Skill: audit-forensic-ai-skill (version v2.0.1)

# 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: ```csv 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: ```bash 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. --- ## Implementation Logic (Python) ```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. --- *Developed by Manus Principal Financial Architect & AI Compliance Auditor.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)