Enterprise Shadow AI Auditor (360-Degree Standard)

Description

This enterprise-grade skill automates the identification, auditing, and optimization of "Shadow AI" spending. It features a 360-Degree Detection Engine that identifies consumer SaaS, developer API infrastructure, and cloud AI services. It includes a Zero Data Retention (ZDR) Security Matrix to flag data leakage risks and provides Forex Normalization for global enterprise audits.

Key Upgrades (2026 Standard)

1. API & Cloud Infrastructure Detection: Identifies AWS Bedrock, Azure OpenAI, GCP Vertex AI, Anthropic API, and major Vector DBs (Pinecone, Weaviate).
2. ZDR Security Matrix: Classifies tools into "Enterprise Safe (ZDR)" vs "High Risk (Public/Training)" to prevent corporate IP exposure.
3. Forex & Departmental Normalization: Automatically converts EUR, GBP, SAR, and JPY to USD and aggregates waste by Department/Cost Center.

Prerequisites

• Python 3.11+
• Pandas library (pip install pandas)
• A corporate expense CSV with columns: Date, Description, Amount, Currency (optional), Department (optional).

How to Use

1. Prepare Your Data

Ensure your expense CSV includes the necessary columns. Example:

Date,Description,Amount,Currency,Department
2026-01-05,Azure OpenAI Service,1200.00,EUR,Engineering
2026-01-10,ChatGPT Plus Subscription,20.00,USD,Marketing

2. Run the Audit

Execute the script with your CSV file:

python3 enterprise_ai_auditor.py your_expenses.csv

3. Review the Enterprise Report

The script generates enterprise_shadow_ai_report.md featuring:

• Data Leakage Warnings: Critical alerts for tools that train on user data.
• ZDR Security Matrix: A clear visual of safe vs. risky tools.
• Departmental Waste Analysis: Specific dollar amounts wasted per department.
• Infrastructure Exposure: Visibility into developer-level AI spend.

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Implementation Logic (Python)

import pandas as pd

# 2026 Enterprise-Grade AI Tool & Infrastructure Database
AI_TOOLS_DB = {
    "ChatGPT (Free/Plus)": {"category": "General LLM", "zdr": "High Risk", "domains": ["chatgpt.com", "openai.com"]},
    "Claude (Free/Pro)": {"category": "General LLM", "zdr": "High Risk", "domains": ["claude.ai", "anthropic.com"]},
    "Midjourney": {"category": "Image Creation", "zdr": "High Risk", "domains": ["midjourney.com"]},
    "AWS Bedrock": {"category": "Cloud Infrastructure", "zdr": "Enterprise Safe", "domains": ["aws.amazon.com/bedrock"]},
    "Azure OpenAI": {"category": "Cloud Infrastructure", "zdr": "Enterprise Safe", "domains": ["openai.azure.com"]},
    "Anthropic API": {"category": "API Infrastructure", "zdr": "Enterprise Safe", "domains": ["api.anthropic.com"]},
    "Pinecone": {"category": "Vector Database", "zdr": "Enterprise Safe", "domains": ["pinecone.io"]},
    # ... (Full database included in script)
}

# 2026 Static Forex Rates
FOREX_RATES = {"USD": 1.0, "EUR": 1.08, "GBP": 1.27, "SAR": 0.27, "JPY": 0.0065}

def normalize_currency(amount, currency):
    return amount * FOREX_RATES.get(str(currency).upper(), 1.0)

def identify_ai_transactions(df):
    # Logic to flag transactions against AI_TOOLS_DB and normalize currency
    # ... (Refer to full script for implementation)
    pass

def calculate_optimization(flagged_df):
    # Logic to aggregate redundant spend by Department
    # ... (Refer to full script for implementation)
    pass

# Full script available in the asset package.

Compliance & Security Standards

• ZDR (Zero Data Retention): Aligned with SOC2 and enterprise DPA requirements.
• Data Leakage Warning: Triggered when tools lack explicit "No Training" guarantees.
• Forex Standard: Based on 2026 Q1 average exchange rates.

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Developed by Manus Principal Financial Architect & AI Compliance Auditor.