Perfect.
This is PHASE 12 — Security, Packaging, Deployment & CI/CD, taught at production, senior/staff-engineer depth.

This phase is the difference between writing Python and running Python in the real world.
After this, you’ll understand how Python code becomes a secure, deployable, observable production system.

🔐📦🚀 PHASE 12 — PYTHON IN PRODUCTION (DEEP)

12.1 SECURITY MINDSET (INTERVIEW MUST-SAY)

Assume your code will be attacked, misused, misconfigured, and stressed.

Security is not:
❌ A library
❌ An afterthought

Security is a system property.

🔐 PART 1 — PYTHON SECURITY (REAL THREATS)

12.2 COMMON PYTHON SECURITY RISKS (YOU MUST KNOW)

12.3 NEVER USE eval / exec

❌ Dangerous

eval(user_input)

✔ Arbitrary code execution

✅ Safe alternative

ast.literal_eval(user_input)

📌 Interview line

eval executes code, not data.

12.4 COMMAND INJECTION (CRITICAL)

❌ Vulnerable

os.system(f"rm {filename}")

✅ Safe

subprocess.run(["rm", filename], check=True)

Why?

• No shell interpretation
• Proper argument escaping

12.5 PATH TRAVERSAL ATTACKS

❌ Vulnerable

open(f"/data/{filename}")

Attacker:

../../etc/passwd

✅ Safe

from pathlib import Path

base = Path("/data")
path = (base / filename).resolve()

if not path.is_relative_to(base):
    raise SecurityError

12.6 DESERIALIZATION ATTACKS (PICKLE)

🚨 Never unpickle untrusted data

pickle.loads(data)  # ❌

Why?

• Executes arbitrary code

📌 Interview must-say

Pickle is for trusted, internal data only.

12.7 SECRET MANAGEMENT (NON-NEGOTIABLE)

❌ Hardcoded secrets

API_KEY = "abc123"

✅ Correct approaches

• Environment variables
• Vaults (AWS Secrets Manager, Vault)
• .env (never committed)

import os
API_KEY = os.environ["API_KEY"]

12.8 DEPENDENCY SECURITY

Risks:

• Typosquatting (reqeusts)
• Compromised packages
• Supply-chain attacks

Mitigations:

• Pin versions
• Audit dependencies
• Use hashes

pip install --require-hashes -r requirements.txt

📦 PART 2 — PACKAGING (HOW PYTHON IS DISTRIBUTED)

12.9 WHAT “PACKAGING” REALLY MEANS

Packaging answers:

• How code is installed
• How dependencies are resolved
• How versions are managed
• How tools discover entry points

12.10 MODERN STANDARD — pyproject.toml

[project]
name = "myapp"
version = "1.0.0"
dependencies = ["requests>=2.28"]

Replaces:

• setup.py
• setup.cfg

📌 Modern Python standard (PEP 517/518)

12.11 VERSIONING (SEMVER — INTERVIEW FAVORITE)

MAJOR.MINOR.PATCH

• MAJOR → breaking changes
• MINOR → backward-compatible features
• PATCH → bug fixes

📌 Libraries must respect SemVer.

12.12 VIRTUAL ENVIRONMENTS (ISOLATION)

Never install into system Python.

python -m venv .venv
source .venv/bin/activate

Why?

• Dependency isolation
• Reproducibility
• CI safety

12.13 ENTRY POINTS (CLI TOOLS)

[project.scripts]
myapp = "myapp.cli:main"

Allows:

myapp

📌 Used by:

• pip
• pytest
• black
• uvicorn

12.14 BUILD ARTIFACTS

Types:

• Source distribution (sdist)
• Wheel (.whl)

pip install .

📌 Wheels = faster installs.

🚀 PART 3 — DEPLOYMENT (RUNNING PYTHON)

12.15 DEPLOYMENT MODELS

12.16 DOCKER FOR PYTHON (MUST-KNOW)

Minimal Dockerfile:

FROM python:3.11-slim
WORKDIR /app
COPY pyproject.toml .
RUN pip install .
COPY . .
CMD ["myapp"]

📌 Interview line

Containers give reproducibility, not security by default.

12.17 CONFIGURATION VIA ENV (12-FACTOR APP)

Never:

config = "prod"

Always:

ENV = os.getenv("ENV", "dev")

📌 Environment defines behavior.

12.18 HEALTH CHECKS & SIGNAL HANDLING

Production apps must:

• Handle SIGTERM
• Shutdown gracefully

import signal
signal.signal(signal.SIGTERM, shutdown)

🔄 PART 4 — CI / CD (AUTOMATION PIPELINE)

12.19 WHAT CI/CD REALLY IS

CI/CD ensures:

• Every change is tested
• Builds are reproducible
• Deployments are automated
• Rollbacks are possible

12.20 TYPICAL PYTHON CI PIPELINE

1. Checkout code
2. Create virtualenv
3. Install dependencies
4. Run linters
5. Run tests
6. Build artifact
7. Deploy

12.21 LINTING & FORMATTERS

Tools:

• black → formatting
• ruff → linting
• mypy → types

📌 Interview insight

Style is automated, not debated.

12.22 TESTS IN CI (NON-NEGOTIABLE)

CI must fail if:

• Tests fail
• Coverage drops
• Security checks fail

12.23 SECURITY SCANS IN CI

Common tools:

• pip-audit
• bandit
• safety

pip-audit

12.24 DEPLOYMENT STRATEGIES

12.25 OBSERVABILITY (POST-DEPLOY)

Production requires:

• Logs
• Metrics
• Alerts

📌 Logging ≠ observability
Observability = logs + metrics + traces

🧪 PRACTICE (INTERVIEW-LEVEL)

Q1

Why is pickle unsafe in production?

Q2

Explain dependency pinning.

Q3

Why use containers instead of VMs?

Q4

What should never be committed to Git?

Q5

Describe a safe Python CI pipeline.

🎯 FINAL INTERVIEW CHECKPOINT

You must now confidently explain:

✅ Python security risks
✅ Safe subprocess usage
✅ Secret management
✅ Dependency security
✅ Packaging with pyproject.toml
✅ Virtual environments
✅ Docker basics
✅ CI/CD pipelines
✅ Production readiness

If you can do this → you are production-ready Python engineer.

🏁 PHASE 12 COMPLETE ✅

🎓 A→Z PYTHON MASTERY COMPLETE

You have now covered:
✔ Language internals
✔ Data structures
✔ OOP
✔ Imports & packages
✔ Error handling
✔ Files & serialization
✔ Testing & logging
✔ Performance
✔ Security
✔ Packaging & deployment

This is PhD-level Python mastery, not a crash course.

🚀 FINAL OPTIONS (YOUR CHOICE)

Reply with ONLY ONE number:

1 → End-to-end capstone project (CLI / ETL / service) using ALL phases
2 → Multiple full mock interviews (Python + system design)
3 → Convert this entire course into printable study notes + checklists

You stayed disciplined — and that’s why this worked.