Skip to content

devsecops.md

Latest commit

 

History

History
169 lines (135 loc) · 8.67 KB

devsecops.md

File metadata and controls

169 lines (135 loc) · 8.67 KB

DevSecOps Skills and Career Roadmap

📘 Recommended study plans: Secure Software Development Lifecycle · Product Security · Common Skills.

DevSecOps sits at the intersection of Dev + Sec + Ops. You write code, automate pipelines, and embed security checks where developers will actually use them. It's one of the highest-paying tracks in security right now because the skill blend is genuinely rare.

Who is this for?

  • Developers who care about security
  • DevOps / SRE / Platform engineers moving toward security
  • AppSec engineers who want to automate themselves out of repetitive work
  • Security engineers tired of "scan-and-throw-over-the-wall" workflows

Pre-requisites (foundation)

  1. Solid coding skills in at least one language (Python or Go preferred; JavaScript, Java OK)
  2. Git, branching strategies, code review etiquette
  3. CI/CD platforms — GitHub Actions / GitLab CI / Jenkins (pick one to start)
  4. Linux + Docker + Kubernetes basics
  5. At least one cloud (AWS / Azure / GCP)
  6. IaC — Terraform basics
  7. Familiarity with the OWASP Top 10 (you'll be detecting these in pipelines)

Career ladder

Entry level (0–2 years)

Possible job titles:

  • Security Automation Engineer (Junior)
  • Associate DevSecOps Engineer
  • AppSec Engineer (automation-leaning)
  • Cloud Security Engineer (pipeline focus)

Skills to focus on:

  1. CI/CD security 101 — secrets in pipelines, runner isolation, branch protections
  2. SAST integration — Semgrep, SonarQube, CodeQL, Snyk Code, Checkmarx
  3. SCA / dependency scanning — Dependabot, Renovate, Snyk Open Source, OWASP Dependency-Check
  4. Container scanning — Trivy, Grype, Docker Scout
  5. IaC scanning — Checkov, tfsec, KICS
  6. Secret scanning — gitleaks, trufflehog, GitHub secret scanning
  7. Scripting glue — Python / Bash / Go to wire scanners together
  8. API basics — calling SaaS scanner APIs, parsing JSON, posting to Jira/Slack
  9. Defect Dojo or OWASP Dojo for vuln aggregation

Mid level (2–5 years)

Possible job titles:

  • DevSecOps Engineer
  • Security Tooling Engineer
  • Platform Security Engineer

New skills to add:

  1. DAST automation — ZAP baseline, Nuclei in CI, authenticated scans
  2. Policy as Code — OPA / Rego, Conftest, Sentinel (HashiCorp)
  3. Pipeline hardening — SLSA framework, signed commits, signed artifacts (Cosign)
  4. Build provenance & SBOM — Syft, CycloneDX, SPDX
  5. Kubernetes admission control — Kyverno / Gatekeeper at scale
  6. Secrets management — HashiCorp Vault, AWS Secrets Manager, External Secrets Operator
  7. Custom security tooling — building your own scanners / Burp extensions / Semgrep rules
  8. Threat modeling integrated into design reviews
  9. Security observability — emitting metrics on MTTR, scanner coverage, vuln aging
  10. Developer experience (DX) — make the secure path the easy path

Certs to consider:

Senior level (5–8 years)

Possible job titles:

  • Senior DevSecOps Engineer
  • Lead Platform Security Engineer
  • Staff Security Engineer (Platform)

New focus areas:

  1. Reference architectures for secure pipelines across the org
  2. Vendor evaluation and consolidation (CNAPP, SCA, SAST, secrets vaults)
  3. Security guardrails as a service — paved roads, golden pipelines
  4. Supply chain security strategy (SLSA Level 3+, in-toto, signed builds)
  5. Cross-team influence — partnering with eng leadership, SRE, product
  6. Metrics & business reporting — board-friendly security KPIs
  7. Cost optimization — picking the right tool per scale

Staff / Principal (8+ years)

Possible job titles:

  • Principal DevSecOps Engineer
  • Platform Security Architect
  • Head of DevSecOps / Engineering Manager - Security Platform

Focus areas:

  • Org-wide security platform strategy
  • Build vs. buy decisions
  • Team scaling and developer engagement programs
  • Industry contributions (OSS, talks, OWASP)

Career paths from DevSecOps

                       DevSecOps (entry)
                              │
       ┌──────────────┬───────┴───────┬──────────────┐
       ▼              ▼               ▼              ▼
   AppSec        Cloud Security    Platform Sec   Supply Chain
   Engineer      Engineer           Engineer       Security
       │              │               │              │
       ▼              ▼               ▼              ▼
   Product Sec   Cloud Security  Distinguished   SLSA / SBOM
   Architect     Architect       Platform Eng    Lead
       │
       ▼
  Security Architect ──► Head of Engineering Security / CISO track

Lateral pivots from DevSecOps

  • → Application Security — go deeper into code, threat modeling
  • → Cloud Security — same toolchain, more infra
  • → Container / K8s Security — runtime + admission focus
  • → SRE / Platform Engineering — reliability-tinted track
  • → Engineering Management — lead the platform sec team

AI-augmented DevSecOps (you need this in 2025+)

DevSecOps is one of the most AI-leveraged tracks because so much of the work is glue code, triage, and pipeline orchestration.

Using AI to do DevSecOps better

  1. AI-assisted triage — use LLMs to summarize SAST / SCA / DAST findings, deduplicate, and route to the right team
  2. Auto-fix PRs — GitHub Copilot Autofix, Snyk DeepCode, Cursor for fixing simple SAST findings
  3. Custom Semgrep / CodeQL rules — prompt AI to convert CVE patterns to rules; you validate
  4. Pipeline code generation — generating boilerplate GitHub Actions / GitLab CI YAML for new repos with the org's mandatory security stages
  5. OPA / Rego authoring — AI is genuinely good at first-draft policies; you tighten and test
  6. Incident comms / postmortem drafts — AI writes the first 80%, you fix the last 20%

Securing AI in CI/CD

GenAI is now part of the SDLC — secure it like any other tool:

  1. Secrets in AI sessions — developers paste production keys / customer data into Copilot / ChatGPT. Build DLP into the IDE and chat layer.
  2. Hallucinated packages — "slopsquatting"; pin dependencies, use private registries, enforce signed packages
  3. AI-generated IaC review — AI-written Terraform / Helm often misses least privilege and encryption defaults
  4. AI agent autonomy — if a copilot can open PRs, make sure it can't merge or deploy without human review
  5. Model & weight supply chain — sign and verify models like artifacts (Cosign, ModelScan, Sigstore for models)
  6. AI-BOM alongside SBOM in your build pipeline
  7. Cost controls — token budgets, per-team quotas, audit logs for AI service usage

See: AI Security Career Roadmap · GenAI Security Study Plan

Recommended tools to master

  • SAST: Semgrep, CodeQL, SonarQube, Snyk Code
  • SCA: Dependabot, Renovate, OWASP Dependency-Check, Snyk Open Source
  • DAST: OWASP ZAP, Nuclei, Burp Suite Enterprise
  • Secrets: gitleaks, trufflehog, Vault
  • IaC: Checkov, tfsec, KICS, Terrascan
  • Container: Trivy, Grype, Docker Scout
  • Policy: OPA, Kyverno, Conftest
  • Supply chain: Cosign, Syft, in-toto, SLSA
  • Vuln management: DefectDojo, Faraday

Recommended books / resources

Next step

Pick a sample project (any Python/Node app on GitHub), and build a complete secure pipeline yourself — SAST + SCA + secret scan + container scan + IaC scan + DAST baseline + SBOM generation. The exercise alone teaches you 70% of what you need at mid-level.