. Does a detection rule exist? If not, build one. If one exists, is it working?
This is a reasonable expectation on its own. The problem is volume and velocity. Threat intelligence is constantly producing new requirements. Detection engineering is expected to absorb them, prioritize them, and ship coverage without a clear view into what already exists, what is actually working, or what the current backlog looks like.
In platforms like Sumo Logic SIEM and Datadog Cloud SIEM, teams are managing hundreds or thousands of detection rules. Without a systematic way to map threat intelligence requirements to existing coverage, teams duplicate work, miss gaps, and make promises they cannot keep.
Detection-as-Code (DaC) frameworks have emerged as one approach to bring structure to this problem, treating detection rules like software with version control, testing, and deployment pipelines. But even with DaC, the coverage mapping problem remains largely manual.
Threat Hunting: The Backlog Generator
Threat hunters operate in the gaps. They look for evidence of compromise that existing detections miss, which by definition means they are surfacing detection engineering failures.
A mature threat hunt produces two things: a finding and a recommendation. The finding might be a gap in visibility. The recommendation is almost always a new detection rule.
The problem is that threat hunting teams are rarely positioned to write production-quality detection logic. What gets handed to detection engineering is often a rough query written in a hunting notebook — not validated against production data volumes, not tested for performance, and not checked against the existing rule library to see if coverage already exists.
That rule lands in the detection engineering backlog alongside the threat intel requests, the SOC escalations, and the red team findings. It may sit there for weeks. By the time a detection engineer picks it up, the context behind the hunt has evaporated and the work has to start from scratch.
In Sumo Logic and Datadog environments, this problem is compounded by the complexity of writing performant queries at scale. A rule that works fine in a hunting notebook can cause serious performance degradation in a production SIEM if it is not properly tuned. Detection engineers carry that optimization burden alone.
SOC Analysts: The Signal-to-Noise Crisis
While threat intelligence and threat hunting are pushing for more coverage, SOC analysts are pushing back on the coverage that already exists.
Alert fatigue is not a new problem, but it has reached a breaking point in many organizations. Analysts are triaging queues filled with alerts they do not trust. They have learned through experience which rules fire constantly and rarely produce anything actionable. They build mental models around which detections to take seriously and which ones to process and close without investigation.
This is a detection engineering problem that never gets fully surfaced. The analyst does not write a ticket every time a noisy rule fires. They absorb the cost silently until morale collapses or headcount escalations get forced.
When SOC analysts do escalate, the message to detection engineering is blunt: these rules are broken, we are drowning in false positives, fix this. But detection engineering is simultaneously being told by threat intelligence and threat hunting to add more coverage. The two demands are in direct conflict, and there is no system to arbitrate between them.
Detection-as-Code practices can help here by building automated testing and validation into the detection pipeline, catching rules that would generate unacceptable noise before they ever reach production. But adoption of DaC in enterprise environments running Sumo Logic SIEM or Datadog Cloud SIEM is still far from universal, and even teams practicing detection-as-code often lack the tooling to continuously validate rule quality against live production data.
Red Teams: The Broken Rule Revelation
Red teams exist to test whether defenses actually work. For detection engineering, this means periodic adversarial simulation exercises that answer a simple question: would we catch this attack?
The answer is often no.
Red teams find detection rules that are broken for reasons that have nothing to do with the original rule logic. Log formats change. Data sources get reconfigured. A pipeline update shifts a field name. An endpoint agent gets updated and the telemetry schema looks different. No one updated the detection rule. No one even knew the drift happened.
This is one of the most insidious problems in detection engineering. A rule can exist, pass its original tests, live in the rule library as "active," and still fail to detect the exact threat it was built for. In large environments with hundreds of active rules across platforms like Datadog Cloud SIEM and Sumo Logic SIEM, log schema drift is constant and largely invisible.
The red team finds this during a simulation. But the rule was broken long before they showed up. The real question is: how long was the organization blind, and how many other rules have the same problem?
There is no easy answer without continuous validation. Spot-checking is too slow. Manual review does not scale. And yet most detection engineering teams have no automated mechanism to confirm that active rules are performing as intended against current data.
The Center Cannot Hold
Put all four stakeholders together and the picture becomes clear.
Threat intelligence wants more coverage. Threat hunting surfaces gaps and hands over rough, unvalidated rules. SOC analysts are drowning in false positives and want fewer, better detections. Red teams are finding that existing rules are silently broken.
Detection engineering sits in the middle of all of this with no unified view of coverage, no automated validation, no clear prioritization framework, and no way to communicate the state of detection health to leadership or stakeholders.
This is not a workflow problem that a better ticketing system will solve. It is a systemic gap in how detection engineering operates as a function.
The industry has started addressing pieces of this with Detection-as-Code (DaC), bringing software engineering discipline to rule development and lifecycle management. Platforms like Sumo Logic, Sumo Logic SIEM, Datadog, and Datadog Cloud SIEM have invested in improving rule management and detection coverage tooling. But the connective tissue between these stakeholders is still missing.
What detection engineering teams need is not more rules. It is continuous validation that existing rules are working, a clear map of coverage against the threats the organization actually faces, and a feedback loop that connects SOC signal quality back into the detection engineering workflow automatically.
Until recently, that infrastructure did not exist. But the argument that detection engineering is permanently impossible does not hold up under scrutiny, because the data required to solve every one of these problems is already there.
This Problem Is Solvable
Look at what surrounds a detection engineer on any given day: threat intelligence feeds mapping adversary TTPs to the MITRE ATT&CK framework, alert metadata showing which rules fire and how often analysts dismiss them, threat hunting queries that represent the best thinking on where gaps exist, massive volumes of log data and schema metadata across every source in the pipeline, a backlog of detection requests with varying priority and staleness, and red team and pen test results that reveal exactly which rules are broken and why.
The data is not missing. It is scattered, unstructured, and too voluminous for any human team to synthesize manually. But it is all there, and the problems it maps to are well-defined.
Coverage mapping is solvable. Ingest threat intelligence, map it against the existing rule library, and answer the coverage question before a human has to ask it. Alert quality is solvable. Analyze SOC alert data to identify which rules are generating noise and which are producing real signal, and feed that quality assessment back into the detection pipeline continuously. The hunting handoff is solvable. Take the rough queries from threat hunters, validate them against production data volumes, check for overlap with existing rules, and surface only the net-new coverage gaps worth building. Rule drift is solvable. Monitor log schemas and data pipelines in real time, flagging rules that have fallen out of alignment with the telemetry they depend on before a red team has to discover the failure during a simulation.
None of this requires replacing detection engineers. It requires giving them a system that handles the synthesis, validation, and continuous monitoring that no human team can sustain at scale. Detection-as-Code gave detection engineering the discipline of software development. The next step is the operational capacity to actually keep pace with the demands being placed on it.
Detection engineering is not a permanently impossible job. It just needs the right platform underneath it.
That is what Rilevera is building. If detection engineering is a strategic priority for your organization, we should talk.
