90% of Devs Fail at AI Coding – Here’s Anthropic’s Secret 4-Step Workflow That Even Builds Secure Code + Video

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Introduction:

Most developers treat AI coding assistants like Code as a magic “generate and paste” button, skipping critical planning and exploration phases. This leads to inconsistent, often vulnerable, code that fails in production. Anthropic’s internal workflow—Explore, Plan, Implement, Commit—transforms AI from a random guesser into a disciplined security co-pilot when paired with the right commands and hardening techniques.

Learning Objectives:

  • Master the 4‑step Explore → Plan → Implement → Commit workflow to eliminate inconsistent AI output.
  • Integrate automated security scanning (secrets, SAST, dependency checks) into each phase using open‑source tools.
  • Build and enforce pre‑commit hooks that let manage `git` while preventing vulnerable code from reaching production.

You Should Know

  1. The ‘Explore’ Phase – Mapping Your Codebase Like a Security Auditor

What it does:

Before writing a single line, reads files, identifies patterns, and answers questions. This context‑loading phase is where you also discover hardcoded secrets, outdated libraries, and misconfigurations.

Step‑by‑step guide:

  1. Ask to describe the project structure without making changes.
    “Explore the repo. List all configuration files, API keys patterns (e.g., API_KEY=), and known vulnerable functions like `eval()` or exec().”
  2. Manually verify with security tools to augment ’s output:

– Secrets detection (Linux/macOS): 

git init . && trufflehog filesystem --directory .

Windows (PowerShell with WSL or Docker):

docker run --rm -v ${PWD}:/code trufflesecurity/trufflehog filesystem /code

– Dependency vulnerabilities (Linux/WSL): 

 Python
pip-audit
 Node.js
npm audit --json | jq '.advisories'

3. Generate a map of all entry points (routes, API endpoints, CLIs) using `grep` and find: 

grep -r "app.get|router.post|@app.route" --include=".py"

Windows (PowerShell):

Get-ChildItem -Recurse -Include .py | Select-String "app.get|router.post"

4. Ask for a heatmap of files that changed most often – these are your high‑risk areas for regression bugs or security flaws.

  1. The ‘Plan’ Phase – Threat Modeling and Secure Design

What it does:

produces a detailed implementation plan with security checkpoints before any code is written. This replaces random prompting with a formal design review.

Step‑by‑step guide:

  1. Prompt to create a threat model for the planned feature:
    “Plan the new authentication endpoint. Include STRIDE threats (Spoofing, Tampering, Repudiation, Info disclosure, DoS, Elevation of privilege). Output as a table.”
  2. Use `draw.io` (offline) or `threatspec` to visualise data flows: 
    Install threatspec (Linux/macOS)
brew install threatspec/threatspec/threatspec
threatspec init
threatspec generate

3. Request to map controls to each threat:

  • Example: “For Tampering of JWT, plan to use RS256 with key rotation every 24h.”
  1. Validate the plan against OWASP ASVS using a script: 
    Download ASVS checklist
wget https://raw.githubusercontent.com/OWASP/ASVS/master/4.0/asvs-4.0-en.csv
grep -i "authentication" asvs-4.0-en.csv
  2. Ask to rewrite the plan as a series of `TODO:` comments inside code files – this becomes the future implementation contract.

  3. The ‘Implement’ Phase – Test‑Driven Security with Self‑Verification

What it does:

writes code and generates tests that verify correctness and security. The model double‑checks its own output against the plan.

Step‑by‑step guide:

  1. “Implement the plan from step 2. Write unit tests that:

– Assert login fails after 5 wrong attempts (rate limiting)
– Verify tokens expire after 15 minutes
– Check that password hashing uses bcrypt (cost=12)”

2. Run tests automatically (Linux/macOS/WSL):

pytest --cov=. --cov-report=term-missing

Windows (native):

python -m pytest --cov=. --cov-report=html

3. Add a security linter to the test suite – e.g., `bandit` for Python: 

bandit -r . -f json -o bandit_report.json

Or `semgrep` for multi‑language:

semgrep scan --config auto --sarif -o semgrep.sarif

4. Ask to self‑correct by feeding it the test failures:
“The test `test_rate_limiting` failed. Re‑implement the login view using `django-ratelimit` and re‑run the tests.”
5. Verify with dynamic analysis (run a local server and fuzz): 

 Using OWASP ZAP in daemon mode
zap-cli quick-scan -s "http://localhost:8000/login" -r
  1. The ‘Commit’ Phase – Let AI Own Git with Pre‑Commit Hooks

What it does:

stages files, writes commit messages (following Conventional Commits), and pushes only after all security hooks pass. You delegate `git` operations to the AI, but enforce rules with client‑side hooks.

Step‑by‑step guide:

1. Install `pre-commit` framework (Linux/macOS/PowerShell with Python):

pip install pre-commit
pre-commit install

2. Create `.pre-commit-config.yaml` with security checks:

repos:
- repo: https://github.com/Yelp/detect-secrets
rev: v1.4.0
hooks:
- id: detect-secrets
args: ['--baseline', '.secrets.baseline']
- repo: https://github.com/gitleaks/gitleaks
rev: v8.18.0
hooks:
- id: gitleaks
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.4.0
hooks:
- id: ruff
args: [--fix, --unsafe-fixes]

3. Tell to run the pre-commit scan before any commit:
“Stage all changed files, run pre-commit run --all-files, if passes then commit with message ‘feat(auth): implement rate‑limited login’ and push to origin/main.”
4. Allow to manage `git blame` safely – restrict its permissions via environment variables: 

export GIT_AUTHOR_NAME=""
export GIT_AUTHOR_EMAIL="@anthropic"

5. Set up a server‑side hook (GitHub Actions) to prevent merging if tries to bypass: 

 .github/workflows/pre-merge.yml
name: Security
on: pull_request
jobs:
pre-commit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: pip install pre-commit && pre-commit run --all-files

5. Hardening AI‑Generated Code Against Common Vulnerabilities

What it does:

often generates SQL injection, XSS, or command injection flaws when prompted ambiguously. This section shows how to prompt for safe code and then verify it.

Step‑by‑step guide:

1. Always ask for parameterised queries (not concatenation):

Bad prompt: “Write a SQL search for username.”

Good prompt: “Write a SQL search using `psycopg2.sql` with placeholders – never use f‑strings or %.”
2. Add a static analysis rule to catch dangerous patterns:
– Linux (using `grep` as a quick check): 

grep -rn "execute(\"%|execute(f\"|execute('\" --include=".py"

– Windows (PowerShell): 

Select-String -Path .py -Pattern 'execute("%|execute(f"|execute('"'`

3. Run a SAST tool that understands AI patterns – `CodeQL` (free for open source): 

codeql database create ./db --language=python --source-root=.
codeql database analyze ./db --format=sarif-latest --output=codeql.sarif

4. Create a prompt template to force to output safe code:

“You are a security‑aware senior developer. Never use eval, exec, system, `subprocess` with shell=True, or raw SQL strings. If you must, explain the risk and ask for approval.”

5. Manually test injection vulnerabilities using `sqlmap`:

  sqlmap -u "http://localhost/search?q=test" --batch --level=2
  1. Cloud and API Security Integration with AI Workflows

What it does:

Extend the 4‑step workflow to infrastructure‑as‑code (IaC) and API gateways. can write Terraform, CloudFormation, or Kubernetes manifests with security baked in.

Step‑by‑step guide:

  1. Ask to “Explore” your cloud config for misconfigurations:
    “Read main.tf. List all security groups with port 22 open to 0.0.0.0/0 – do not change anything.”

2. Use `checkov` to automatically scan IaC (Linux/macOS/WSL):

checkov -d ./terraform --framework terraform --output json --output-file-path .

3. During “Plan”, instruct to enforce least privilege on IAM roles:
“Plan an S3 bucket policy. Never use "Action": "s3:". Generate only specific actions like s3:GetObject.”
4. In “Implement”, generate API security headers (CSP, HSTS, CORS) for AWS API Gateway or Azure Front Door: 

 CloudFormation snippet from 
ApiGatewayMethod:
ResponseParameters:
method.response.header.Strict-Transport-Security: "'max-age=31536000'"

5. Commit phase – run `tflint` and `tfsec` before allowing to push: 

tflint --chdir=./terraform
tfsec ./terraform

Windows: Use Docker: `docker run –rm -v ${PWD}:/src aquasec/tfsec /src`

  1. Training Your Team on the 4‑Step Secure AI Workflow

What it does:

Turn individual workflows into organisational standards. Use the LinkedIn‑shared URL https://lnkd.in/gcmtcg6A for structured AI courses and https://lnkd.in/g_zZqhvq for a weekly AI security newsletter.

Step‑by‑step guide:

  1. Create an internal “ Code Security Playbook” containing:

– The four phases (Explore, Plan, Implement, Commit)
– Mandatory pre‑commit hooks (as configured in Section 4)
– Forbidden prompt patterns (e.g., “write this without tests”)
2. Schedule a live lab where teams run trufflehog, bandit, and `pre-commit` on their own repos.

3. Leverage the provided learning resources:

  • Use https://lnkd.in/gcmtcg6A to enrol in “AI for Secure Development” course (includes modules on prompt injection, AI‑generated code review).
  • Subscribe to the newsletter at https://lnkd.in/g_zZqhvq to get weekly case studies of AI‑related security fails and fixes.
  1. Set up a Slack / Teams bot that posts the commit summary plus the output of `gitleaks` and `semgrep` for every `main` branch push.
  2. Measure improvement by tracking the number of security findings in ‑generated code before vs. after the training – use `defectdojo` to aggregate: 
    Upload SAST reports to DefectDojo
curl -X POST https://your-dojo/api/v2/import-scan/ -H "Authorization: Token $API_KEY" -F "file=@bandit_report.json"

What Undercode Say:

  • Key Takeaway 1: Inconsistent AI output is almost always a missing “Explore” phase – not a model failure. Running security commands like `trufflehog` or `gitleaks` during Explore turns into a proactive vulnerability hunter.
  • Key Takeaway 2: Pre‑commit hooks are the only way to make respect security boundaries. Without them, the “Commit” phase is trust‑based; with them, you enforce SAST, secrets detection, and linting before every AI‑driven push.

Analysis: The hype around AI coding assistants has focused on speed, but security and consistency lag behind. Anthropic’s internal workflow, when combined with open‑source tooling, gives defenders a repeatable process to harden AI outputs. The most dangerous pattern is skipping planning – it leads to hard‑to‑find vulnerabilities buried inside AI‑generated boilerplate. Organisations that embed bandit, checkov, and pre‑commit into ’s git workflow will see a 60‑80% reduction in low‑effort bugs (injection, exposed secrets) within two sprints. The future is not “AI replaces developers” but “developers who use AI with security pipelines replace those who don’t.”

Prediction:

Within 18 months, major CI/CD platforms (GitLab, GitHub Actions, Jenkins) will ship native “AI workflow steps” that wrap prompts with mandatory security scanning. Code and similar tools will be rejected by enterprise git servers unless they can prove an Explore plan was produced and all pre‑commit hooks passed. Consequently, the demand for cross‑skilled engineers who understand `semgrep` rules, Terraform policies, and prompt engineering will skyrocket, while “vibe‑coded” projects become the leading cause of data breaches. Learning the 4‑step workflow today is not optional – it is the new baseline for professional software development.

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