From SIEM detection rules to IaC security scanning — a framework for hiring Security Engineers who find real threats in production environments, not theoretical vulnerabilities in audit reports.
Christina Zhukova
EXZEV
Security engineering is the most technically heterogeneous discipline in software. The same job title covers engineers who write detection rules in SPL, engineers who build CI/CD pipeline security gates, engineers who conduct application penetration tests, and engineers who manage CSPM platforms across 200 cloud accounts. These are not interchangeable. Hiring one when you need another is the most common and most expensive mistake in security team building.
The failure modes are specific. A mediocre security engineer runs automated vulnerability scanners and reports the output. They file a JIRA ticket for every medium-severity CVE, including the ones in dependencies that are not reachable in the application's code path. The engineering team spends three weeks patching vulnerabilities that posed no real exploitability risk, stops trusting the security team's prioritization, and begins routing around the security review process. The high-severity vulnerability that was actually exploitable — the one that required contextual understanding of the application's business logic to identify — was in the backlog, unread.
An elite security engineer understands exploitability in context. They know which CVE in your specific environment is actually reachable, and which one requires a code path that is never executed in production. They write detection rules that catch real attacker behavior while generating fewer than five false positives per day — because false-positive fatigue produces the same operational outcome as no detection at all. They shift security left into the development process in a way that engineers adopt rather than route around.
The title, disaggregated by specialization — and these are genuinely different jobs:
Posting a "Security Engineer" JD without specifying the primary specialization attracts the full range and selects by accident. The engineering team gets whoever showed up first.
The rule: A security engineer who generates 200 vulnerability tickets per month and cannot explain which three of them represent real exploitability risk in your environment is producing security theater, not security outcomes.
| Question | Why It Matters |
|---|---|
| Primary specialization? (AppSec / Cloud / Detection & Response / DevSecOps / Penetration Testing) | Non-interchangeable specializations — be explicit before sourcing begins |
| Shift-left or detect-and-respond? | Preventing vulnerabilities before code ships vs. detecting and responding to active incidents are different technical profiles and different personality types |
| What is the existing security tooling stack? | A SIEM migration from Splunk to Chronicle, vs. building a SIEM from scratch, vs. extending a mature Chronicle environment are different scopes |
| Does this engineer work with or within engineering teams? | AppSec engineers embedded in product squads vs. centralized security team members have different collaboration models and different skills mixes |
| Cloud provider and infrastructure stack? | AWS vs. Azure vs. GCP have different security service ecosystems — expertise does not fully transfer |
| Is bug bounty or penetration test management in scope? | Managing external researchers and coordinating with audit firms is a distinct operational skill |
| On-call incident response responsibility? | Detection engineers who are not in the incident response chain are not owning the detection mission — they are building tools for someone else's response |
| AI security exposure? | Prompt injection, model poisoning, and LLM-specific attack surfaces are emerging specializations that will be table stakes by 2027 |
The most common security engineer JD failure: listing certifications (CEH, OSCP, GCIH) and framework compliance knowledge without describing the actual technical work the engineer will do daily.
Instead of: "Security Engineer with experience in vulnerability management, SIEM, threat hunting, penetration testing, and cloud security to join our growing security team..."
Write: "You will be our first Detection and Response engineer. You will own the SIEM (Chronicle SIEM) including all detection rule development, triage of alerts, incident investigation, and SOAR playbook automation (Google SOAR). Current state: 12 rules in production, average 140 alerts/day, 95% false positive rate — this is the problem you are solving. Primary data sources: CloudTrail, GCP Audit Logs, Okta, CrowdStrike Falcon. You are on-call for P1 security incidents on a rotation with two other security engineers. Stack: GCP, Kubernetes (GKE), Chronicle, UEBA."
Structure that converts:
Highest signal:
Mid signal:
Low signal:
The EXZEV approach: We maintain a pre-vetted network of security engineers assessed across specialization depth, production tooling portfolio quality, and detection accuracy track record. Most clients receive a shortlist within 48 hours.
Security engineering screening fails when it focuses on conceptual security knowledge rather than demonstrated technical capability. A security engineer who can explain how a SQL injection works but cannot write a detection rule that identifies SQL injection attempts in application logs is not a security engineer — they are a security awareness training recipient.
Stage 1 — Async Technical Questionnaire (40 minutes)
Five questions evaluated on specificity and practical methodology.
Example questions that reveal real depth:
What you're looking for: Detection rule precision (they describe specific log fields and thresholds, not abstract concepts), exploitability context (they do not treat every scanner finding as equally urgent), and developer empathy (they design security tooling that developers will adopt rather than route around).
Red flag: "I would scan the environment and identify vulnerabilities" — this is a description of a scanner, not a security engineer.
One senior security engineer, structured:
Do not give algorithm challenges or abstract security theory. Do give: real log data, real code, real Terraform — and evaluate whether they produce actionable output, not correct descriptions of what they would do in theory.
Four parts. Senior security engineers are in high demand and evaluate organizations on the quality of the technical environment — not just the compensation.
Your most senior security engineer or the CISO. Deep dive on the candidate's most technically complex security project. For detection engineers: "Walk me through the most sophisticated threat actor behavior you have built detection logic for. What were the TTPs (MITRE ATT&CK mapping), what was the detection logic, and what was the false positive rate at steady state?" For AppSec engineers: "Walk me through the most critical vulnerability you found in a production application. How did you verify it was exploitable, what was the impact assessment, and what was the remediation?" Specificity of TTPs, specific MITRE ATT&CK technique IDs, specific exploit proof-of-concept — these distinguish practitioners from theorists.
A structured hands-on exercise relevant to the specialization:
Detection engineer prompt: "Here is a simulated CloudTrail dataset covering 24 hours of activity in our AWS environment. There is one adversarial event sequence embedded in the data. You have 40 minutes to identify it, describe the attack technique (MITRE ATT&CK mapping), and write the detection rule you would implement to catch this technique across future log data."
AppSec engineer prompt: "Here is a 200-line API endpoint (Python/Django or Node.js). You have 40 minutes to conduct a security code review: identify every security issue, severity-classify each using CVSS, and write one finding in the format you would deliver to the engineering team for the highest-severity issue."
Evaluate: Do they find the intentional finding? Do they find things beyond the intentional finding? Is their severity classification calibrated to real exploitability, not just theoretical impact?
Engineering manager and one software engineer from the team the security engineer will work with. The question: will this security engineer be treated as a trusted collaborator by the development team, or as an external auditor to be managed? The security function that engineers route around is not providing security — it is providing friction.
Ask the engineering manager: "When security engineers tell your team about a vulnerability, what does the conversation usually look like?" Ask the candidate: "How do you communicate a critical vulnerability to an engineering team that has a release scheduled tomorrow?"
CISO or security lead. "Walk me through a security incident where you were the primary responder — from the initial alert to the post-mortem. What did you triage first? What did you get wrong in the initial assessment? What was in the post-mortem that changed your detection or response tooling?" The answer reveals operational judgment under pressure and the intellectual honesty that separates engineers who learn from incidents from those who document them.
Technical red flags:
Behavioral red flags:
Security engineers in production security operations — detection, cloud security, and application security — command a significant premium above standard software engineering compensation bands, reflecting the specialized knowledge domain and the direct organizational risk they manage.
| Level | Remote (Global) | US Market | Western Europe |
|---|---|---|---|
| Mid-Level (2–4 yrs) | $100–135k | $155–205k | €90–130k |
| Senior (4–8 yrs) | $135–180k | $205–275k | €130–175k |
| Lead / Staff (8+ yrs) | $180–240k | $275–370k | €175–235k |
Offensive security / penetration testing premium: Senior penetration testers and red team operators command 10–20% above equivalent defensive security engineers, reflecting the OSCP/CRTE/CRTO certification investment, the specialized tooling knowledge, and the genuine scarcity of engineers with practical offensive experience.
AI security emerging premium: Engineers with LLM security expertise — prompt injection testing, model output validation, LLM-specific SAST rules — are commanding a nascent premium that will become significant over the next 18 months as AI attack surfaces become primary organizational risks.
Week 1–2: Detection and vulnerability inventory audit Before writing a new rule or patching a new vulnerability, audit the current state: every active detection rule and its false positive rate, the open vulnerability backlog and the age distribution of critical findings, the SIEM data source coverage and its gaps, and the last incident and its MTTD/MTTR. This baseline is the starting point for measuring everything that follows.
Week 3–4: First detection rule or security control with measured impact For a detection engineer: write and deploy one new detection rule — a specific adversarial TTP identified through threat modeling or threat intelligence — with documented false positive validation against 30 days of historical log data. For an AppSec engineer: complete a security code review for one feature in active development, with findings delivered in the format the engineering team will use, and measure how many were remediated. For a cloud security engineer: complete a CSPM misconfiguration scan and remediate the five highest-priority findings with documented exploitability context.
Month 2: First integration into the development process For detection engineers: implement one SOAR playbook that automates a manual step in the incident triage process and reduces mean time to triage for one alert category. For AppSec engineers: introduce one automated security gate in the CI/CD pipeline for the team they are embedded with — SAST, secrets scanning, or dependency vulnerability scanning — with a false-positive rate low enough that the engineering team does not turn it off.
Month 3: First security metric that is tracked by the business Establish one security metric that is visible to the engineering leadership or the CISO on a weekly basis: alert false positive rate trending down, mean time to remediate critical vulnerabilities, percentage of new features that received a threat model review before deployment. Engineers who own a metric own the outcome — and organizations that track security outcomes rather than security activities improve faster.
The security engineering market in 2026 is full of professionals who can run automated tools and produce reports. The ones who understand exploitability in context, write detection rules that catch real attacker behavior without generating operational noise that destroys the SOC's effectiveness, and integrate security into engineering processes in a way that developers adopt rather than route around — they require a search process that tests technical judgment under realistic conditions.
Every security engineer in the EXZEV database has been assessed on specialization-specific technical depth, production tooling portfolio quality, and demonstrated detection accuracy or vulnerability exploitation track record. We do not introduce candidates who score below 8.5. Most clients make an offer within 10 days of their first shortlist.
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