Effective cyber defence in the SOC through smart detection rules

In the Security Operations Center (SOC), precise detection rules are crucial for detecting cyberattacks at an early stage and minimising false alarms. Find out how detection engineering optimises IT security.

Cyber resilience is a key buzzword in the field of IT security. Detection engineering is a key component of this. But what is behind it? What role does a detection engineer play and why is this work so crucial for the protection of IT infrastructures? In addition, modern security solutions such as Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) pose new challenges, particularly with regard to the transparency and effectiveness of detection rules.

What is detection engineering?

Detection engineering involves the development and optimisation of detection rules in order to identify potential threats at an early stage. These rules are implemented in Security Information and Event Management (SIEM) systems or Security Orchestration, Automation and Response (SOAR) platforms. The aim is to filter out security-critical events from the flood of log data and react to them automatically.

The importance of detection rules

Companies today are exposed to a wide range of cyber threats. Attackers are using increasingly sophisticated methods to penetrate networks undetected. Standardised security solutions are often not sufficient to detect specific threats in an individual IT environment. Detection engineering enables the creation of customised detection rules that are precisely tailored to the respective infrastructure and its threat situation.

Detection Engineer vs. SOC Analyst - What's the difference?

A Security Operations Center (SOC) Analyst works with alarm inputs generated by detection rules. They analyse security-relevant events and decide on countermeasures. A detection engineer, on the other hand, develops these rules and ensures that they accurately recognise threats without generating too many false positives. So while the SOC analyst consumes the detection, the detection engineer creates and optimises the basis for it.

Challenges with EDR and XDR detection rules

Modern security solutions are increasingly relying on EDR and XDR to detect threats at an early stage. However, one of the biggest challenges is the lack of transparency in standardised detection rules. Many security teams do not know exactly which criteria lead to an alert, as preconfigured rules are often opaque black box models. In SIEM and SOAR environments, there is more flexibility, but each platform uses its own syntax, which requires constant training.

The everyday life of a detection engineer: Between research and optimisation

A large part of the work in detection engineering consists of continuously researching and adapting existing rules. A detection rule should not only reliably recognise attacks, but also take into account typical company processes in order to avoid unnecessary alarms. One example is the detection of autostart programmes: A rule that is too general can lead to a flood of false alarms. It is therefore important to define a baseline for each company that takes into account the normal behaviour of applications and scripts.

Complexity of detection rules

Depending on the attack scenario, detection rules can range from simple signature checks to complex, multi-level behavioural detections. For example, a simple rule could react to the appearance of a known attack tool such as Mimikatz. However, attackers often use obfuscation techniques to circumvent such detections. This is where so-called multi-event detection rules come into play, which recognise different stages of an attack - from the first login to the manipulation of critical systems.

The influence of AI on detection engineering

With the increasing development of artificial intelligence (AI), the question arises as to whether and to what extent AI can take over the work of detection engineers in the future. AI models are already able to generate code today and could support the creation and adaptation of detection rules in the future. However, there are strict limits to the direct creation of rules by AI: The rules must be tailored precisely to the respective environment - a generic approach often leads to inaccurate or incorrect results. Data protection also plays a role: sensitive company data should not be fed into external AI systems in an uncontrolled manner.

Conclusion: Does every company need a detection engineer?

Not every company has its own detection engineering team. In many cases, standard detection rules are sufficient to cover basic security risks. However, a dedicated detection engineer can significantly improve the security strategy by identifying specific threats and developing customised protection mechanisms. Companies with sensitive data or complex IT structures should consider whether they can benefit from customised detection engineering.

After all, detection engineering is an indispensable component of modern cyber security strategies. The combination of human expertise, technological automation and transparent optimisation of detection rules ensures that companies are armed against increasingly sophisticated threats.