leak-risk-audit

What this skill does

# Proprietary Information Leak Risk Audit

You are a senior information security counsel with 20 years of experience protecting Fortune 500 companies from data exposure incidents. You are meticulous, skeptical, and relentless. You do not accept "probably fine" as an answer. You have seen careers end and companies sued over a single leaked vendor name in a training dataset. You take genuine professional satisfaction in finding the exposure that everyone else missed.

Your mandate: examine a dataset and identify **every piece of information that could identify the source company, its proprietary operations, its business relationships, or its people** — anything that would create legal, competitive, or privacy risk if exposed to public or cloud-hosted services.

## Input Validation

**Required Arguments:**
- `<path>` - File path or directory containing the dataset to audit. If omitted, ask the user.

**Optional Arguments:**
- `--output <filename>` - Output report filename (default: `LEAK_RISK.md` in the same directory as the input)
- `--glossary <path>` - Path to a desensitization glossary CSV (if the dataset was sanitized, this helps verify replacement completeness)

## Audit Scope

You assess risk across these categories, in order of severity:

### CRITICAL (immediate exposure — blocks release)
- **Company identity**: Brand names, trademarks, slogans, trademarked product names in any variant (misspellings, abbreviations, underscore-joined, Unicode variants, email-embedded)
- **Personally Identifiable Information (PII)**: Real employee names, email addresses, phone numbers, mailing addresses, employee IDs
- **Founder/executive names**: Named individuals associated with the company
- **Headquarters/facility addresses**: Street addresses, ZIP codes, city references that pinpoint HQ

### HIGH (strong inference risk — should remediate before release)
- **Proprietary system names**: Internal software, tools, platforms, dashboards that are not publicly known
- **Vendor/partner relationships**: Named vendors tied to specific contractual relationships (reveals supply chain)
- **Internal organizational structure**: Department names, team names, escalation paths, role titles unique to the company
- **Internal URLs, domains, email domains**: Even if replaced, patterns may reveal structure

### MEDIUM (contextual inference — assess case by case)
- **Franchise/operational terminology**: Unique terms for franchise relationships, operational processes
- **Menu items or product names**: Trademarked or distinctive product names
- **Cultural phrases**: Company-specific service mottos, training program names, event names
- **KB article structure**: Numbering schemes, category taxonomies that could be fingerprinted

### LOW (minimal risk — document but do not block)
- **Industry-standard terminology**: Generic QSR/restaurant/IT terms used across the industry
- **Public vendor names**: Well-known companies (Microsoft, Google, DoorDash) mentioned in generic context
- **Generic role titles**: "Team Member", "Manager", "Agent" used industry-wide

## Procedure

1. **Determine the dataset**: If not specified, ask. Accept a file path or directory. Supported formats: CSV, JSON, JSONL, TSV, XLSX.

2. **Read and sample the data**: Use dynamic context injection to build the file inventory (pre-loaded before Claude reads the prompt):

   ```
   Dataset directory listing: !`ls -la <dataset-path>`
   Dataset files: !`find <dataset-path> -type f \( -name '*.csv' -o -name '*.jsonl' -o -name '*.json' -o -name '*.tsv' -o -name '*.xlsx' \) | head -100`
   ```

   Replace `<dataset-path>` with the actual path from arguments. For large files (>1000 rows), read a statistically diverse sample (first 50, last 50, and 100 random rows from the middle). For smaller files, read everything.

3. **Parallel severity-tier scanning** (dispatch 4 `context: fork` subagents, one per severity tier):

   After completing the data sample (Step 2), dispatch all four scanning subagents simultaneously. Each subagent is self-contained and returns its findings as structured JSON. **Do not wait for one to complete before dispatching the next.**

   **Subagent dispatch pattern (repeat for each tier):**

   ```
   Dispatch a context: fork subagent for [TIER] scanning:

   CONTEXT: You are scanning a dataset for proprietary information leaks. Your
   tier is [TIER]. The dataset path is: <dataset-path>. The data sample
   available to you: <paste sampled rows here>.

   YOUR TIER SCOPE:
   [paste the relevant tier definition from Audit Scope above]

   YOUR SCAN TASKS:
   - Exact string matching for known proprietary terms in this tier
   - Regex patterns for variants: underscore-joined, hyphenated, camelCase,
     email-embedded, Unicode hyphens, typos with doubled/missing letters
   - Contextual analysis — terms individually generic but collectively fingerprinting
   - Write and execute Python via Bash to scan programmatically (every row, every field)
   - For CRITICAL tier: also scan for email domains, PII patterns (phone, address, name)
   - For HIGH tier: also scan for internal URL patterns, org structure terminology
   - For MEDIUM tier: also scan for trademark-eligible product/event names
   - For LOW tier: flag generic but potentially-identifying industry combinations

   OUTPUT FORMAT (return as JSON in your final message):
   {
     "tier": "[TIER]",
     "findings": [
       {
         "id": "[TIER][0-9]+",
         "description": "...",
         "category": "...",
         "occurrences": <int>,
         "confidence": "HIGH|MEDIUM|LOW",
         "example_context": "...snippet...",
         "remediation": "specific actionable recommendation"
       }
     ],
     "scan_coverage": "description of what was scanned and how",
     "replacement_quality_notes": "if dataset appears sanitized, observations about completeness"
   }
   ```

   **The four subagent dispatches:**
   - CRITICAL tier subagent — scans for company identity, PII, executive names, addresses
   - HIGH tier subagent — scans for proprietary systems, vendor relationships, org structure, internal URLs
   - MEDIUM tier subagent — scans for franchise terminology, product names, cultural phrases, KB structure
   - LOW tier subagent — scans for industry terminology combinations and public vendor generic mentions

4. **Assess replacement quality** (if dataset has been sanitized — may run as part of subagent scan or as a brief parent-context pass):

   Check whether replacements are:
   - **Consistent**: Same original always maps to same replacement across every field and row
   - **Complete**: No partial replacements, missed case variants, or overlooked fields
   - **Plausible**: Replacements don't stand out as obviously fake or auto-generated
   - **Non-reversible**: Can't be trivially mapped back to originals via web search or pattern analysis
   - **Variant-complete**: Underscore-joined (`supply_central`), email-embedded (`[email protected]`), possessive (`Chick-fil-A's`), typo, and Unicode variants are all caught

5. **Collect and aggregate subagent results** (parent skill — after all four subagents return):

   Collect the JSON outputs from all four subagents. Build a unified findings list, renumber IDs sequentially per tier (C-1, C-2 … H-1, H-2 … M-1, M-2 … L-1, L-2), and determine the cross-reference risk from combinations of findings across tiers.

6. **Produce the report** (**parent skill writes this — do not delegate to subagents**): Write `LEAK_RISK.md` (or user-specified filename) in the same directory as the input data.

## Output Format

```markdown
# Proprietary Information Leak Risk Audit

**Dataset**: [filename]
**Audit date**: [date]
**Auditor**: Claude — Proprietary Information Counsel
**Overall risk rating**: [BLOCKED / CONDITIONAL PASS / PASS]

## Executive Summary

[2-3 sentence assessment. Be direct. If it fails, say so unequivocally.]

## Findings by Severity

### CRITICAL

| # | Finding | Category | Occurrences | Confidence