What Programming Languages Do Security Engineers Actually Use?

Mar 21, 2026

Cowrie

Dev @ Bswen

The Problem

I love object-oriented programming. I spent years mastering Java design patterns, C# architecture, and TypeScript generics. When I started exploring security engineering, I hit a wall.

The Reddit post I found echoed my frustration: “I love OOP languages but in the areas I like, these languages are barely used. What’s the reality?”

The answers surprised me. One comment with 38 upvotes cut to the chase: “There’s a reason security stuff is mostly declarative, a very good one…”

This made me rethink my assumptions about what security engineers actually do and what languages they use.

What I Discovered: Languages by Domain

Security engineering isn’t one job. It’s several different jobs that happen to share a title. The language depends entirely on which domain you work in.

Offensive Security (Penetration Testing, Red Team)

Python dominates here. I looked at the top penetration testing tools:

Tool              Language
────────────────────────────
Metasploit        Ruby
Burp Suite        Java
Nmap              C/Lua
Wireshark         C
sqlmap            Python
pwntools          Python
Impacket          Python

Python wins because offensive work needs:

• Fast iteration (write a script in 10 minutes)
• Easy sharing (copy one file to a target machine)
• Rich libraries (requests, scapy, paramiko)

The top comment on that Reddit thread: “Python is everywhere in offensive security, automation, tooling, and research.”

Go is growing for implants and cross-platform tools. A single binary with no dependencies is gold when you’re deploying to a target machine.

Defensive Security (Blue Team, SOC)

Here I found a different pattern:

Task                    Language/Tool
─────────────────────────────────────
Log analysis            Python + SQL
Detection rules         YAML/Sigma
SIEM queries            Splunk SPL, KQL
Threat hunting          Python
Automation scripts       Python, Bash
Infrastructure tools    Go

Notice what’s missing? Traditional OOP. Detection rules are declarative:

detection-rule.yaml

title: Suspicious PowerShell Execution
status: stable
description: Detects encoded PowerShell commands
logsource:
  product: windows
  service: powershell
detection:
  selection:
    EventID: 4104
    CommandLine|contains:
      - 'FromBase64String'
      - 'encodedcommand'
  condition: selection
level: high

This isn’t object-oriented. It’s declarative. The Reddit commenter with 38 upvotes was right.

Security Tool Development

When building tools you’ll deploy to other teams, Go and Rust shine:

scanner/main.go

package main

import (
    "fmt"
    "net/http"
    "time"
)

func scanEndpoint(url string) (int, error) {
    client := &http.Client{Timeout: 5 * time.Second}
    resp, err := client.Get(url)
    if err != nil {
        return 0, err
    }
    defer resp.Body.Close()
    return resp.StatusCode, nil
}

func main() {
    // Compile: go build -o scanner
    // Deploy: just copy and run - no dependencies
    endpoints := []string{
        "https://example.com/admin",
        "https://example.com/.git",
    }
    for _, url := range endpoints {
        code, _ := scanEndpoint(url)
        fmt.Printf("%s: %d\n", url, code)
    }
}

A Reddit comment with 20 upvotes explained: “In security, you want: Fast iteration, Easy deployment (single binary with Go, or just copy a Python script), Minimal dependencies (less attack surface), Readability for the next person who has to audit it.”

Rust provides memory safety for tools that need it, without the garbage collection that can interfere with low-level operations.

Application Security

This is where OOP languages still appear:

Application Type        Languages You'll Test
─────────────────────────────────────────────
Enterprise web apps     Java, C#
Frontend security       JavaScript, TypeScript
APIs                    Go, Python, Node.js
Mobile apps             Kotlin, Swift, Java

If you’re testing Java applications, knowing Java helps. But you’re not writing Java—you’re writing scripts to test it.

Why Security Prioritizes Differently

I realized security engineers have different priorities than application developers:

Priority	Security Engineering	Traditional Development
Speed	Fast iteration, one-off scripts	Maintainable codebases
Deployment	Single binary, no dependencies	CI/CD pipelines
Dependencies	Minimal (attack surface)	Feature-rich frameworks
Patterns	Declarative, functional	OOP, design patterns
Audience	Security team, auditors	End users

The “minimal dependencies” point is crucial. Every dependency is a potential attack vector. Security tools are audited. Adding a heavy framework makes auditing harder.

The Reality Check

A comment on that Reddit thread caught me off guard: “How much programming are you expecting as a security engineer? That’s really more of a role that knows about programming but doesn’t spend a lot of time past scripting.”

This made me reconsider what security engineers actually do all day:

Security Engineer Day:
├── 30% Reading logs and alerts
├── 25% Configuring tools (SIEM, EDR, firewalls)
├── 20% Writing detection rules (YAML, SQL)
├── 15% Scripting automation (Python, Bash)
├── 10% Actual tool development
└── 0% Building OOP architectures

Most security work is:

• Reading and understanding systems
• Writing configuration and rules
• Running and configuring existing tools
• Analyzing outputs

Not building software.

What I’d Learn First (If Starting Over)

Based on what I found, here’s what I’d prioritize:

Offensive Security Career Path

Must learn:     Python
Should learn:   Bash/PowerShell, Go
Nice to have:   C (for exploit development)

Python alone covers 80% of offensive work. Bash/PowerShell for target environment scripting. Go if you want to build tools others will use.

Defensive Security Career Path

Must learn:     Python, SQL
Should learn:   Bash, YAML (for configs)
Nice to have:   Go (for custom tools)

SQL surprised me, but it makes sense. Log analysis and threat hunting require database queries.

Application Security Career Path

Must learn:     JavaScript/TypeScript, Python
Should learn:   The language of apps you test
Nice to have:   Go, Rust

This is where OOP skills matter most. If you’re testing Java applications, knowing Spring Boot helps you find vulnerabilities.

Security Tool Development Career Path

Must learn:     Go or Rust
Should learn:   Python
Nice to have:   C/C++ (for system-level tools)

This is the most programming-heavy path. Choose Go for fast tool development, Rust for memory-safe tools.

Typical Security Script Pattern

Most security scripts I found look like this:

security-scanner.py

import requests
from pathlib import Path

def check_exposed_endpoints(domain: str) -> list:
    """Check for common misconfigurations."""
    endpoints = ["/admin", "/.git", "/config", "/backup"]
    findings = []

    for endpoint in endpoints:
        url = f"https://{domain}{endpoint}"
        response = requests.get(url, timeout=5)
        if response.status_code == 200:
            findings.append(url)

    return findings

# No classes. No OOP. Just functions.
# Fast to write, easy to audit, works.
if __name__ == "__main__":
    results = check_exposed_endpoints("example.com")
    print(f"Found {len(results)} exposures")

Not object-oriented. Not complex. Just a function that does one thing well.

Common Misconceptions I Had

Misconception 1: “Security engineers need to be expert programmers.”

Reality: Most security work involves scripting and tool usage, not building complex software. Python proficiency is enough for most roles.

Misconception 2: “OOP languages are useless in security.”

Reality: They appear in application security, enterprise security tools, and cloud security roles. But you won’t be designing class hierarchies—you’ll be testing systems built with OOP.

Misconception 3: “I need to learn C to work in security.”

Reality: Python covers most needs. C is niche (malware analysis, exploit development, OS security).

Misconception 4: “Security engineering is all coding.”

Reality: It’s more about understanding systems, reading logs, configuring tools, and writing detection rules. Coding is maybe 20% of the job.

Where OOP Skills Still Matter

If you love OOP like I do, don’t despair. These areas still use OOP:

1. Application security testing: Testing Java/C# apps requires understanding their architecture
2. Security tool development: Building enterprise-grade security products
3. Cloud security: AWS, Azure SDKs often use OOP patterns
4. Enterprise security platforms: SIEM platforms like Splunk have Java backends

The question isn’t “will I use OOP?” but “will I use OOP patterns, or just understand OOP systems?”

What I’m Doing Now

I’m learning Python properly (not just the basics I picked up for scripting). But I’m not abandoning my OOP background. Understanding object-oriented architectures helps me analyze the systems I’m securing.

My learning path:

Current: Deep Python (for daily security work)
Next: Go (for tool building)
Later: Rust (for specialized tools)
Keep: Java/C# knowledge (for app security testing)

The Reddit comment that stuck with me: “In security, you want readability for the next person who has to audit it.” Whether that’s OOP or functional or procedural matters less than clarity.

Summary

Security engineers primarily use Python for most tasks, Go for deployable tools, and declarative languages (YAML, SQL) for configurations and analysis. While OOP skills aren’t wasted, the security field prioritizes scripting speed, minimal dependencies, and clarity over traditional software architecture.

The key insight from my research: security engineering is less about building software and more about understanding systems. Python’s dominance comes from its versatility for scripting, automation, and tool usage—not from being “better” for security, but from being practical for the actual work security engineers do.

If you’re an OOP developer moving into security, your architecture knowledge helps you understand the systems you’re securing. But expect to write more scripts than class hierarchies.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 Reddit: Do security engineers really not use OOP languages?
• 👨‍💻 OWASP Security Knowledge Framework
• 👨‍💻 Python Security Tools

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

Comments

Tags:

• security
• career
• python
• backend

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