Detection Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Applying for a Detection Engineer job in the UK? Here is what you are likely to be asked. These questions come from our Detection Engineer question bank of 12 questions, written for UK employers at mid-level level.

Eight Detection Engineer interview questions, and what the interviewer is thinking

1. What is Sigma, and why would you use it as a detection engineer?

Why they ask: They want to confirm you know the common vendor-neutral rule format and its practical value.

2. How do you decide what to detect, and how do you use MITRE ATT&CK to prioritise detection work?

Why they ask: They want to see a threat-informed approach rather than random rule writing.

3. How would you test and validate a new detection before it goes live?

Why they ask: They want to understand your engineering discipline around quality and false positives.

4. Write or describe a query to find suspicious use of PowerShell that you might use in your SIEM, and explain how you'd cut false positives.

Why they ask: They want to see hands-on query ability and tuning instincts.

5. What log sources and data quality problems have you encountered that limit detection, and how have you addressed them?

Why they ask: They want to see you understand that detection quality depends on data engineering.

6. One of your detection rules is generating hundreds of false positives per day and the SOC wants it switched off. What do you do?

Why they ask: They want to see how you balance coverage against analyst workload.

7. Tell me about a time you created a detection that found something real.

Why they ask: They want evidence your work has delivered security outcomes.

8. What makes a great detection engineer, and why does this role interest you?

Why they ask: They want to see you understand the blend of attacker knowledge and engineering discipline.

A sample answer to question 1

What is Sigma, and why would you use it as a detection engineer?

Sigma is an open, YAML-based format for describing detection logic against log data, in the same way Snort rules do for network traffic and YARA does for files. A rule defines the log source, the selection and filter conditions, a title, severity, false positive notes and ATT&CK tags. The value is portability: I write a rule once, then convert it with pySigma or sigma-cli into Splunk SPL, Microsoft Sentinel KQL, Elastic queries or CrowdStrike syntax, depending on the platform. That makes it easier to share logic across teams, reuse community rules from the SigmaHQ repository, and migrate when a SIEM changes. It also fits detection-as-code: rules live in Git, go through pull request review, and are validated automatically. The limits are that conversions can need manual tuning for field mappings, and Sigma can't express every stateful or behavioural correlation, so I treat it as a strong starting point rather than the entire detection library.

Common mistake to avoid: Confusing Sigma with YARA or Snort; Ignoring field mapping issues

Want the rest?

Full model answers, key points and common mistakes for all 12 questions are on the Detection Engineer interview questions page. The first five are free, no sign-up needed.