The Intelligent Accessibility Assistant handles potentially sensitive data including source code, DOM structures, and user interface elements. This document outlines security measures and best practices.

1. HTML/DOM Structure: Page markup and accessibility tree
2. CSS Styles: For contrast and visual analysis
3. Screenshots: Optional visual analysis
4. Code Snippets: For fix generation

• User credentials or authentication tokens
• Personal user data or form inputs
• Analytics or telemetry
• Browsing history

• Zero External Calls: All processing happens locally
• No Data Transmission: Nothing leaves your machine
• Rule-Based: Uses curated knowledge base
• Privacy-Preserving: Complete data isolation

When using external AI services:

1. Data Masking: PII is automatically masked before transmission
2. Minimal Context: Only relevant code snippets sent
3. No Credentials: API keys never logged or transmitted
4. Audit Trail: All AI calls logged locally
5. User Control: Explicit opt-in required

Before sending to AI:

• Email addresses → <email>
• Phone numbers → <phone>
• API keys → <api_key>
• Tokens → <token>
• Names (when detected) → <name>

DO:

• Store in environment variables
• Use .env files (gitignored)
• Rotate regularly
• Use least-privilege keys

DON'T:

• Hardcode in source
• Commit to version control
• Share in logs
• Reuse across environments

# .env file (never commit)
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

# Usage
import os
api_key = os.getenv("OPENAI_API_KEY")

• Each test runs in isolated browser context
• Cookies/storage cleared between runs
• No persistent state
• Sandboxed execution

• HTTPS enforced for external URLs
• Certificate validation enabled
• No proxy bypass
• Timeout limits enforced

# Secure secrets usage
- name: Run accessibility test
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
  run: iaa test https://example.com --ai-enabled

1. Use Secrets Management: GitHub Secrets, AWS Secrets Manager, etc.
2. Limit Permissions: Read-only access where possible
3. Audit Logs: Enable logging for compliance
4. Fail Secure: Default to offline mode if credentials missing

• Pin dependency versions in pyproject.toml
• Regular security audits via pip-audit
• Automated updates via Dependabot
• Verify package signatures

# Check for known vulnerabilities
pip-audit

# Update dependencies
pip install --upgrade -e ".[dev]"

• Framework does NOT execute user-provided code
• AI-generated fixes are suggestions only
• Manual review required before applying fixes

• Browser automation runs in isolated process
• No file system access beyond test targets
• Network access limited to test URLs

• No personal data collection
• Data processing happens locally (offline mode)
• User controls all data transmission (online mode)
• Right to deletion: simply delete local reports

• Audit logging available
• Access controls via API keys
• Encryption in transit (HTTPS)
• No data retention (stateless)

• Not recommended for processing PHI/PCI data
• Use offline mode only
• Mask sensitive data before testing
• Consult compliance team

If you discover a security vulnerability:

1. DO NOT open a public issue
2. Email security@example.com
3. Include detailed description
4. Allow 90 days for patch before disclosure

Before deploying IAA:

• API keys stored in environment variables
• .env file in .gitignore
• Offline mode enabled for sensitive data
• Data masking rules reviewed
• Dependency versions pinned
• Security scanning enabled in CI
• Audit logging configured
• Team trained on security practices

• Security patches released immediately
• Subscribe to GitHub security advisories
• Enable Dependabot alerts
• Review CHANGELOG for security notes

Security concerns: security@example.com General support: support@example.com