How to Share Code with AI Safely — Complete Secure AI Coding Workflow
AI coding assistants — ChatGPT, Claude, GitHub Copilot, Gemini, Cursor — are now core developer tools. They write boilerplate, find bugs, optimize queries, and generate tests. But every paste of real code exposes secrets, proprietary logic, and personal data to third-party AI infrastructure. This guide gives you a complete, repeatable workflow for getting full AI help without exposing a single credential or proprietary identifier.
3 steps
Scan → Mask → Send to AI → Restore. Under 60 seconds for any code snippet
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Languages supported: JS, TS, Python, Go, Java, SQL, Ruby, Swift, Kotlin, Bash, YAML, and more
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AI prompt templates: code review, bug report, security audit, performance, docs, refactor, tests, explain
The Core Problem: AI Tools Receive Everything You Send
When you paste code into ChatGPT or Claude, the entire text of that prompt — including any API keys, database URLs, emails, IP addresses, and function names — is transmitted to the AI provider's servers. The AI does not need your real credentials to help you. It only needs the structure and logic of your code. The fix is to replace real values with safe placeholders before sending, then restore them after.
This is code masking, and it works with any AI tool — ChatGPT, Claude, Copilot Chat, Gemini, Codeium, Tabnine, or any future assistant. The masking happens in your browser. The AI never sees your real identifiers.
The Complete Secure Workflow
Step 1 — Open Code Prompt Shield
Go to unblockdevs.com/code-prompt-shield. The tool runs entirely in your browser — nothing is sent to any server, including this one. All masking and restore logic executes in your local browser tab.
Step 2 — Select language and masking options
Select your programming language from 18 options (JavaScript, TypeScript, Python, Go, Java, SQL, JSON, C#, PHP, Rust, Ruby, Swift, Kotlin, Bash, YAML, TOML, C/C++, XML). Enable "Identifiers" to mask variable/function names, "Secrets" to mask API keys and credentials, and "PII" to mask emails, phone numbers, and IP addresses.
Step 3 — Click "Scan first" (optional but recommended)
Before masking, click "Scan first" to see exactly what sensitive content is in your code. The scanner lists every detected secret, PII item, and schema reference with severity level (critical/high/medium/low) and occurrence count. No masking happens yet — this is purely an audit step.
Step 4 — Add custom patterns if needed
Open Advanced Options to add custom regex patterns. If your company uses internal token formats (e.g., CORP-[A-Z]{4}-[0-9]{8}) or proprietary ID schemes, add the pattern here. It will be detected and masked as SECRET_XXXX along with the built-in patterns.
Step 5 — Click "Mask code"
Click Mask code (or press Cmd/Ctrl+Enter). Every identifier, secret, and PII item is replaced with a deterministic placeholder — VAR_XXXX for variables/functions, SECRET_XXXX for credentials, PII_XXXX for personal data, TABLE_XXXX for SQL tables, KEY_XXXX for JSON/YAML keys. The stat badges show exactly how many of each type were masked.
Step 6 — Choose an AI prompt template
Select a template from the dropdown: Code review, Bug report, Security audit, Performance, Documentation, Refactor, Write tests, or Explain code. The template prepends a context-setting instruction that tells the AI to use the same placeholder names in its response — which is essential for clean restoration.
Step 7 — Copy and paste into your AI tool
Click ChatGPT, Claude, Copilot, or Gemini to copy the masked code (with template). Paste into your AI tool. Ask your question. The AI responds with full logic help using the masked placeholder names — it never sees your real identifiers.
Step 8 — Restore original identifiers
Copy the AI's response. Paste it into the Restore panel in Code Prompt Shield. Click Restore. Every VAR_XXXX, SECRET_XXXX, and PII_XXXX placeholder is replaced back with the original real name. The restored code is production-ready.
What Gets Masked — By Category
Category Placeholder format Examples detected
────────────────────────────────────────────────────────
Identifiers VAR_XXXX getUserOrders, fetchPaymentData, calculateChurn
FN_XXXX (Bash function names)
Secrets SECRET_XXXX sk-proj-abc123, AKIA..., eyJh..., postgres://...
ghp_tokens, xox-Slack-tokens, AIza-Google-keys
PII PII_XXXX user@example.com, +1-555-123-4567, 192.168.1.100
SQL tables TABLE_XXXX production.users, analytics.events
SQL columns COL_XXXX customer_email, lifetime_value, churn_risk_score
JSON/YAML keys KEY_XXXX "apiKey": ..., database_url: ...
XML tags TAG_XXXX <UserProfile>, <ApiConfig>
TOML sections SEC_XXXX [database], [production]Same identifier → same placeholder everywhere
The masking is deterministic: getUserOrders always becomes the same VAR_XXXX in every occurrence — in function definitions, calls, imports, and JSDoc comments. This is what makes restoration lossless: every occurrence of the placeholder in the AI response maps back to the same original name.
Choosing the Right AI Prompt Template
The prompt template matters. Telling the AI upfront that it is working with masked code and should preserve placeholder names in its response prevents the AI from renaming variables or using generic names that break restoration. Here are when to use each:
🔍 Code review
Best for: general quality feedback, catching bugs, suggesting better patterns. The AI reviews the masked code and gives feedback referencing VAR_XXXX names. After restoration, the feedback references your real function names.
🐛 Bug report
Best for: a specific bug or unexpected behavior. Describe the bug after the masked code. The AI diagnoses the issue using placeholder names. Restore to get a diagnosis that references your real code.
🛡️ Security audit
Best for: checking for injection vulnerabilities, insecure patterns, missing input validation, or authentication flaws. The AI audits structure and logic — real credentials don't need to be present for a meaningful security review.
⚡ Performance
Best for: database query optimization, algorithm complexity analysis, identifying N+1 problems, suggesting caching strategies. Structural analysis works identically with masked names.
📝 Documentation
Best for: generating docstrings, JSDoc comments, README sections, or API documentation. The AI writes docs using placeholder names; restore to get docs with your real function and parameter names.
🧪 Write tests
Best for: generating unit tests, integration test stubs, or mock data. The AI writes test cases using masked names. After restoration, the tests reference your real functions and classes — ready to run.
Advanced: Custom Patterns for Company-Specific Secrets
The built-in detection covers all major public API key formats. But your company may use internal token formats, proprietary ID schemes, or custom authentication headers that the general patterns do not match. The Advanced Options panel lets you add custom regex patterns:
# Internal session token format
SESS-[A-Za-z0-9]{32}
# Company service account credential
svc-[a-z]+-[0-9]{8}-[a-zA-Z0-9]{16}
# Internal API endpoint with sensitive subdomain
https://[a-z]+-internal.company.com/[a-zA-Z0-9/]+
# Legacy auth token format
AUTH:[A-Z]{2}[0-9]{6}:[a-zA-Z0-9+/]{44}==
# Custom payment reference
PAY-[A-Z]{3}-[0-9]{4}-[A-Z0-9]{8}Any string matching a custom pattern is masked as SECRET_XXXX and fully restored after AI processing.
Team Workflow: Sharing Mapping Files
For team-based AI workflows — where one developer masks code, another asks the AI, and a third restores — the mapping file enables collaboration without breaking the security model:
Download the mapping file for team workflows
Cross-session consistency
Load a saved mapping file at the start of a new session to continue where you left off. Subsequent masks of related code will use the same placeholder assignments for identifiers that appeared in previous sessions — ensuring consistency across multi-session AI workflows.
Mapping file security
The mapping file contains your real function names, variable names, and secret values. Treat it with the same access controls as the secrets themselves. Store it in a secrets manager or encrypted file store — not in a shared Google Drive or Slack channel.
CI/CD integration
For automated code review workflows, the masking logic can be incorporated into pre-commit hooks or CI pipelines. Strip secrets before code reaches any AI-powered analysis tool — treat it as a pre-processing step in your pipeline.
Onboarding developers to the workflow
Add Code Prompt Shield to your developer onboarding documentation alongside your secrets management guidelines. The four-step workflow (paste → scan → mask → send) takes under 60 seconds once learned.
What the AI Can Still Do With Masked Code
The most common concern is that masking reduces AI help quality. It does not. Here is a complete list of what AI tools do equally well with masked code:
AI quality: masked vs. unmasked
What you think AI needs — your real names
// Concern: AI won't help without real names
// WRONG — AI sees this as equivalent structure:
function getUserOrders(userId: string): Promise<Order[]> {
return db.query('SELECT * FROM orders WHERE user_id = $1', [userId]);
}What AI actually needs — just the structure and pattern
// What AI actually receives — structurally identical:
function VAR_ABCD(VAR_EFGH: string): Promise<VAR_IJKL[]> {
return VAR_MNOP.VAR_QRST('SELECT * FROM VAR_UVWX WHERE VAR_YZAB = $1', [VAR_EFGH]);
}
// AI can: type this correctly, optimize the query,
// add error handling, suggest indexes, write tests
// — all without seeing getUserOrders or the real table name.AI tools work equally well with masked code for all of the following:
Logic and algorithm improvements
AI identifies inefficient loops, suggests better data structures, spots unnecessary complexity — all from code structure alone.
Security vulnerability detection
SQL injection patterns, missing input validation, insecure random number usage, hardcoded values — structural issues detectable without real names.
Test generation
AI writes unit tests, edge case tests, and mock data for masked functions. After restoration, tests reference real function names and are immediately runnable.
Documentation generation
Docstrings, JSDoc, type annotations, README sections — all generated from masked code and restored to use real names.