MAHMOUD ABDELNABY
Career · TEMPLATE FRAMEWORK · 1

Marketing Agency Safe AI Adoption & Data Privacy Policy

Deploy AI across your agency safely without client data leaks. Enterprise AI usage policy, RFP disclosure clauses, and automated PII scrubber.

Problem it addresses

Build an open-source Marketing Agency AI Adoption and Privacy Pack: 1) Complete corporate AI usage policy (Approved vs Restricted tools, Model training opt-outs, Copyright guidelines), 2) Client RFP compliance disclosure addendum, 3) Python PII data scrubber script (scrub_pii.py) anonymizing names, phones, and emails before AI prompting, 4) Employee AI onboarding checklist.

Who it is for

PRO operating in high-growth e-commerce, digital publishing, and direct-response marketing across Saudi Arabia, UAE, and Egypt.

What is inside

Key files listed in the README repository architecture:

  • checklists/employee-ai-tool-whitelist.md
  • contracts/client-ai-disclosure-addendum.md
  • LICENSE
  • policies/agency-ai-governance-policy.md
  • policy/agency-ai-data-security-charter.md
  • README.md
  • src/check_pii_leakage.py
  • src/scrub_pii.py
  • templates/client-ai-usage-disclosure.md
  • tests/test_pii.py
  • tests/test_scrubber.py
  • training/employee-ai-dos-and-donts.md

How to evaluate and use it

  1. Read the README first and confirm that the documented problem matches your use case.
  2. Inspect the listed files to find the data, tools, templates, and tests relevant to your task.
  3. Follow the repository’s Quick Start and check prerequisites before running commands.
  4. Compare outputs with documented examples and review limitations before using them in a decision or client workflow.

Quick Start from the README: python -m unittest discover -s tests · python src/check_pii_leakage.py --help

Scope and limitations

  • Client Data Isolation: Prohibits pasting sensitive client PII, financial statements, or unreleased product roadmaps into public AI model prompts.
  • Enterprise Opt-Out Verification: Mandates use of enterprise AI tiers with zero data retention and explicit exclusion from public model training datasets.
  • Human-in-the-Loop Deliverable Accountability: Requires human agency review and fact-checking of every AI-assisted client deliverable before client delivery.

This page summarizes the available documentation; it does not establish performance or business results. Verify data and prerequisites before use.

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