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@@ -166,14 +166,16 @@ The `matched_rules` column shows which detection rules matched. The `set_roles`
 
 ## Sanitization
 
-All customer-identifying information removed through a 4-layer iterative pipeline:
+All customer-identifying information has been removed through a comprehensive, iterative four-layer sanitization pipeline. Quality was prioritized over processing speed. The pipeline is [open source](https://github.com/witfoo/dataset-from-precinct6) under the Apache 2.0 license.
 
-1. **Structured field sanitization + Aho-Corasick sweep** (~166,000 patterns) with product identifier protection
-2. **Format-specific message parsing** (8 parsers: Cisco ASA, Windows XML, WinLogBeat, CloudTrail, PAN, VMware, DNS, generic)
-3. **ML/NER residual detection** (Presidio + BERT NER)
-4. **Claude AI contextual review**
+1. **Structured field sanitization + Aho-Corasick multi-pattern sweep** — Known fields are replaced with deterministic tokens (IPs to [RFC 5737](https://datatracker.ietf.org/doc/html/rfc5737) ranges, hostnames to `HOST-NNNN`, etc.). Every record is then swept using an [Aho-Corasick automaton](https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm) built from all 302,000+ registry entries to catch PII in unexpected contexts. Product identifiers are explicitly protected from the sweep.
+2. **Format-specific log message parsing** — Eight specialized parsers handle Cisco ASA syslog, Windows Security Event XML, WinLogBeat JSON, AWS CloudTrail, Palo Alto Networks, VMware vCenter, DNS logs, and a generic fallback — sanitizing PII within structured fields like XML elements, nested JSON, and CSV columns.
+3. **Machine learning residual detection** — [Microsoft Presidio](https://microsoft.github.io/presidio/) (spaCy NLP) and [BERT NER](https://huggingface.co/dslim/bert-base-NER) scan sanitized records for residual PII that pattern-based approaches miss. New discoveries trigger full re-sanitization.
+4. **Large language model contextual review** — [Claude](https://www.anthropic.com/claude) reviews stratified samples for subtle PII: embedded org names, internal hostnames revealing organizational structure, employee names in file paths, AD group names, and geographic identifiers. Findings trigger re-sanitization.
 
-Final PII registry: **~302,000 unique mappings** (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.)
+**Iterative convergence:** The four layers run in cycles. PII discovered by ML/AI in one cycle is caught automatically by Layer 1 in all subsequent cycles across the complete dataset. Cycles repeat until near-zero new discoveries.
+
+**Final PII registry:** ~302,000 unique mappings across 14 categories (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.). All replacements are consistent — the same original value always maps to the same token, preserving graph topology.
 
 ## Graph Data