Security teams love coverage maps. MITRE ATT&CK heatmaps, detection matrices, percentages of techniques covered, and dashboards full of green squares implying completeness.
The problem is that detection engineering does not fail because organizations lack detections, but because they rarely make deliberate decisions about where detection matters most.
According to Prophet Security, a leading agentic AI SOC platform following a $30M Series A led by Accel, most SOCs are still operating under the outdated assumption that broad coverage equals strong security. Therefore, teams try to monitor everything, detect everything, and map everything across sprawling attack frameworks.
In reality, a meaningful portion of those techniques may never apply to their environment at all.
A manufacturer is not vulnerable to the same attack vectors that a cloud-native SaaS vendor is. The operating hazards of a hospital are very different from those of a bank. And still, many detection systems continue to seek out-of-the-box solutions based on the assumption that all threats have equal likelihood and consequences.
That is where detection engineering starts breaking down. The culprit isn’t teams lacking effort, but because they fail to make trade-offs explicit.
You Can’t Detect Everything, and Trying is Why You Fail
Detection engineering has become trapped in checklist thinking: ATT&CK coverage, Sigma rule counts, and total detections deployed. These metrics are easy to report, but often disconnected from operational reality.
In practice, broad coverage can create a false sense of maturity while introducing new problems: false positives, analyst fatigue, unmanageable alert queues, and detections built around hypothetical scenarios rather than real attack paths. For many SOCs, detection engineering becomes a rule-writing exercise with little measurable defensive value.
The bigger issue is latency. Organizations rarely ask whether detections are reducing real risk quickly enough. A highly effective detection is of limited value if it takes months to deploy while attack chains, cloud infrastructure, and identity abuse techniques evolve constantly.
Yet many detection engineering programs still operate like legacy software release cycles. By the time some detections go live, the environment they were designed for has already changed.
Coverage and Efficiency are the Two Pillars, and Most Teams Optimize Only One
Effective detection engineering depends on balancing two competing forces, coverage and efficiency.
Coverage asks:
- Are we checking on the right systems?
- Are we looking for real attack vectors?
- Are we identifying attacks that are likely and meaningful for our organization?
- Is there anything happening anywhere where we just don’t have visibility?
Coverage is not about monitoring everything everywhere. It is about understanding where detections matter most.
A smaller set of highly contextual detections tied to identity abuse, privilege escalation, cloud control plane manipulation, or lateral movement may provide vastly more defensive value than hundreds of generic low-context alerts.
Efficiency asks:
- Are our alerts actionable?
- Are our analysts overwhelmed with noise?
- Are our detections leading to valuable leads?
- Are we spending more time analyzing than getting better?
It is here that many detection systems fail from their own success. Too much coverage and not enough throughput lead invariably to one thing: too many alerts and not enough analysis. At that stage, the issue is not any longer the problem of detecting but investigating.
This is why detection quality is as important as detection quantity. A SOC which creates 500 suspicious alerts every day is not necessarily better off compared to one creating 50 high-quality detections linked to specific attack chains.
Both are equally critical, and neither can be neglected. Better detection without better investigation will overwhelm the SOC with too much noise. Better investigation without better detection will leave the SOC open to attack from unexpected sources.
Risk-Based Prioritization is the Missing Layer
This is the part many organizations skip. Detection engineering is not just a technical discipline; it is fundamentally a risk management function.
The question shouldn’t be: “How quickly can we detect everything?” The important question is: “What’s the best timing for detection to optimize response efficiency?”
Timing is key here. In some instances, a quick reaction is necessary due to the inherent dangers of malefactors being allowed to linger within systems. In other situations, a delayed response would be more efficient with greater insights into behavioral context.
Organizations should start thinking in terms of detection quality versus detection latency curves.
In other words, consider what degree of delay is operationally tolerable, and what degree of fidelity is needed before escalating? Also, which attack vectors should a detection response be highly sensitive to, and for what attacks does noise inflict greater harm than latency?
Don’t think of these as design flaws, but questions of strategy. Yet far too many SOC teams fail to address them. They attempt instead to find everything equally, when that usually means finding nothing especially well.
What Effective Detection Engineering Looks Like
Strong detection engineering programs are rarely the biggest, but they are usually the most disciplined.
They share a few characteristics consistently.
For one, they are iterative. Good teams are constantly optimizing their detections based on operational feedback in the real world. They don’t get buried in six-month rule writing efforts that become obsolete before implementation.
They are also focused. They concentrate on detections associated with risk, attacker behavior, and exposures, never theory of perfection.
They are owned. Detection engineering needs ownership and engineering time. If every analyst is permanently trapped in investigation queues, nobody is improving the detection layer itself.
That creates a vicious cycle: More alerts, less engineering time, worse detections, even more alerts.
Interrupting this circle requires a real dedication to organizational change and not just improved tooling.
AI augments the process rather than the responsibility, and it will certainly augment detection engineering.
It can accelerate the triage process, assist in investigations, enhance data analysis, provide insights on tuning, and potentially reveal patterns we may not have thought of ourselves, but it will never replace human prioritization.
Businesses will always rely on people to determine:
- What risks to prioritize
- What trade-offs are worth making
- What attack vectors are worth targeting
- What operational expenses are warranted
The detection strategy cannot be outsourced to automation because automation does not understand the business consequences. That remains human work.
Better Detection Isn’t More Rules, It’s Better Decisions
The future of detection engineering lies in contextually covering more ground. The best SOCs will not be the ones who create the biggest ATT&CK matrix or have the most rules written down. Instead, they will be the businesses that understand their environment well enough to explicitly and intentionally decide how they will cover, optimize, and operate.
Detection engineering is never about finding everything and then moving on, but always about prioritization and understanding where you don’t cover.
Kirsten Doyle
News Editor
