Why SIEMs Need Strong Detection Engineering and How We Approach It at CyberSift

There is a recurring assumption in many environments: if the SIEM is properly configured, detection is “solved.” In reality, SIEMs don’t detect threats - they execute logic. And that logic is only as good as the assumptions behind it.

What we consistently observe in real-world incidents is not a lack of SIEM coverage, but a lack of detection engineering discipline.

At CyberSift, this is one of the core areas we continuously invest in: expanding, validating, and maintaining detection rules as environments evolve.

The real problem isn’t the SIEM

Most SIEM deployments fail in predictable ways:

• Logs exist, but not the right ones

• Rules are deployed, but not tuned for the environment

• Alerts exist, but lack investigative context

• Coverage looks strong on paper, but gaps exist in real attack paths

This creates a false sense of security. Dashboards look healthy, but detection depth is shallow.

We’ve seen environments with hundreds of active rules still miss basic attack chains because none of those rules were designed around how attackers actually move, only around what telemetry was available.

Detection engineering is the missing layer

Detection engineering is what turns SIEM data into usable security decisions.

In practice, it means:

• Designing rules around attacker behavior, not log availability

• Continuously validating detections against real techniques

• Eliminating noisy or low-value alerts

• Maintaining rules as infrastructure and cloud services change

• Closing gaps between individual alerts and full attack chains

Without this layer, SIEMs naturally drift into two states: overload or blindness.

How CyberSift approaches detection engineering

Within CyberSift’s SOC operations, detection engineering is a continuous function.

Our approach focuses on three core activities:

1. Continuous rule expansion

We are constantly adding new detection logic based on:

• Emerging attacker techniques

• Real incident patterns observed in customer environments

• Threat intelligence and post-incident analysis

• Internal simulation and validation exercises

New rules are not added for volume - they are added for coverage of actual attack behavior.

2. Detection validation as a routine process

Every meaningful detection is tested against:

• Known benign activity

• Realistic adversary simulations

• Cross-telemetry validation (endpoint, network, cloud)

If a rule cannot survive this validation, it does not reach production.

3. Reducing alert noise through precision engineering

One of the biggest operational problems in SOC environments is not lack of alerts but lack of trust in alerts.

We actively refine rules to ensure:

• Higher signal-to-noise ratio

• Clearer investigative context per alert

• Better correlation across stages of attack

• Reduced duplication across detection sources

The goal is simple: fewer alerts, but each one worth investigating.

What this means in practice

Тhis approach results in:

• Faster detection of multi-stage attacks

• Less time wasted on low-value alerts

• Better visibility into lateral movement and identity abuse

• More consistent detection coverage as environments evolve

But more importantly, it changes how security operations feel: less reactive filtering, more focused investigation.

Final thought

A SIEM without detection engineering is just a log repository with rules attached.

The difference between “having visibility” and “detecting attacks” is not the tool - it’s the discipline behind it.

At CyberSift, that discipline is what we are continuously building into every detection rule we ship.

Our active detection rules can be reviewed here.

-Written by Stanislav Stoychev