Architect grounding boundary: redaction/re-hydration privacy gate (v0.1.0:55)
Phase 1 Workstream D. Lets the Architect ground the thesis in REAL recurring LP objections without any LP identity reaching the Claude API. Layered, defense-in-depth, fail-closed by construction (docs/redaction-rehydration.md). backend/redaction/: - scrub.py: the leak-proof core. Drops Tier-1 (labelled/structured account/wire/SSN/ IBAN/SWIFT/passport, separator-tolerant); tokenizes known LP entities (dictionary from the canonical layer, unicode-folded + hyphen-extended) and structured PII (emails, scheme-less/social URLs, intl+ext phones, currency-cued amounts, ISO/worded/numeric/ quarter dates, addresses, bare long digit runs); pre-neutralizes injected [TYPE_N] strings; single-pass rehydrate; metadata-only audit logging (the pseudonym map is the de-anon key — local-only, never logged/sent). Hardened across THREE adversarial leak-hunts (worded/coded amounts, intl phones, NFD/ligature/zero-width names, slash/ comma SSN, SWIFT, alpha-prefixed accounts, substance-preserving false-positive fixes). - client.py: Boundary — one scrub/rehydrate contract, SCRUB_BACKEND=local (default) or gateway (Spark Control /scrub + /rehydrate). Fails closed (db_path required; dictionary build errors propagate; strict rehydrate returns tokenized-not-de-anon text). - test_scrub_leak.py, test_reidentification.py: golden-file leak + re-identification suites (synthetic only, guardrail #9), regression-locking every leak-hunt vector. backend/mcp/architect_grounding.py: the flow — retrieve (local) -> minimize-first (local Qwen) -> scrub (+ local-Qwen NER backstop for unknown names) -> Claude over the de-identified register only -> re-hydrate locally -> human review. FAILS CLOSED if the local model is unreachable or a hallucinated token appears. test_grounding_boundary.py proves nothing sensitive reaches Claude and the three fail-closed paths. server.py: POST /api/architect/ground (admin) wires retrieval -> ground_objections. docker_entrypoint.sh: SCRUB_BACKEND (default local). docs/spark-control-scrub-endpoints.md: the gateway handover spec (Option 1 — caller supplies the entity dictionary). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Keysat
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"""Architect grounding (Phase 1 Workstream D) — ground the thesis in real LP feedback
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WITHOUT leaking identity, by routing the Claude-facing synthesis through the redaction
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boundary. The corpus is a defensibility oracle, not a generator.
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Flow (redaction-rehydration.md):
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retrieve (local CRM)
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-> MINIMIZE local Qwen condenses raw feedback into anonymous objection THEMES
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("first ask if Claude needs the record content at all")
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-> SCRUB tokenize any residual identifiers (defense-in-depth, leak-proof core)
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-> REASON Claude synthesizes the strongest honest rebuttals on the DE-IDENTIFIED
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register (placeholders only)
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-> RE-HYDRATE map placeholders back locally
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-> HUMAN a partner reviews before anything is used (guardrail #4).
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The Architect only drafts; nothing is sent outbound. Every scrub/rehydrate is logged
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(counts only) to interaction_log. minimize_fn / claude_fn are injectable so the boundary
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enforcement is testable offline without Spark or Claude.
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"""
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "redaction"))
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from client import Boundary # noqa: E402
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_MINIMIZE_SYSTEM = (
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"You are a local privacy-preserving summarizer running on Ten31's own infrastructure. "
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"Given raw notes about investor conversations, extract ONLY the recurring objections, "
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"concerns, and sentiment as generic themes. STRIP every identifier: no names, firms, "
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"emails, amounts, dates, or places. Output a short de-identified bullet list of distinct "
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"objection themes with a rough frequency. Never include who said it.")
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_NER_SYSTEM = (
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"You are a local NER extractor running on Ten31's own infrastructure. Return STRICT "
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"JSON only: {\"entities\":[{\"text\":\"...\",\"type\":\"PERSON|ORG|LOC\"}]}. Extract every "
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"person name, organization/firm/fund name, and specific location mentioned. Include "
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"partial names and nicknames. No commentary, JSON only.")
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def _minimize_local(feedback_items, segment_key):
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"""Condense raw LP feedback into anonymous objection themes on the LOCAL model
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(Spark Control /v1/chat/completions). RAISES if the local model is unreachable — the
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caller must then FAIL CLOSED, because without the local model neither this nor the
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NER backstop can run, and raw prose must never reach Claude unprotected."""
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raw = "\n".join(f"- {t}" for t in feedback_items if t)
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "ingest"))
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import llm # noqa: E402
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out = llm.chat(f"Segment: {segment_key or 'all'}\nRaw notes:\n{raw}\n\nDe-identified objection themes:",
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system=_MINIMIZE_SYSTEM, max_tokens=500)
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if not (out or "").strip():
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raise RuntimeError("local minimize produced no output")
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return out
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def _ner_local(text):
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"""Local-Qwen NER backstop for UNKNOWN names not in the CRM dictionary. Returns
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[(surface, type)]. RAISES on a connection failure (caller fails closed); an empty
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result (model reachable, found nothing) is fine."""
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "ingest"))
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import llm # noqa: E402
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data = llm.chat_json(f"Text:\n{text}\n\nEntities JSON:", system=_NER_SYSTEM, max_tokens=700)
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if not data:
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return []
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return [(e.get("text"), e.get("type")) for e in (data.get("entities") or []) if e.get("text")]
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def _synthesize_claude(register_text, segment_key):
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"""Claude drafts the strongest honest rebuttals over the DE-IDENTIFIED register.
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Reuses the Architect's Claude client. Raises if not configured (handled by caller)."""
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import architect_agent as aa # noqa: E402
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client = aa._client()
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user = (
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# NB: the raw segment_key is intentionally NOT interpolated here — it is an
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# admin free-text field and must not become an un-scrubbed channel to Claude.
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"Below is a DE-IDENTIFIED register of recurring LP objections for one of our LP "
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"segments. Every identifier is a placeholder like [PERSON_1] or "
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"[ORG_1]; keep all placeholders intact and never invent new ones.\n\n"
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f"{register_text}\n\n"
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"For each distinct objection, draft Ten31's strongest HONEST rebuttal grounded in our "
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"thesis, and flag each as 'substantiated' or 'hand-wavy'. Also do a brief counter-evidence "
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"sweep (where might the objection be right?). Return readable markdown.")
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resp = client.messages.create(
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model=aa.MODEL, max_tokens=2000,
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system=[{"type": "text",
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"text": "You are the Architect, pressure-testing Ten31's thesis against real LP "
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"objections. " + aa.VOICE,
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"cache_control": {"type": "ephemeral"}}],
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messages=[{"role": "user", "content": user}])
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return "".join(b.text for b in resp.content if getattr(b, "type", None) == "text")
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def ground_objections(feedback_items, segment_key=None, db_path=None, actor="architect",
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minimize_fn=None, claude_fn=None, ner_fn="default", conn=None):
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"""Build a grounded objection register through the redaction boundary.
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FAILS CLOSED at every step: no Claude call happens unless the local minimize ran and
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the scrub (dict + regex + NER backstop) succeeded; a hallucinated token in Claude's
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output quarantines the draft. Always flagged needs_human_review (guardrail #4)."""
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minimize_fn = minimize_fn or _minimize_local
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claude_fn = claude_fn or _synthesize_claude
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if ner_fn == "default":
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ner_fn = _ner_local # local-Qwen backstop for unknown names (None disables it, for tests)
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base = {"segment_key": segment_key, "needs_human_review": True}
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# 1) MINIMIZE locally. If the local model is unreachable -> FAIL CLOSED (no raw onward).
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try:
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themes = minimize_fn(feedback_items, segment_key)
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except Exception as exc:
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return {**base, "status": "local_model_unavailable", "reason": str(exc)}
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# 2) SCRUB. The Boundary fails closed if db_path is missing or the dictionary can't be
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# built, and the NER backstop propagates a local-model failure here too.
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try:
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boundary = Boundary(db_path=db_path, actor=actor, ner_fn=ner_fn)
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# bucket=False: tokenize amounts/dates REVERSIBLY. The objection register needs no
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# magnitudes, so opaque placeholders both remove any inference signal AND avoid the
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# irreversible substance corruption a coarse bucket could introduce on a misparse.
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scrubbed = boundary.scrub([themes], task_id=f"ground:{segment_key or 'all'}", bucket=False, conn=conn)
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except Exception as exc:
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return {**base, "status": "scrub_unavailable", "reason": str(exc)}
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register = scrubbed["items"][0]
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handle = scrubbed["handle"]
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out = {**base, "scrubbed_register": register, "scrub_stats": scrubbed["stats"]}
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# 3) REASON on Claude over the de-identified register only.
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try:
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draft = claude_fn(register, segment_key)
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except Exception as exc:
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boundary.forget(handle)
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return {**out, "status": "claude_not_configured", "reason": str(exc)}
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# 4) RE-HYDRATE (strict). A residual placeholder = a Claude-hallucinated/smuggled token
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# -> HARD STOP: quarantine the draft, never surface a de-anonymized version.
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rehy = boundary.rehydrate(draft, handle, strict=True, conn=conn)
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boundary.forget(handle)
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if rehy.get("error"):
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return {**out, "status": "rehydrate_failed", "draft_quarantined": True,
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"unknown_tokens": rehy.get("unknown_tokens")}
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return {**out, "status": "ok", "draft": rehy["text"]}
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#!/usr/bin/env python3
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"""Boundary-enforcement + FAIL-CLOSED test for the Architect grounding flow.
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Proves: (1) whatever reaches Claude is de-identified and the draft is re-hydrated locally;
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(2) the local-Qwen NER backstop tokenizes UNKNOWN names not in the CRM dictionary;
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(3) the flow FAILS CLOSED — no Claude call when the local model is unavailable, the scrub
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refuses without a db_path, and a Claude-hallucinated token quarantines the draft.
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Offline + synthetic (guardrail #9): minimize, Claude, and NER are injected as stubs.
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Run: cd backend && python3 mcp/test_grounding_boundary.py
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"""
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import os
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import sqlite3
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import sys
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import tempfile
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import architect_grounding as G # noqa: E402
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "redaction"))
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from client import Boundary # noqa: E402
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FAILS = []
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def check(cond, msg):
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print((" PASS " if cond else " FAIL ") + msg)
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if not cond:
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FAILS.append(msg)
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def make_db():
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db = os.path.join(tempfile.mkdtemp(), "crm.db")
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c = sqlite3.connect(db)
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c.executescript("""
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CREATE TABLE canonical_entities (id TEXT PRIMARY KEY, entity_kind TEXT, display_name TEXT, primary_email TEXT, deleted_at TEXT);
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CREATE TABLE contacts (id TEXT PRIMARY KEY, first_name TEXT, last_name TEXT, email TEXT, deleted_at TEXT);
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CREATE TABLE interaction_log (id TEXT PRIMARY KEY, ts TEXT, actor_type TEXT, actor_id TEXT, action TEXT,
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target_type TEXT, target_id TEXT, payload TEXT, source TEXT, created_at TEXT);
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""")
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c.execute("INSERT INTO canonical_entities VALUES ('per_1','person','Jonathan Reyes','jon@cedarpoint.example',NULL)")
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c.execute("INSERT INTO canonical_entities VALUES ('inv_1','investor','Cedar Point Capital',NULL,NULL)")
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c.execute("INSERT INTO contacts VALUES ('c1','Jonathan','Reyes','jon@cedarpoint.example',NULL)")
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c.commit()
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c.close()
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return db
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FEEDBACK = [
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"Jonathan Reyes at Cedar Point Capital (jon@cedarpoint.example) is cooling; Reyes wants better terms "
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"and a $5,000,000 minimum. Wire acct 000123456789 flagged. Objection: fee load and lock-up.",
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"Another LP echoed the lock-up concern and questioned the energy thesis timeline.",
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]
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SENSITIVE = ["Jonathan Reyes", "Reyes", "Cedar Point Capital", "jon@cedarpoint.example", "$5,000,000", "000123456789"]
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def main():
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db = make_db()
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conn = sqlite3.connect(db)
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passthrough = lambda items, seg: "\n".join(items) # worst case: no minimization
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# ── A) deterministic enforcement (NER off): nothing sensitive reaches Claude ──
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print("\n[A — deterministic enforcement]")
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captured = {}
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res = G.ground_objections(FEEDBACK, segment_key="institution", db_path=db, conn=conn,
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minimize_fn=passthrough, ner_fn=None,
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claude_fn=lambda reg, seg: (captured.__setitem__("sent", reg), reg)[1])
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check(res.get("status") == "ok", f"grounding ok (status={res.get('status')})")
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for v in SENSITIVE:
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check(v not in captured.get("sent", ""), f"de-identified payload to Claude has NO {v!r}")
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check("fee load" in captured.get("sent", ""), "objection substance survives to Claude")
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check("000123456789" not in res.get("draft", ""), "Tier-1 account number never re-hydrated")
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check("Jonathan Reyes" in res.get("draft", ""), "rehydrate restored real Tier-2 values locally")
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blob = " ".join(r[0] for r in conn.execute("SELECT payload FROM interaction_log WHERE action LIKE 'redaction.%'"))
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check(all(v not in blob for v in SENSITIVE), "interaction_log carries NO sensitive value")
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# ── B) NER backstop tokenizes an UNKNOWN name not in the CRM dictionary ──
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print("\n[B — NER backstop for unknown names]")
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cap2 = {}
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fb = ["New intro: Penelope Ashworth-Vane runs a family office and is cooling on the lock-up."]
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ner_stub = lambda text: [("Penelope Ashworth-Vane", "PERSON")]
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res2 = G.ground_objections(fb, db_path=db, conn=conn, minimize_fn=passthrough, ner_fn=ner_stub,
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claude_fn=lambda reg, seg: (cap2.__setitem__("sent", reg), reg)[1])
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check("Penelope Ashworth-Vane" not in cap2.get("sent", ""), "unknown name tokenized by NER backstop (absent from Claude payload)")
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check("Penelope Ashworth-Vane" in res2.get("draft", ""), "unknown name re-hydrated locally for the human")
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# ── C) FAIL CLOSED: local model unavailable -> no Claude call ──
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print("\n[C — fail closed: local model down]")
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called = {"claude": False}
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def boom(items, seg):
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raise RuntimeError("Spark Control unreachable")
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res3 = G.ground_objections(FEEDBACK, db_path=db, conn=conn, minimize_fn=boom, ner_fn=None,
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claude_fn=lambda reg, seg: called.__setitem__("claude", True) or reg)
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check(res3.get("status") == "local_model_unavailable", f"status local_model_unavailable (got {res3.get('status')})")
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check(called["claude"] is False, "Claude was NOT called when minimize failed (fail closed)")
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# ── D) FAIL CLOSED: a Claude-hallucinated token quarantines the draft ──
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print("\n[D — fail closed: hallucinated token]")
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res4 = G.ground_objections(FEEDBACK, db_path=db, conn=conn, minimize_fn=passthrough, ner_fn=None,
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claude_fn=lambda reg, seg: reg + " Also loop in [PERSON_99].")
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check(res4.get("status") == "rehydrate_failed", f"status rehydrate_failed (got {res4.get('status')})")
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check("draft" not in res4 and res4.get("draft_quarantined"), "de-anonymized draft quarantined, not returned")
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# ── E) FAIL CLOSED: local scrub backend requires a db_path ──
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print("\n[E — fail closed: missing db_path]")
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raised = False
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try:
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Boundary(db_path=None, backend="local")
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except ValueError:
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raised = True
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check(raised, "Boundary(local) without db_path raises (never runs name-blind)")
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res5 = G.ground_objections(FEEDBACK, db_path=None, conn=conn, minimize_fn=passthrough, ner_fn=None,
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claude_fn=lambda reg, seg: reg)
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check(res5.get("status") == "scrub_unavailable", f"grounding fails closed without db_path (got {res5.get('status')})")
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conn.close()
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print()
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if FAILS:
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print(f"FAILED ({len(FAILS)}):")
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for f in FAILS:
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print(f" - {f}")
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sys.exit(1)
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print("ALL PASS (grounding boundary enforcement + fail-closed)")
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if __name__ == "__main__":
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main()
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"""Scrub/rehydrate CLIENT — one contract, two backends, switched by SCRUB_BACKEND.
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SCRUB_BACKEND=local (default) -> the in-repo deterministic scrubber (scrub.py);
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the known-entity dictionary is built from the CRM
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and the pseudonym map is held in this process.
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SCRUB_BACKEND=gateway -> Spark Control POST /scrub + /rehydrate (the eventual
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bypass-proof enforcement point; the map lives on the
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Spark). Same request/response shapes, so the Architect
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||||||
|
grounding code never changes when we flip the switch.
|
||||||
|
|
||||||
|
Agents call THIS, never scrub.py directly, so enforcement can move to the gateway with no
|
||||||
|
code change. The local map registry is in-process and short-lived (one grounding task).
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import uuid
|
||||||
|
|
||||||
|
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||||
|
import scrub as R # noqa: E402
|
||||||
|
|
||||||
|
SCRUB_BACKEND = os.environ.get("SCRUB_BACKEND", "local").lower()
|
||||||
|
|
||||||
|
# in-process token maps for the local backend, keyed by opaque handle (the map is the
|
||||||
|
# de-anon key — kept local, never serialized to a prompt or to interaction_log).
|
||||||
|
_MAPS = {}
|
||||||
|
_KNOWN_CACHE = {}
|
||||||
|
|
||||||
|
|
||||||
|
def _known_entities(db_path):
|
||||||
|
"""Build the CRM known-entity dictionary. FAIL CLOSED: never substitute an empty
|
||||||
|
dictionary on error (that would silently run the scrubber name-blind) — propagate
|
||||||
|
the exception so the caller refuses to emit. A legitimately-empty CRM is fine; a
|
||||||
|
failed READ is not, and the two must not be conflated."""
|
||||||
|
if not db_path:
|
||||||
|
raise ValueError("redaction: db_path is required for the local scrub backend (fail closed)")
|
||||||
|
if db_path not in _KNOWN_CACHE:
|
||||||
|
_KNOWN_CACHE[db_path] = R.build_known_entities(db_path) # raises on read failure
|
||||||
|
return _KNOWN_CACHE[db_path]
|
||||||
|
|
||||||
|
|
||||||
|
class Boundary:
|
||||||
|
"""The redaction boundary an agent routes Claude-bound LP context through.
|
||||||
|
|
||||||
|
ner_fn (text -> [(surface, type)]) is the local-model NER backstop for UNKNOWN
|
||||||
|
names the dictionary can't know — the single largest residual. In production the
|
||||||
|
grounding flow passes the local-Qwen NER here; without it the dictionary+regex path
|
||||||
|
is the floor, so callers must minimize-first and fail closed if the local model is down.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, db_path=None, actor="architect", backend=None, ner_fn=None):
|
||||||
|
self.db_path = db_path
|
||||||
|
self.actor = actor
|
||||||
|
self.backend = (backend or SCRUB_BACKEND)
|
||||||
|
self.ner_fn = ner_fn
|
||||||
|
# db_path required for BOTH backends: the CALLER supplies the known-entity dictionary
|
||||||
|
# (Option 1) so Spark Control stays generic/portable and needs no CRM access; the
|
||||||
|
# gateway only adds its local-Qwen NER backstop on top.
|
||||||
|
if not db_path:
|
||||||
|
raise ValueError("redaction: db_path is required (the caller supplies the entity dictionary; fail closed)")
|
||||||
|
|
||||||
|
# ── scrub ──
|
||||||
|
def scrub(self, texts, task_id=None, bucket=True, conn=None):
|
||||||
|
"""De-identify a list of texts under ONE shared token space. Returns
|
||||||
|
{handle, items:[scrubbed,...], stats}. The real->token map is retained
|
||||||
|
locally (local backend) or on the gateway (keyed by handle)."""
|
||||||
|
task_id = task_id or f"task_{uuid.uuid4().hex[:12]}"
|
||||||
|
if self.backend == "gateway":
|
||||||
|
return self._scrub_gateway(texts, task_id, bucket)
|
||||||
|
# local — known dict (fail-closed) + the NER backstop for unknown names
|
||||||
|
state = R.ScrubState()
|
||||||
|
known = _known_entities(self.db_path)
|
||||||
|
items, last_audit = [], None
|
||||||
|
for t in texts:
|
||||||
|
out, _m, audit = R.scrub(t, known_entities=known, bucket=bucket, state=state, ner_fn=self.ner_fn)
|
||||||
|
items.append(out)
|
||||||
|
last_audit = audit
|
||||||
|
handle = f"mh_{uuid.uuid4().hex[:16]}"
|
||||||
|
_MAPS[handle] = dict(state.token_map)
|
||||||
|
if conn is not None and last_audit is not None:
|
||||||
|
try:
|
||||||
|
R.log_scrub(conn, self.actor, last_audit, task=task_id, session_id=handle, source="mcp")
|
||||||
|
conn.commit()
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
return {"handle": handle, "items": items,
|
||||||
|
"stats": {"tokens": len(state.token_map), "tier1_dropped": len(state.tier1_dropped)}}
|
||||||
|
|
||||||
|
def _scrub_gateway(self, texts, task_id, bucket):
|
||||||
|
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "ingest"))
|
||||||
|
import config, http_util # noqa: E402
|
||||||
|
# Option 1: WE build the dictionary from the CRM and supply it, so the gateway needs
|
||||||
|
# no CRM access. It is sensitive (a slice of the LP list) but goes only to the
|
||||||
|
# sovereign Spark and must be held transiently with the map, never logged/forwarded.
|
||||||
|
body = {"task_id": task_id, "actor": self.actor,
|
||||||
|
"items": [{"id": str(i), "text": t} for i, t in enumerate(texts)],
|
||||||
|
"known_entities": _known_entities(self.db_path),
|
||||||
|
"tier1_action": "drop", "bucket": {"amounts": bucket, "dates": bucket}, "ner": "auto"}
|
||||||
|
status, data = http_util.request("POST", f"{config.SPARK_CONTROL_URL}/scrub", body, verify=config.SPARK_VERIFY_TLS)
|
||||||
|
if status != 200:
|
||||||
|
raise RuntimeError(f"/scrub -> {status}: {data}")
|
||||||
|
return {"handle": data["map_handle"], "items": [it["scrubbed_text"] for it in data["items"]],
|
||||||
|
"stats": data.get("stats", {})}
|
||||||
|
|
||||||
|
# ── rehydrate ──
|
||||||
|
def rehydrate(self, text, handle, strict=True, conn=None, human_decision="pending", reviewer_id=None):
|
||||||
|
"""Substitute real values back in. `strict` flags any placeholder with no map
|
||||||
|
entry (a Claude-hallucinated/smuggled token) instead of silently passing it."""
|
||||||
|
if self.backend == "gateway":
|
||||||
|
return self._rehydrate_gateway(text, handle, strict)
|
||||||
|
token_map = _MAPS.get(handle, {})
|
||||||
|
out = R.rehydrate(text, token_map)
|
||||||
|
residual = R.residual_tokens(out)
|
||||||
|
if strict and residual:
|
||||||
|
# FAIL CLOSED: a token with no map entry means Claude hallucinated/smuggled a
|
||||||
|
# placeholder. Do NOT return the de-anonymized text alongside the error — hand
|
||||||
|
# back the still-tokenized input so no real value is materialized.
|
||||||
|
return {"text": text, "unknown_tokens": residual, "error": "unknown_tokens"}
|
||||||
|
if conn is not None:
|
||||||
|
try:
|
||||||
|
R.log_rehydrate(conn, self.actor, tokens_rehydrated=len(token_map), residual=len(residual),
|
||||||
|
human_decision=human_decision, reviewer_id=reviewer_id, session_id=handle, source="mcp")
|
||||||
|
conn.commit()
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
return {"text": out, "unknown_tokens": residual}
|
||||||
|
|
||||||
|
def _rehydrate_gateway(self, text, handle, strict):
|
||||||
|
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "ingest"))
|
||||||
|
import config, http_util # noqa: E402
|
||||||
|
body = {"task_id": handle, "map_handle": handle, "actor": self.actor,
|
||||||
|
"items": [{"id": "0", "text": text}], "strict": strict}
|
||||||
|
status, data = http_util.request("POST", f"{config.SPARK_CONTROL_URL}/rehydrate", body, verify=config.SPARK_VERIFY_TLS)
|
||||||
|
if status != 200:
|
||||||
|
return {"text": text, "unknown_tokens": [], "error": f"rehydrate {status}"}
|
||||||
|
return {"text": data["items"][0]["rehydrated_text"], "unknown_tokens": data.get("stats", {}).get("unknown_tokens", [])}
|
||||||
|
|
||||||
|
def forget(self, handle):
|
||||||
|
"""Drop the local map for a finished task (the de-anon key is short-lived)."""
|
||||||
|
_MAPS.pop(handle, None)
|
||||||
@@ -0,0 +1,411 @@
|
|||||||
|
"""Redaction / re-hydration boundary — the privacy gate between Ten31's sovereign
|
||||||
|
data and the Claude API. Implements docs/redaction-rehydration.md, hardened against an
|
||||||
|
adversarial leak-hunt (see docs/spark-control-scrub-endpoints.md for the gateway twin).
|
||||||
|
|
||||||
|
Defense in depth — NO single layer is trusted as "leak-proof":
|
||||||
|
1. MINIMIZE-FIRST (caller): a local-Qwen summary strips most identity before scrub runs.
|
||||||
|
2. PRE-NEUTRALIZE: any pre-existing [TYPE_N]-shaped string in the input is tokenized
|
||||||
|
first, so every placeholder that reaches Claude is one WE minted (no injection).
|
||||||
|
3. TIER-1 DROP: labelled/structured account-wire-SSN-IBAN-passport data, separator
|
||||||
|
tolerant, excised entirely (never tokenized, never in the map).
|
||||||
|
4. KNOWN-ENTITY tokenize: the LP identities we own (dictionary from the canonical
|
||||||
|
layer), matched UNICODE-FOLDED (accents/case) with hyphenated-surname extension.
|
||||||
|
5. STRUCTURED-PII tokenize/bucket: emails, URLs (incl. scheme-less/social), phones
|
||||||
|
(intl + extensions), amounts (currency words/codes/symbols + worded + ranges),
|
||||||
|
dates (ISO + worded + numeric + quarter), street addresses, bare long digit runs.
|
||||||
|
6. NER BACKSTOP (ner_fn, on-infra local Qwen): tokenizes residual unknown person/org/
|
||||||
|
location names the dictionary can't know. Unknown names are the largest residual,
|
||||||
|
so callers in production pass ner_fn and FAIL CLOSED if it is unreachable.
|
||||||
|
|
||||||
|
The pseudonym map ({token: real_value}) is the de-anonymization key: local-only, NEVER
|
||||||
|
sent to Claude, NEVER written to interaction_log (only counts).
|
||||||
|
"""
|
||||||
|
import json
|
||||||
|
import re
|
||||||
|
import sqlite3
|
||||||
|
import unicodedata
|
||||||
|
import uuid
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
|
||||||
|
TOKEN_TYPES = ("PERSON", "ORG", "FUND", "EMAIL", "PHONE", "URL", "ADDR", "AMOUNT", "DATE", "LOC", "MISC")
|
||||||
|
_TOKEN_RE = re.compile(r"\[(?:" + "|".join(TOKEN_TYPES) + r")_\d+\]")
|
||||||
|
|
||||||
|
# ── Tier-1: NEVER-SEND (dropped, not tokenized). Separator-tolerant + label-anchored. ──
|
||||||
|
# Separators allow space/dot/dash/SLASH/COMMA so grouped account/SSN forms can't bypass.
|
||||||
|
_SEP = r"[\s.\-/,]"
|
||||||
|
_LABEL = (r"(?:acct|account|a/c|wire|routing|aba|sort\s?code|ssn|social\s?security|tax\s?id|"
|
||||||
|
r"ein|policy|member|ref)")
|
||||||
|
TIER1_PATTERNS = [
|
||||||
|
("ssn", re.compile(r"\b\d{3}" + _SEP + r"\d{2}" + _SEP + r"\d{4}\b")),
|
||||||
|
("ssn", re.compile(r"(?i)\b(?:ssn|social\s?security|tax\s?id|ein)\b[^\d]{0,12}\(?\d{3}\)?" + _SEP + r"{0,3}\d{2}" + _SEP + r"{0,3}\d{4}\b")),
|
||||||
|
("iban", re.compile(r"\b[A-Z]{2}\d{2}(?:\s?[A-Z0-9]){11,30}\b")), # IBAN >=15 chars; excludes 12-char ISIN
|
||||||
|
("swift", re.compile(r"(?i)\b(?:swift|bic)\b[^A-Za-z0-9]{0,8}[A-Z]{4}[A-Z]{2}[A-Z0-9]{2,5}\b")),
|
||||||
|
("passport", re.compile(r"(?i)\bpassport\b(?:\s?(?:no|number|num|#)\.?)?[^\dA-Za-z]{0,6}[A-Za-z]{0,2}[\s\-]?\d{6,9}\b")),
|
||||||
|
("labeled_account", re.compile(r"(?i)\b" + _LABEL + r"\b[^\dA-Za-z]{0,14}[#:]?\s*[\dXx](?:[\dXx]" + _SEP + r"?){5,}\b")),
|
||||||
|
# labelled identifier with a LETTER prefix or an intervening 'no/number/id/ref/to' word
|
||||||
|
# (e.g. 'acct A123456789012', 'member ID: X4451200931', 'Wire to GB123456789012') — these
|
||||||
|
# slip the digit-led rule above, the bare-digit catch, and the IBAN floor.
|
||||||
|
("labeled_account", re.compile(r"(?i)\b" + _LABEL + r"\b(?:[\s.:#\-]{0,3}(?:no|number|num|id|ref|to)\b)?[\s.:#\-]{0,4}[A-Za-z]{0,4}\d[\dA-Za-z]{4,}\b")),
|
||||||
|
]
|
||||||
|
|
||||||
|
# ── structured PII (Tier-2) ────────────────────────────────────────────────────
|
||||||
|
_EMAIL_RE = re.compile(r"\b[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}\b")
|
||||||
|
_URL_RE = re.compile(
|
||||||
|
r"\bhttps?://[^\s)\]]+"
|
||||||
|
r"|\bwww\.[^\s)\]]+"
|
||||||
|
r"|\b(?:[a-z0-9\-]+\.)?(?:linkedin|twitter|github|facebook|instagram|x|substack|medium)\.com/[^\s)\]]+",
|
||||||
|
re.IGNORECASE)
|
||||||
|
# Phones: NANP (3-3-4, optional +1, optional extension) OR E.164/international (leading +).
|
||||||
|
# Tightened so plain 4-4 year ranges ('2019-2024') don't match.
|
||||||
|
_PHONE_RE = re.compile(
|
||||||
|
r"(?<![\w.])(?:"
|
||||||
|
r"(?:\+?1[\s.\-]?)?(?:\(\d{3}\)[\s.\-]?|\d{3}[\s.\-])\d{3}[\s.\-]\d{4}"
|
||||||
|
r"|\+\d{1,3}(?:[\s.\-]?\d){7,14}"
|
||||||
|
r")(?:\s?(?:x|ext\.?|extension)\s?\d{1,6})?(?![\w])")
|
||||||
|
# Amounts: ONLY currency-anchored (symbol / code / currency-word), so non-money quantities
|
||||||
|
# ('3m tall', 'ten million tokens', '250k followers') are NOT eaten. Bare magnitudes without
|
||||||
|
# a currency cue are left to minimize-first + NER, which strip real money amounts.
|
||||||
|
_NUMWORD = (r"(?:one|two|three|four|five|six|seven|eight|nine|ten|eleven|twelve|thirteen|"
|
||||||
|
r"fourteen|fifteen|sixteen|seventeen|eighteen|nineteen|twenty|thirty|forty|fifty|"
|
||||||
|
r"sixty|seventy|eighty|ninety|hundred|couple|few|several|half|a)")
|
||||||
|
_MAG = r"(?:mm|bn|tn|thousand|million|billion|trillion|k|m|b)" # longest-first so 'MM' isn't split into 'M'
|
||||||
|
_AMOUNT_RES = [
|
||||||
|
re.compile(r"[$€£]\s?\d[\d,. ]*\d?\s?-\s?[$€£]?\s?\d[\d,. ]*\d?(?:\s?" + _MAG + r")?", re.IGNORECASE), # $3-5M range
|
||||||
|
re.compile(r"[$€£]\s?\d[\d,]*(?:\.\d+)?(?:\s?" + _MAG + r")?", re.IGNORECASE), # $5,000,000 / $5m
|
||||||
|
re.compile(r"\b(?:USD|EUR|GBP|CHF|CAD|AUD)\s?[$€£]?\s?\d[\d,]*(?:\.\d+)?(?:\s?" + _MAG + r")?", re.IGNORECASE),
|
||||||
|
re.compile(r"\b\d[\d,]*(?:\.\d+)?\s?(?:dollars?|euros?|pounds?)\b", re.IGNORECASE), # 5,000,000 dollars
|
||||||
|
re.compile(r"(?i)\b(?:" + _NUMWORD + r"[\s\-]+){1,4}" + _MAG + r"\s+(?:dollars?|euros?|pounds?)\b"), # five million dollars
|
||||||
|
]
|
||||||
|
_MONTHS = (r"(?:jan|feb|mar|apr|may|jun|jul|aug|sep|sept|oct|nov|dec)[a-z]*\.?")
|
||||||
|
_DATE_RES = [
|
||||||
|
re.compile(r"\b(?:19|20)\d{2}-\d{2}-\d{2}\b"), # ISO
|
||||||
|
re.compile(r"(?i)\b" + _MONTHS + r"\s+\d{1,2}(?:st|nd|rd|th)?,?\s+(?:19|20)?\d{2}\b"), # March 12, 1986
|
||||||
|
re.compile(r"(?i)\b\d{1,2}(?:st|nd|rd|th)?\s+" + _MONTHS + r",?\s+(?:19|20)?\d{2}\b"), # 12 March 1986
|
||||||
|
re.compile(r"\b(?:0?[1-9]|1[0-2])[/.\-](?:0?[1-9]|[12]\d|3[01])[/.\-](?:19|20)?\d{2}\b"), # 3/12/86 (valid m/d only)
|
||||||
|
re.compile(r"(?i)\bQ[1-4][\s\-]?(?:19|20)\d{2}\b"), # Q1 1986
|
||||||
|
re.compile(r"(?i)\b" + _MONTHS + r"\s+(?:19|20)\d{2}\b"), # March 1986
|
||||||
|
]
|
||||||
|
# Addresses: US number-first, PO Box, and European -strasse/-gasse + 'Rue/Calle/Via X N'.
|
||||||
|
# Comprehensive international address detection relies on the NER LOC backstop + minimize-first.
|
||||||
|
_ADDR_RE = re.compile(
|
||||||
|
r"\bP\.?\s?O\.?\s?Box\s+\d+"
|
||||||
|
r"|\b\d{1,6}\s+(?:[A-Z][A-Za-z'.]+\s?){1,4}"
|
||||||
|
r"(?:Street|St|Avenue|Ave|Road|Rd|Lane|Ln|Boulevard|Blvd|Drive|Dr|Court|Ct|Way|Place|Pl|Square|Sq|Terrace|Ter)\b\.?"
|
||||||
|
r"(?:,?\s+[A-Z][A-Za-z]+)*"
|
||||||
|
r"|\b[A-Z][A-Za-z]*(?:strasse|straße|gasse|weg)\s+\d{1,5}"
|
||||||
|
r"|\b(?:Rue|Calle|Via|Avenida)\s+(?:[A-Z][A-Za-z'.]+\s?){1,3}\d{1,5}",
|
||||||
|
re.IGNORECASE)
|
||||||
|
_ZIP_RE = re.compile(r"\b[A-Z]{2}\s+\d{5}(?:-\d{4})?\b")
|
||||||
|
# bare long unlabeled run -> reversible [MISC]. Not glued to letters (so an ISIN/ticker like
|
||||||
|
# US0378331005 stays intact substance), and a trailing sentence period doesn't block it.
|
||||||
|
_BARE_DIGITS_RE = re.compile(r"(?<![\dA-Za-z.\-])\d{9,}(?![A-Za-z]|\.?\d)")
|
||||||
|
|
||||||
|
_WORDX = r"[^\W_]" # unicode word char without underscore
|
||||||
|
|
||||||
|
|
||||||
|
def _fold(s):
|
||||||
|
"""1:1 length-preserving fold: strip diacritics per char + casefold, so 'Jonathán'
|
||||||
|
matches a stored ASCII 'Jonathan'. Length preserved so match spans map to the original."""
|
||||||
|
out = []
|
||||||
|
for ch in s:
|
||||||
|
d = unicodedata.normalize("NFKD", ch)
|
||||||
|
base = "".join(c for c in d if not unicodedata.combining(c))
|
||||||
|
out.append((base[0] if base else ch).lower())
|
||||||
|
return "".join(out)
|
||||||
|
|
||||||
|
|
||||||
|
def _bucket_amount(s):
|
||||||
|
num = re.sub(r"[^\d.]", "", s)
|
||||||
|
try:
|
||||||
|
v = float(num)
|
||||||
|
except ValueError:
|
||||||
|
return "~$?"
|
||||||
|
low = s.lower()
|
||||||
|
if "billion" in low or re.search(r"\d\s?bn?\b", low):
|
||||||
|
v *= 1_000_000_000
|
||||||
|
elif "million" in low or re.search(r"\d\s?mm?\b", low):
|
||||||
|
v *= 1_000_000
|
||||||
|
elif "thousand" in low or re.search(r"\d\s?k\b", low):
|
||||||
|
v *= 1_000
|
||||||
|
if v >= 1_000_000_000:
|
||||||
|
return f"~${round(v/1_000_000_000)}B"
|
||||||
|
if v >= 1_000_000:
|
||||||
|
return f"~${round(v/1_000_000)}M"
|
||||||
|
if v >= 1_000:
|
||||||
|
return f"~${round(v/1_000)}k"
|
||||||
|
return "~$<1k"
|
||||||
|
|
||||||
|
|
||||||
|
def _bucket_date(s):
|
||||||
|
iso = re.match(r"((?:19|20)\d{2})-(\d{2})-\d{2}", s)
|
||||||
|
if iso:
|
||||||
|
return f"Q{(int(iso.group(2))-1)//3 + 1} {iso.group(1)}"
|
||||||
|
q = re.search(r"(?i)Q([1-4])[\s\-]?((?:19|20)\d{2})", s)
|
||||||
|
if q:
|
||||||
|
return f"Q{q.group(1)} {q.group(2)}"
|
||||||
|
y = re.search(r"\b((?:19|20)\d{2})\b", s)
|
||||||
|
if y:
|
||||||
|
return y.group(1)
|
||||||
|
yy = re.search(r"[/.\-](\d{2})\b", s) # 2-digit year fallback
|
||||||
|
if yy:
|
||||||
|
return "19" + yy.group(1) if int(yy.group(1)) > 30 else "20" + yy.group(1)
|
||||||
|
return "(period)"
|
||||||
|
|
||||||
|
|
||||||
|
class ScrubState:
|
||||||
|
"""Local pseudonym map for ONE task: same surface string -> same token (injective).
|
||||||
|
The map is the de-anon key — local-only, never sent/serialized to a third party."""
|
||||||
|
def __init__(self):
|
||||||
|
self.token_map = {}
|
||||||
|
self._by_value = {}
|
||||||
|
self._counters = {t: 0 for t in TOKEN_TYPES}
|
||||||
|
self.tier1_dropped = []
|
||||||
|
|
||||||
|
def token_for(self, ttype, surface):
|
||||||
|
key = (ttype, surface)
|
||||||
|
tok = self._by_value.get(key)
|
||||||
|
if tok is None:
|
||||||
|
self._counters[ttype] += 1
|
||||||
|
tok = f"[{ttype}_{self._counters[ttype]}]"
|
||||||
|
self._by_value[key] = tok
|
||||||
|
self.token_map[tok] = surface
|
||||||
|
return tok
|
||||||
|
|
||||||
|
|
||||||
|
def _flatten_known(known_entities):
|
||||||
|
if not known_entities:
|
||||||
|
return []
|
||||||
|
type_by_key = {"persons": "PERSON", "orgs": "ORG", "funds": "FUND", "emails": "EMAIL", "locations": "LOC"}
|
||||||
|
out = []
|
||||||
|
for key, ttype in type_by_key.items():
|
||||||
|
for s in known_entities.get(key, []) or []:
|
||||||
|
s = (s or "").strip()
|
||||||
|
if s:
|
||||||
|
out.append((s, ttype))
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
def _match_known(text, known_list, state):
|
||||||
|
"""Tokenize known entities, matched UNICODE-FOLDED + case-insensitive, longest-first,
|
||||||
|
extending over hyphen/apostrophe compounds so a known half of a double-barrelled
|
||||||
|
surname pulls in the whole token. Operates by span so we can fold for matching but
|
||||||
|
replace the ORIGINAL surface (preserved for rehydrate)."""
|
||||||
|
if not known_list:
|
||||||
|
return text
|
||||||
|
folded = _fold(text)
|
||||||
|
pairs = sorted(((_fold(unicodedata.normalize("NFKC", s)), t) for s, t in known_list),
|
||||||
|
key=lambda x: len(x[0]), reverse=True)
|
||||||
|
type_by_folded = {}
|
||||||
|
for fs, t in pairs:
|
||||||
|
type_by_folded.setdefault(fs, t)
|
||||||
|
alt = "|".join(re.escape(fs) for fs, _ in pairs if fs)
|
||||||
|
if not alt:
|
||||||
|
return text
|
||||||
|
rx = re.compile(r"(?<![0-9A-Za-z])(?:" + alt + r")(?![0-9A-Za-z])")
|
||||||
|
spans = []
|
||||||
|
for m in rx.finditer(folded):
|
||||||
|
st, en = m.start(), m.end()
|
||||||
|
ttype = type_by_folded.get(folded[st:en], "MISC")
|
||||||
|
# extend over hyphen/apostrophe compounds on both sides
|
||||||
|
while st > 1 and folded[st - 1] in "-'’" and re.match(_WORDX, folded[st - 2] or ""):
|
||||||
|
k = st - 2
|
||||||
|
while k >= 0 and (re.match(_WORDX, folded[k]) or folded[k] in "-'’"):
|
||||||
|
k -= 1
|
||||||
|
st = k + 1
|
||||||
|
while en < len(folded) - 1 and folded[en] in "-'’" and re.match(_WORDX, folded[en + 1] or ""):
|
||||||
|
k = en + 1
|
||||||
|
while k < len(folded) and (re.match(_WORDX, folded[k]) or folded[k] in "-'’"):
|
||||||
|
k += 1
|
||||||
|
en = k
|
||||||
|
spans.append((st, en, ttype))
|
||||||
|
if not spans:
|
||||||
|
return text
|
||||||
|
# merge overlaps, replace right-to-left in the ORIGINAL
|
||||||
|
spans.sort()
|
||||||
|
merged = [spans[0]]
|
||||||
|
for st, en, tt in spans[1:]:
|
||||||
|
ps, pe, ptt = merged[-1]
|
||||||
|
if st <= pe:
|
||||||
|
merged[-1] = (ps, max(pe, en), ptt)
|
||||||
|
else:
|
||||||
|
merged.append((st, en, tt))
|
||||||
|
for st, en, tt in reversed(merged):
|
||||||
|
surface = text[st:en]
|
||||||
|
text = text[:st] + state.token_for(tt, surface) + text[en:]
|
||||||
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
def scrub(text, known_entities=None, bucket=False, state=None, ner_fn=None):
|
||||||
|
"""De-identify `text`. Returns (outbound_text, token_map, audit). Pass ner_fn (a
|
||||||
|
local-model NER callable text->[(surface,type)]) in production to catch unknown
|
||||||
|
names; without it the dictionary+regex path leaves unknown free-text names as
|
||||||
|
residual (callers should minimize-first and/or fail closed)."""
|
||||||
|
if text is None:
|
||||||
|
text = ""
|
||||||
|
st = state or ScrubState()
|
||||||
|
# NFKC-normalize so decomposed (NFD) names and ligatures align with the dictionary
|
||||||
|
# (else 'Reyés' in NFD or 'Steffen' with a ligature would miss and leak), and strip
|
||||||
|
# zero-width characters that could split a known name ('Rey<U+200B>es').
|
||||||
|
s = unicodedata.normalize("NFKC", str(text))
|
||||||
|
s = re.sub(r"[\u200b\u200c\u200d\u2060\ufeff]", "", s)
|
||||||
|
|
||||||
|
# 1) PRE-NEUTRALIZE pre-existing [TYPE_N] strings so they can't collide with our tokens.
|
||||||
|
s = _TOKEN_RE.sub(lambda m: st.token_for("MISC", m.group(0)), s)
|
||||||
|
|
||||||
|
# 2) TIER-1 DROP (labelled/structured; separator tolerant). Neutral marker, no value.
|
||||||
|
for label, pat in TIER1_PATTERNS:
|
||||||
|
def _drop(_m, _label=label):
|
||||||
|
st.tier1_dropped.append(_label)
|
||||||
|
return "[redacted]"
|
||||||
|
s = pat.sub(_drop, s)
|
||||||
|
|
||||||
|
# 3) KNOWN ENTITIES (unicode-folded, hyphen-extended).
|
||||||
|
s = _match_known(s, _flatten_known(known_entities), st)
|
||||||
|
|
||||||
|
# 4) STRUCTURED PII. Order matters: emails/urls/addresses, then DATES and AMOUNTS
|
||||||
|
# (so dashed ISO dates / ranges aren't swallowed by the permissive phone matcher),
|
||||||
|
# then PHONES, then any bare long digit run left over.
|
||||||
|
s = _EMAIL_RE.sub(lambda m: st.token_for("EMAIL", m.group(0)), s)
|
||||||
|
s = _URL_RE.sub(lambda m: st.token_for("URL", m.group(0)), s)
|
||||||
|
s = _ZIP_RE.sub(lambda m: st.token_for("LOC", m.group(0)), s) # state+ZIP before ADDR (which would eat the state)
|
||||||
|
s = _ADDR_RE.sub(lambda m: st.token_for("ADDR", m.group(0)), s)
|
||||||
|
for date_re in _DATE_RES:
|
||||||
|
if bucket:
|
||||||
|
s = date_re.sub(lambda m: _bucket_date(m.group(0)), s)
|
||||||
|
else:
|
||||||
|
s = date_re.sub(lambda m: st.token_for("DATE", m.group(0)), s)
|
||||||
|
for amt_re in _AMOUNT_RES:
|
||||||
|
if bucket:
|
||||||
|
s = amt_re.sub(lambda m: _bucket_amount(m.group(0)), s)
|
||||||
|
else:
|
||||||
|
s = amt_re.sub(lambda m: st.token_for("AMOUNT", m.group(0)), s)
|
||||||
|
s = _PHONE_RE.sub(lambda m: st.token_for("PHONE", m.group(0)), s)
|
||||||
|
# bare long unlabeled digit runs -> reversible [MISC] (never leak digits to Claude;
|
||||||
|
# don't DROP, since these may be substance like share counts / security ids).
|
||||||
|
s = _BARE_DIGITS_RE.sub(lambda m: st.token_for("MISC", m.group(0)), s)
|
||||||
|
|
||||||
|
# 5) NER BACKSTOP for unknown names (production: local Qwen). Tokenize what it finds.
|
||||||
|
# A connection failure here propagates so the caller can FAIL CLOSED rather than
|
||||||
|
# emit name-blind. Sort longest-first so a full name is tokenized before its parts.
|
||||||
|
if ner_fn is not None:
|
||||||
|
for surface, ntype in sorted((ner_fn(s) or []), key=lambda e: len(e[0] or ""), reverse=True):
|
||||||
|
surface = (surface or "").strip()
|
||||||
|
if not surface or _TOKEN_RE.search(surface):
|
||||||
|
continue
|
||||||
|
tt = ntype if ntype in TOKEN_TYPES else "PERSON"
|
||||||
|
s = re.sub(r"(?<![0-9A-Za-z])" + re.escape(surface) + r"(?![0-9A-Za-z])",
|
||||||
|
lambda m: st.token_for(tt, m.group(0)), s)
|
||||||
|
|
||||||
|
audit = {
|
||||||
|
"token_count": len(st.token_map),
|
||||||
|
"tokens_by_type": _counts_by_type(st.token_map),
|
||||||
|
"tier1_dropped_count": len(st.tier1_dropped),
|
||||||
|
"tier1_dropped_kinds": sorted(set(st.tier1_dropped)),
|
||||||
|
"bucketed": bool(bucket),
|
||||||
|
"outbound_chars": len(s),
|
||||||
|
}
|
||||||
|
return s, dict(st.token_map), audit
|
||||||
|
|
||||||
|
|
||||||
|
def _counts_by_type(token_map):
|
||||||
|
out = {}
|
||||||
|
for tok in token_map:
|
||||||
|
m = re.match(r"\[([A-Z]+)_\d+\]", tok)
|
||||||
|
if m:
|
||||||
|
out[m.group(1)] = out.get(m.group(1), 0) + 1
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
def rehydrate(text, token_map):
|
||||||
|
"""Substitute real values back in via a SINGLE non-overlapping pass (one alternation,
|
||||||
|
longest tokens first) so an inserted value that is itself token-shaped can't be
|
||||||
|
re-substituted by a later pass. Tier-1 drops are not restorable — excluded by design."""
|
||||||
|
s = str(text or "")
|
||||||
|
if not token_map:
|
||||||
|
return s
|
||||||
|
rx = re.compile("|".join(re.escape(t) for t in sorted(token_map, key=len, reverse=True)))
|
||||||
|
return rx.sub(lambda m: token_map[m.group(0)], s)
|
||||||
|
|
||||||
|
|
||||||
|
def residual_tokens(text):
|
||||||
|
return _TOKEN_RE.findall(str(text or ""))
|
||||||
|
|
||||||
|
|
||||||
|
# ── known-entity dictionary from the CRM (read-only) ───────────────────────────
|
||||||
|
|
||||||
|
def build_known_entities(db_path):
|
||||||
|
"""Deterministic dictionary of OUR entities to tokenize, read-only from the CRM.
|
||||||
|
Includes full names AND every name part (so mid-prose surnames are caught) + email
|
||||||
|
local-parts. RAISES on read failure — callers must fail closed, never run name-blind."""
|
||||||
|
persons, orgs, funds, emails = set(), set(), set(), set()
|
||||||
|
conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
|
||||||
|
conn.row_factory = sqlite3.Row
|
||||||
|
|
||||||
|
def _add_person(name):
|
||||||
|
name = (name or "").strip()
|
||||||
|
if len(name) >= 2:
|
||||||
|
persons.add(name)
|
||||||
|
for part in re.split(r"[\s'’\-]+", name):
|
||||||
|
if len(part) >= 2 and not part.isdigit(): # index every part incl. short surnames (Wu, Li)
|
||||||
|
persons.add(part)
|
||||||
|
|
||||||
|
def _safe(q, fn):
|
||||||
|
try:
|
||||||
|
for r in conn.execute(q):
|
||||||
|
fn(r)
|
||||||
|
except sqlite3.OperationalError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# No `deleted_at` filter: tokenizing a soft-deleted name is desirable, and the live
|
||||||
|
# contacts/canonical schemas vary on that column — filtering on it silently zeroed the
|
||||||
|
# whole dictionary (a missing-column OperationalError swallowed by _safe).
|
||||||
|
_safe("SELECT display_name, primary_email FROM canonical_entities WHERE entity_kind='person'",
|
||||||
|
lambda r: (_add_person(r["display_name"]), r["primary_email"] and emails.add(r["primary_email"].strip().lower())))
|
||||||
|
_safe("SELECT first_name, last_name, email FROM contacts",
|
||||||
|
lambda r: (_add_person(f"{r['first_name'] or ''} {r['last_name'] or ''}"),
|
||||||
|
r["email"] and emails.add(r["email"].strip().lower())))
|
||||||
|
_safe("SELECT full_name, email FROM fundraising_contacts",
|
||||||
|
lambda r: (_add_person(r["full_name"]), r["email"] and emails.add(r["email"].strip().lower())))
|
||||||
|
_safe("SELECT display_name FROM canonical_entities WHERE entity_kind IN ('organization','investor','lp')",
|
||||||
|
lambda r: r["display_name"] and orgs.add(r["display_name"].strip()))
|
||||||
|
_safe("SELECT name FROM organizations", lambda r: r["name"] and orgs.add(r["name"].strip()))
|
||||||
|
_safe("SELECT investor_name FROM fundraising_investors", lambda r: r["investor_name"] and orgs.add(r["investor_name"].strip()))
|
||||||
|
_safe("SELECT fund_name FROM fundraising_funds", lambda r: r["fund_name"] and funds.add(r["fund_name"].strip()))
|
||||||
|
conn.close()
|
||||||
|
|
||||||
|
for e in list(emails):
|
||||||
|
lp = e.split("@")[0]
|
||||||
|
if len(lp) >= 3 and not lp.isdigit():
|
||||||
|
persons.add(lp)
|
||||||
|
return {"persons": sorted(persons, key=len, reverse=True),
|
||||||
|
"orgs": sorted(orgs, key=len, reverse=True),
|
||||||
|
"funds": sorted(funds, key=len, reverse=True),
|
||||||
|
"emails": sorted(emails, key=len, reverse=True)}
|
||||||
|
|
||||||
|
|
||||||
|
# ── audit logging (metadata only — never the map or real values) ───────────────
|
||||||
|
|
||||||
|
def _now():
|
||||||
|
return datetime.now(timezone.utc).replace(tzinfo=None).isoformat() + "Z"
|
||||||
|
|
||||||
|
|
||||||
|
def log_scrub(conn, actor_id, audit, task=None, session_id=None, target_id=None, source="mcp"):
|
||||||
|
payload = {"task": task, "session_id": session_id,
|
||||||
|
"token_count": audit.get("token_count"), "tokens_by_type": audit.get("tokens_by_type"),
|
||||||
|
"tier1_dropped_count": audit.get("tier1_dropped_count"),
|
||||||
|
"tier1_dropped_kinds": audit.get("tier1_dropped_kinds"),
|
||||||
|
"bucketed": audit.get("bucketed"), "outbound_chars": audit.get("outbound_chars")}
|
||||||
|
conn.execute(
|
||||||
|
"""INSERT INTO interaction_log (id, ts, actor_type, actor_id, action, target_type, target_id, payload, source, created_at)
|
||||||
|
VALUES (?,?, 'agent', ?, 'redaction.scrub', 'canonical_entity', ?, ?, ?, ?)""",
|
||||||
|
(str(uuid.uuid4()), _now(), actor_id, target_id, json.dumps(payload), source, _now()))
|
||||||
|
|
||||||
|
|
||||||
|
def log_rehydrate(conn, actor_id, tokens_rehydrated, residual, human_decision="pending",
|
||||||
|
reviewer_id=None, task=None, session_id=None, source="mcp"):
|
||||||
|
payload = {"task": task, "session_id": session_id, "tokens_rehydrated": tokens_rehydrated,
|
||||||
|
"residual_placeholders": residual, "human_decision": human_decision, "reviewer_id": reviewer_id}
|
||||||
|
conn.execute(
|
||||||
|
"""INSERT INTO interaction_log (id, ts, actor_type, actor_id, action, target_type, target_id, payload, source, created_at)
|
||||||
|
VALUES (?,?, 'agent', ?, 'redaction.rehydrate', 'canonical_entity', NULL, ?, ?, ?)""",
|
||||||
|
(str(uuid.uuid4()), _now(), actor_id, json.dumps(payload), source, _now()))
|
||||||
@@ -0,0 +1,133 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Re-identification spot-check (redaction-rehydration.md §6) — OFFLINE + SYNTHETIC.
|
||||||
|
|
||||||
|
A deterministic approximation of "feed only the scrubbed prompt to a model and try to
|
||||||
|
recover who it is." Three probes:
|
||||||
|
A. Exact/normalized leak gate (MUST PASS): re-scan the scrubbed payload for ANY known
|
||||||
|
real value or Tier-1 string under normalization (case, punctuation, reversed
|
||||||
|
'Last First', email local-part). Any hit = tokenizer miss = FAIL.
|
||||||
|
B. Descriptive-identifier residual (BOUNDED): phrases that re-identify even with names
|
||||||
|
tokenized ("the family that sold the mining company in Texas"). The deterministic
|
||||||
|
scrubber is not expected to catch these (the on-infra local-Qwen pass + the
|
||||||
|
minimize-first summary do); this probe MEASURES the residual and fails only if it
|
||||||
|
EXCEEDS a committed ceiling, so leakage can't silently grow.
|
||||||
|
C. Inference via bucketing: with bucket=True, no exact amount/identity-date survives,
|
||||||
|
and a (amount-band, year, sector) tuple is not unique to one synthetic entity.
|
||||||
|
|
||||||
|
Run: cd backend && python3 redaction/test_reidentification.py
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
import sys
|
||||||
|
|
||||||
|
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||||
|
import scrub as R # noqa: E402
|
||||||
|
|
||||||
|
FAILS = []
|
||||||
|
|
||||||
|
|
||||||
|
def check(cond, msg):
|
||||||
|
print((" PASS " if cond else " FAIL ") + msg)
|
||||||
|
if not cond:
|
||||||
|
FAILS.append(msg)
|
||||||
|
|
||||||
|
|
||||||
|
def _norm(s):
|
||||||
|
return re.sub(r"[^a-z0-9]+", " ", s.lower()).strip()
|
||||||
|
|
||||||
|
|
||||||
|
# Probe A — known synthetic entities that must NOT be recoverable from the scrubbed text
|
||||||
|
KB = {
|
||||||
|
"persons": ["Jonathan Reyes", "Marta Quine"],
|
||||||
|
"orgs": ["Cedar Point Capital"],
|
||||||
|
"emails": ["jon@cedarpoint.example"],
|
||||||
|
"tier1": ["000123456789", "123-45-6789"],
|
||||||
|
}
|
||||||
|
RAW_A = ("Jonathan Reyes at Cedar Point Capital (jon@cedarpoint.example) is cooling; Reyes wants better "
|
||||||
|
"terms. Marta Quine disagrees. acct 000123456789. Substance: fee and lock-up objections.")
|
||||||
|
KNOWN_A = {"persons": ["Jonathan Reyes", "Reyes", "Marta Quine", "Marta"],
|
||||||
|
"orgs": ["Cedar Point Capital"], "funds": [], "emails": ["jon@cedarpoint.example"]}
|
||||||
|
|
||||||
|
|
||||||
|
def probe_a():
|
||||||
|
print("\n[probe A — exact/normalized leak gate]")
|
||||||
|
outbound, _, _ = R.scrub(RAW_A, known_entities=KNOWN_A, bucket=False)
|
||||||
|
nout = _norm(outbound)
|
||||||
|
hits = []
|
||||||
|
for cat in ("persons", "orgs", "emails", "tier1"):
|
||||||
|
for v in KB[cat]:
|
||||||
|
variants = {_norm(v)}
|
||||||
|
if " " in v and cat == "persons":
|
||||||
|
a, b = v.split()[0], v.split()[-1]
|
||||||
|
variants.add(_norm(f"{b} {a}")) # reversed Last First
|
||||||
|
variants.add(_norm(b)) # bare surname
|
||||||
|
if "@" in v:
|
||||||
|
variants.add(_norm(v.split("@")[0])) # email local-part
|
||||||
|
for var in variants:
|
||||||
|
if var and var in nout:
|
||||||
|
hits.append((v, var))
|
||||||
|
check(not hits, f"no known identifier recoverable from scrubbed text (hits={hits})")
|
||||||
|
|
||||||
|
|
||||||
|
# Probe B — descriptive re-identifiers (bounded residual)
|
||||||
|
DESCRIPTIVE = [
|
||||||
|
"the family that sold the mining company in Texas",
|
||||||
|
"the former CTO of a well-known payments unicorn",
|
||||||
|
"the senator's brother who runs a family office",
|
||||||
|
]
|
||||||
|
RAW_B = "Notes: our contact is " + DESCRIPTIVE[0] + ". Another is " + DESCRIPTIVE[1] + ". A third is " + DESCRIPTIVE[2] + "."
|
||||||
|
RESIDUAL_CEILING = 3 # known residual the deterministic scrubber alone does not catch;
|
||||||
|
# the on-infra Qwen pass + minimize-first summary drive this toward 0.
|
||||||
|
|
||||||
|
|
||||||
|
def probe_b():
|
||||||
|
print("\n[probe B — descriptive-identifier residual, bounded]")
|
||||||
|
outbound, _, _ = R.scrub(RAW_B, known_entities={"persons": [], "orgs": [], "funds": [], "emails": []})
|
||||||
|
surviving = [d for d in DESCRIPTIVE if d in outbound]
|
||||||
|
for d in surviving:
|
||||||
|
print(f" flagged residual (handled on-infra by Qwen/minimize-first): {d!r}")
|
||||||
|
check(len(surviving) <= RESIDUAL_CEILING,
|
||||||
|
f"descriptive residual within committed ceiling ({len(surviving)} <= {RESIDUAL_CEILING})")
|
||||||
|
|
||||||
|
|
||||||
|
# Probe C — bucketing destroys exact values + singling-out
|
||||||
|
ENTITIES_C = [
|
||||||
|
{"name": "A", "amount": "$5,200,000", "date": "1986-02-10", "sector": "energy"},
|
||||||
|
{"name": "B", "amount": "$4,800,000", "date": "1986-03-20", "sector": "energy"}, # same band/year/sector as A
|
||||||
|
{"name": "C", "amount": "$25,000,000", "date": "1991-09-01", "sector": "bitcoin"},
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def probe_c():
|
||||||
|
print("\n[probe C — inference via bucketing]")
|
||||||
|
raw = " ".join(f"Investor commits {e['amount']} on {e['date']} in {e['sector']}." for e in ENTITIES_C)
|
||||||
|
outbound, _, _ = R.scrub(raw, known_entities={"persons": [], "orgs": [], "funds": [], "emails": []}, bucket=True)
|
||||||
|
check(re.search(r"\$\s?\d[\d,]{2,}", outbound) is None, "no exact $ amount survives bucketing")
|
||||||
|
check(re.search(r"\b(?:19|20)\d{2}-\d{2}-\d{2}\b", outbound) is None, "no exact date survives bucketing")
|
||||||
|
# singling-out: the (amount-band, year, sector) tuple must not be unique to one entity
|
||||||
|
tuples = {}
|
||||||
|
for e in ENTITIES_C:
|
||||||
|
band = R._bucket_amount(e["amount"])
|
||||||
|
year = e["date"][:4]
|
||||||
|
tuples.setdefault((band, year, e["sector"]), []).append(e["name"])
|
||||||
|
unique = [k for k, v in tuples.items() if len(v) == 1]
|
||||||
|
# A and B collapse to the same bucket-tuple; C is alone but that's an accepted single in this fixture
|
||||||
|
check(any(len(v) > 1 for v in tuples.values()),
|
||||||
|
f"bucketing collapses distinct entities into shared bands (tuples={ {k: v for k,v in tuples.items()} })")
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
probe_a()
|
||||||
|
probe_b()
|
||||||
|
probe_c()
|
||||||
|
print()
|
||||||
|
if FAILS:
|
||||||
|
print(f"FAILED ({len(FAILS)}):")
|
||||||
|
for f in FAILS:
|
||||||
|
print(f" - {f}")
|
||||||
|
sys.exit(1)
|
||||||
|
print("ALL PASS (re-identification spot-check)")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,171 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Golden-file LEAK TEST for the redaction boundary, hardened across two adversarial
|
||||||
|
leak-hunts. Synthetic fixtures only (guardrail #9).
|
||||||
|
|
||||||
|
Per case: must_vanish (never reach Claude), tier1_excluded (also not in the map),
|
||||||
|
substance (survives verbatim), perfect inverse, leak-proof audit. Plus a round-2
|
||||||
|
"hardening vectors" section that regression-locks: NFD/ligature unicode names,
|
||||||
|
slash/comma SSN + SWIFT + passport Tier-1 drops, sentence-final bare digits, the
|
||||||
|
rehydrate collision fix, and the FALSE-POSITIVE survival of non-money quantities /
|
||||||
|
version numbers / ISINs (we de-identify, we don't destroy substance).
|
||||||
|
|
||||||
|
Deterministic + offline (the dictionary is each case's own lists; the unknown-name
|
||||||
|
NER backstop is exercised in test_grounding_boundary.py). Currency-CUED amounts are
|
||||||
|
caught here; bare magnitudes ('5MM') are left to minimize-first + NER by design.
|
||||||
|
Run: cd backend && python3 redaction/test_scrub_leak.py
|
||||||
|
"""
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
import sqlite3
|
||||||
|
import sys
|
||||||
|
import unicodedata
|
||||||
|
|
||||||
|
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||||
|
import scrub as R # noqa: E402
|
||||||
|
|
||||||
|
CASES = [
|
||||||
|
{
|
||||||
|
"name": "labeled-tier1 + core tier2",
|
||||||
|
"raw": ("Jonathan Reyes (jon@cedarpoint.example) at Cedar Point Capital is cooling on Fund III. "
|
||||||
|
"Reyes would commit $5,000,000. Wire to acct 000123456789 spooked compliance. Met 1986-03-12. "
|
||||||
|
"Substance: the objection is fee load and lock-up; sentiment negative on the energy thesis."),
|
||||||
|
"known": {"persons": ["Jonathan Reyes", "Reyes"], "orgs": ["Cedar Point Capital"],
|
||||||
|
"funds": ["Fund III"], "emails": ["jon@cedarpoint.example"]},
|
||||||
|
"must_vanish": ["Jonathan Reyes", "Reyes", "jon@cedarpoint.example", "Cedar Point Capital",
|
||||||
|
"Fund III", "$5,000,000", "1986-03-12", "000123456789"],
|
||||||
|
"tier1_excluded": ["000123456789"],
|
||||||
|
"substance": ["the objection is fee load and lock-up", "sentiment negative on the energy thesis"],
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "worded/coded amounts, intl phone, urls, non-iso dates",
|
||||||
|
"raw": ("He would commit five million dollars; a $5MM ticket, USD 5,000,000, and a $3-5M range. "
|
||||||
|
"Reach +44 20 7946 0958 or www.cedarpoint.example; profile linkedin.com/in/jreyes. "
|
||||||
|
"Met March 12, 1986 and again 3/12/86. Concern: liquidity timeline only."),
|
||||||
|
"known": {"persons": [], "orgs": [], "funds": [], "emails": []},
|
||||||
|
"must_vanish": ["five million dollars", "$5MM", "USD 5,000,000", "$3-5M", "+44 20 7946 0958",
|
||||||
|
"www.cedarpoint.example", "linkedin.com/in/jreyes", "March 12, 1986", "3/12/86"],
|
||||||
|
"tier1_excluded": [],
|
||||||
|
"substance": ["Concern: liquidity timeline only"],
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "diacritics + hyphenated + short surnames",
|
||||||
|
"raw": ("Spoke to Jonathán Reyés about the thesis. Reyes-Castellanos co-invests. "
|
||||||
|
"Wu is warm; Li wants a side letter on fees."),
|
||||||
|
"known": {"persons": ["Jonathan Reyes", "Reyes", "Li Wu", "Li", "Wu"], "orgs": [], "funds": [], "emails": []},
|
||||||
|
"must_vanish": ["Jonathán", "Reyés", "Castellanos", "Wu", "Li"],
|
||||||
|
"tier1_excluded": [],
|
||||||
|
"substance": ["wants a side letter on fees"],
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "tier1 separators (slash/comma/space) + swift + address + ext",
|
||||||
|
"raw": ("Wire to acct # 1234-5678-9012 spooked compliance. SSN 123/45/6789 and 123 45 6789 on file. "
|
||||||
|
"Via SWIFT CHASUS33XXX. Lives at 42 Maple Avenue, Greenwich, CT 06830. Office 212-555-0188 x4021. "
|
||||||
|
"Substance: wants a co-investment right."),
|
||||||
|
"known": {"persons": [], "orgs": [], "funds": [], "emails": []},
|
||||||
|
"must_vanish": ["1234-5678-9012", "123/45/6789", "123 45 6789", "CHASUS33XXX", "42 Maple Avenue",
|
||||||
|
"212-555-0188", "x4021", "06830"],
|
||||||
|
"tier1_excluded": ["1234-5678-9012", "123/45/6789", "123 45 6789", "CHASUS33XXX"],
|
||||||
|
"substance": ["wants a co-investment right"],
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
FAILS = []
|
||||||
|
|
||||||
|
|
||||||
|
def check(cond, msg):
|
||||||
|
print((" PASS " if cond else " FAIL ") + msg)
|
||||||
|
if not cond:
|
||||||
|
FAILS.append(msg)
|
||||||
|
|
||||||
|
|
||||||
|
def tier1_redacted(raw):
|
||||||
|
s = unicodedata.normalize("NFKC", raw)
|
||||||
|
for _, pat in R.TIER1_PATTERNS:
|
||||||
|
s = pat.sub("[redacted]", s)
|
||||||
|
return s
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
db = os.path.join(__import__("tempfile").mkdtemp(), "log.db")
|
||||||
|
conn = sqlite3.connect(db)
|
||||||
|
conn.execute("""CREATE TABLE interaction_log (id TEXT PRIMARY KEY, ts TEXT, actor_type TEXT, actor_id TEXT,
|
||||||
|
action TEXT, target_type TEXT, target_id TEXT, payload TEXT, source TEXT, created_at TEXT)""")
|
||||||
|
|
||||||
|
for case in CASES:
|
||||||
|
raw, known = case["raw"], case["known"]
|
||||||
|
print(f"\n[{case['name']}]")
|
||||||
|
check(not R.residual_tokens(raw), "raw fixture has no [TYPE_N]-shaped strings")
|
||||||
|
outbound, tmap, audit = R.scrub(raw, known_entities=known, bucket=False)
|
||||||
|
for v in case["must_vanish"]:
|
||||||
|
check(v not in outbound, f"identifier {v!r} absent from outbound")
|
||||||
|
for v in case["tier1_excluded"]:
|
||||||
|
check(all(v not in mv for mv in tmap.values()), f"Tier-1 {v!r} excluded, not tokenized")
|
||||||
|
for s in case["substance"]:
|
||||||
|
check(s in outbound, f"substance survives: {s!r}")
|
||||||
|
check(len(set(tmap.values())) == len(tmap), "map injective")
|
||||||
|
check(R.rehydrate(outbound, tmap) == tier1_redacted(raw), "rehydrate == raw w/ Tier-1 redacted (perfect inverse)")
|
||||||
|
check(not R.residual_tokens(R.rehydrate(outbound, tmap)), "no placeholder survives rehydrate")
|
||||||
|
R.log_scrub(conn, "architect", audit, task="g", session_id="t", source="mcp")
|
||||||
|
conn.commit()
|
||||||
|
blob = " ".join(r[0] for r in conn.execute("SELECT payload FROM interaction_log"))
|
||||||
|
check(all(v not in blob for v in case["must_vanish"]), "audit log carries NO sensitive value")
|
||||||
|
|
||||||
|
# ── round-2 hardening vectors ──
|
||||||
|
def out(raw, known=None):
|
||||||
|
o, _m, _a = R.scrub(raw, known_entities=known or {}, bucket=False)
|
||||||
|
return o
|
||||||
|
|
||||||
|
print("\n[unicode — NFD / ligature names]")
|
||||||
|
nfd = unicodedata.normalize("NFD", "Jonathan Reyés is cooling.")
|
||||||
|
check("Reyés" not in unicodedata.normalize("NFKC", out(nfd, {"persons": ["Jonathan Reyes", "Reyes"]})),
|
||||||
|
"NFD-decomposed accented name does not leak")
|
||||||
|
check("Steffen" not in out("LP Steffen is cooling.", {"persons": ["Steffen"]}),
|
||||||
|
"ligature name (Steffen) does not leak")
|
||||||
|
|
||||||
|
print("\n[tier1 — slash/comma/swift/passport]")
|
||||||
|
o, m, _ = R.scrub("Reyes SSN 123/45/6789 and 123,45,6789 on the W9.", known_entities={}, bucket=False)
|
||||||
|
check("123/45/6789" not in o and "123,45,6789" not in o, "slash/comma SSN dropped")
|
||||||
|
check(all("123/45/6789" not in v and "123,45,6789" not in v for v in m.values()), "SSN not in map (excluded)")
|
||||||
|
check("CHASUS33XXX" not in out("Wire via SWIFT CHASUS33XXX today."), "SWIFT/BIC dropped")
|
||||||
|
check("a1234567" not in out("Passport number a1234567 expires 2030."), "passport-with-'number' dropped")
|
||||||
|
|
||||||
|
print("\n[bare digits at sentence end]")
|
||||||
|
check("123456789012" not in out("The security ID is 123456789012."), "9+ digit run at sentence end tokenized")
|
||||||
|
|
||||||
|
print("\n[FALSE-POSITIVE survival — substance preserved]")
|
||||||
|
check("3m tall" in out("The wall is 3m tall."), "'3m tall' (meters) NOT eaten as money")
|
||||||
|
check("250k followers" in out("She has 250k followers on X."), "'250k followers' NOT eaten as money")
|
||||||
|
check("3.14.159" in out("Pi is roughly 3.14.159 here."), "version-ish number NOT eaten as a date")
|
||||||
|
check("US0378331005" in out("We hold ISIN US0378331005 in the sleeve."), "ISIN preserved (substance, not dropped)")
|
||||||
|
check("2019-2024" in out("Track record spans 2019-2024."), "year range NOT mislabeled as a phone")
|
||||||
|
|
||||||
|
print("\n[integrity — rehydrate single-pass, no cascade]")
|
||||||
|
raw = "Refer to [MISC_2] then [PERSON_9]."
|
||||||
|
oo, mm, _ = R.scrub(raw, known_entities={}, bucket=False)
|
||||||
|
check(R.rehydrate(oo, mm) == raw, "same-length placeholder literals round-trip without cascade")
|
||||||
|
|
||||||
|
print("\n[round-4 — alpha-prefixed accounts, MM, zero-width]")
|
||||||
|
o, m, _ = R.scrub("Acct A123456789012 flagged. Member ID: X4451200931 noted. Wire to GB123456789012 today.",
|
||||||
|
known_entities={}, bucket=False)
|
||||||
|
for v in ["A123456789012", "X4451200931", "GB123456789012"]:
|
||||||
|
check(v not in o, f"alpha-prefixed labelled identifier {v!r} dropped")
|
||||||
|
check(all(v not in mv for mv in m.values()), f"{v!r} excluded, not tokenized")
|
||||||
|
o2 = out("Commit of $5MM and €10MM confirmed.")
|
||||||
|
check("$5MM" not in o2 and "5M " not in o2 and "MM" not in o2, "double-magnitude $5MM fully tokenized (no stray 'M')")
|
||||||
|
zw = "LP Reyes is cooling." # zero-width space splitting the surname
|
||||||
|
check("Reyes" not in out(zw, {"persons": ["Reyes"]}) and "Reyes" not in out(zw, {"persons": ["Reyes"]}),
|
||||||
|
"zero-width-split known name does not leak")
|
||||||
|
|
||||||
|
conn.close()
|
||||||
|
print()
|
||||||
|
if FAILS:
|
||||||
|
print(f"FAILED ({len(FAILS)}):")
|
||||||
|
for f in FAILS:
|
||||||
|
print(f" - {f}")
|
||||||
|
sys.exit(1)
|
||||||
|
print("ALL PASS (redaction leak test — hardened x2)")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -59,9 +59,11 @@ try:
|
|||||||
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "mcp"))
|
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "mcp"))
|
||||||
import architect_tools as _architect_tools # type: ignore
|
import architect_tools as _architect_tools # type: ignore
|
||||||
import architect_agent as _architect_agent # type: ignore
|
import architect_agent as _architect_agent # type: ignore
|
||||||
|
import architect_grounding as _architect_grounding # type: ignore
|
||||||
except Exception:
|
except Exception:
|
||||||
_architect_tools = None
|
_architect_tools = None
|
||||||
_architect_agent = None
|
_architect_agent = None
|
||||||
|
_architect_grounding = None
|
||||||
|
|
||||||
# ─── Configuration ────────────────────────────────────────────────────────────
|
# ─── Configuration ────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
@@ -1894,6 +1896,8 @@ class CRMHandler(BaseHTTPRequestHandler):
|
|||||||
return self.handle_generate_options(user, path.split('/')[-2], body)
|
return self.handle_generate_options(user, path.split('/')[-2], body)
|
||||||
if re.match(r'^/api/thesis/nodes/[^/]+/feedback$', path):
|
if re.match(r'^/api/thesis/nodes/[^/]+/feedback$', path):
|
||||||
return self.handle_node_feedback(user, path.split('/')[-2], body)
|
return self.handle_node_feedback(user, path.split('/')[-2], body)
|
||||||
|
if path == '/api/architect/ground':
|
||||||
|
return self.handle_architect_ground(user, body)
|
||||||
if path == '/api/thesis/lines':
|
if path == '/api/thesis/lines':
|
||||||
return self.handle_create_thesis_line(user, body)
|
return self.handle_create_thesis_line(user, body)
|
||||||
if re.match(r'^/api/thesis/lines/[^/]+/nodes$', path):
|
if re.match(r'^/api/thesis/lines/[^/]+/nodes$', path):
|
||||||
@@ -3701,6 +3705,44 @@ class CRMHandler(BaseHTTPRequestHandler):
|
|||||||
return self.send_error_json(res.get('raw') or res['error'], 502)
|
return self.send_error_json(res.get('raw') or res['error'], 502)
|
||||||
return self.send_json({"data": res})
|
return self.send_json({"data": res})
|
||||||
|
|
||||||
|
def _ground_feedback_corpus(self, conn, limit=60):
|
||||||
|
"""Raw LP-feedback prose for grounding (communications + grid notes). Sensitive
|
||||||
|
Tier-2-heavy text; ONLY ever passed into the redaction boundary, never to Claude
|
||||||
|
directly."""
|
||||||
|
items = []
|
||||||
|
for q in ("SELECT body FROM communications WHERE body IS NOT NULL AND TRIM(body)<>'' ORDER BY communication_date DESC LIMIT ?",
|
||||||
|
"SELECT notes FROM fundraising_investors WHERE notes IS NOT NULL AND TRIM(notes)<>'' LIMIT ?"):
|
||||||
|
try:
|
||||||
|
items += [r[0] for r in conn.execute(q, (limit,))]
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
return items[:limit]
|
||||||
|
|
||||||
|
def handle_architect_ground(self, user, body):
|
||||||
|
"""Ground an objection register in real LP feedback THROUGH the redaction boundary
|
||||||
|
(Workstream D). Retrieval + minimization + scrub stay local; only the de-identified
|
||||||
|
register reaches Claude; the re-hydrated draft is for human review (guardrail #4)."""
|
||||||
|
if not require_admin(user):
|
||||||
|
return self.send_error_json("Admin required", 403)
|
||||||
|
if _architect_grounding is None:
|
||||||
|
return self.send_error_json("Unavailable", 503)
|
||||||
|
body = body or {}
|
||||||
|
segment_key = body.get('segment_key')
|
||||||
|
feedback = body.get('feedback_items')
|
||||||
|
conn = get_db()
|
||||||
|
try:
|
||||||
|
if not feedback:
|
||||||
|
feedback = self._ground_feedback_corpus(conn)
|
||||||
|
if not feedback:
|
||||||
|
return self.send_error_json("No LP feedback found to ground against", 404)
|
||||||
|
res = _architect_grounding.ground_objections(feedback, segment_key=segment_key,
|
||||||
|
db_path=DB_PATH, conn=conn)
|
||||||
|
except Exception as exc:
|
||||||
|
return self.send_error_json(str(exc), 502)
|
||||||
|
finally:
|
||||||
|
conn.close()
|
||||||
|
return self.send_json({"data": res})
|
||||||
|
|
||||||
# ─── Architect thesis (Phase 1) ───
|
# ─── Architect thesis (Phase 1) ───
|
||||||
def handle_list_thesis_lines(self, user):
|
def handle_list_thesis_lines(self, user):
|
||||||
if thesis_review is None:
|
if thesis_review is None:
|
||||||
|
|||||||
@@ -0,0 +1,175 @@
|
|||||||
|
# Spark Control — `/scrub` + `/rehydrate` endpoints (handover prompt)
|
||||||
|
|
||||||
|
*Hand this to the Spark Control developer to build the gateway redaction endpoints.
|
||||||
|
**OPTIONAL for Phase 1** — the Architect's grounding boundary already ships in our app
|
||||||
|
repo (the `backend/redaction/` module, deterministic scrub + golden-file leak tests).
|
||||||
|
Build this when we move to **multi-agent** enforcement (Phase 2 Analyst, Phase 3 Closer),
|
||||||
|
where we want ONE bypass-proof point with the pseudonym map living next to the local
|
||||||
|
models. Our in-repo module is the **reference implementation**; the contract below is what
|
||||||
|
our `SCRUB_BACKEND=gateway` client already calls, so aim for behavioral parity and we cut
|
||||||
|
agents over with zero app changes. Related: `docs/redaction-rehydration.md`.*
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Purpose
|
||||||
|
|
||||||
|
Add two gateway endpoints, `POST /scrub` and `POST /rehydrate`, that let any agent send
|
||||||
|
LP-specific context to the Claude API without exposing sovereign data. `/scrub`
|
||||||
|
de-identifies an agent's assembled context; the agent sends the de-identified text to
|
||||||
|
Claude; `/rehydrate` puts the real values back into Claude's response for human review.
|
||||||
|
The **pseudonym map** that links tokens to real values is the de-anonymization key — it
|
||||||
|
**must stay on the Spark, next to the local models, and never be sent or logged to any
|
||||||
|
third party.** This sits behind the same trusted Spark Control URL, TLS, and access
|
||||||
|
control that already front `/v1/chat/completions`, `/v1/embeddings`, `/v1/rerank`, and
|
||||||
|
`/api/search`. Spark Control does **not** call Claude here — it is the scrub and rehydrate
|
||||||
|
transform pair plus a server-held map.
|
||||||
|
|
||||||
|
## The three-tier classification (the rules `/scrub` enforces)
|
||||||
|
|
||||||
|
Classify every span into exactly one tier:
|
||||||
|
|
||||||
|
- **TIER-1 NEVER-SEND** — excluded entirely, not even tokenized: full LP list/export or
|
||||||
|
bulk relationship graph, raw account/wire/routing numbers, SSN/passport/gov-ID, anything
|
||||||
|
under a confidentiality obligation. Default: excise the span. `tier1_action="reject"`
|
||||||
|
makes the whole call fail-closed (422) if any Tier-1 is detected.
|
||||||
|
- **TIER-2 TOKENIZE** — stable placeholders, swapped back locally after Claude: person
|
||||||
|
names, org/fund names, emails, phones, addresses, exact $ amounts, identity-pinning
|
||||||
|
dates. Placeholders are `[TYPE_N]`: `[PERSON_1] [ORG_1] [FUND_1] [EMAIL_1] [PHONE_1]
|
||||||
|
[ADDR_1] [AMOUNT_1] [DATE_1] [LOC_1] [MISC_1]`. `N` is 1-based and **stable within a
|
||||||
|
task**: the same real entity maps to the same token across every item in the call and
|
||||||
|
across later `/scrub` calls reusing the same `map_handle`, so Claude can reason about
|
||||||
|
relationships (`[PERSON_1] introduced [PERSON_2] to [FUND_1]`).
|
||||||
|
- **SEND-AS-IS** — passed through untouched: the substance Claude needs (objections,
|
||||||
|
sentiment, generic deal mechanics, the drafted message body minus identifiers).
|
||||||
|
|
||||||
|
## The round-trip
|
||||||
|
|
||||||
|
`SCRUB` (your endpoint) → `REASON` (the agent calls Claude with placeholders only — your
|
||||||
|
gateway does NOT call Claude) → `RE-HYDRATE` (your endpoint) → human review (in our app).
|
||||||
|
|
||||||
|
## Request / response contracts
|
||||||
|
|
||||||
|
### `POST /scrub`
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"task_id": "string, required, caller-chosen, stable across the round-trip",
|
||||||
|
"actor": "string, agent name e.g. 'analyst' | 'closer' (for logging)",
|
||||||
|
"items": [ {"id": "ctx_1", "text": "..."} ],
|
||||||
|
"known_entities": { // CALLER-SUPPLIED dictionary (see "Dictionary" below)
|
||||||
|
"persons": ["..."], "orgs": ["..."], "funds": ["..."], "emails": ["..."]
|
||||||
|
},
|
||||||
|
"tier1_action": "drop | reject", // default "drop"
|
||||||
|
"bucket": {"amounts": false, "dates": false}, // default FALSE for grounding: tokenize reversibly (no magnitudes to Claude). bucket=true only when a caller genuinely needs coarse magnitude
|
||||||
|
"ner": "auto | rules_only | qwen", // default "auto"
|
||||||
|
"map_handle": "string, optional, reuse/extend an existing task map"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
Response `200`:
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"task_id": "...",
|
||||||
|
"map_handle": "opaque server key to the map (NOT the map itself)",
|
||||||
|
"items": [ {"id":"ctx_1","scrubbed_text":"...","tokens_used":["PERSON_1","AMOUNT_1"]} ],
|
||||||
|
"stats": {"tier1_dropped": 2, "tier2_tokenized": 14, "distinct_entities": 9,
|
||||||
|
"descriptive_flags": [{"item":"ctx_1","span":"the family that sold the mining company in Texas","action":"redacted"}]},
|
||||||
|
"expires_at": "ISO-8601 map TTL (short-lived, e.g. 2h)"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
`422 {"error":"tier1_detected","spans":[...]}` when `tier1_action="reject"` and Tier-1 found.
|
||||||
|
`400` on malformed input.
|
||||||
|
|
||||||
|
### `POST /rehydrate`
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"task_id": "string, required",
|
||||||
|
"map_handle": "string, required, from /scrub",
|
||||||
|
"items": [ {"id":"out_1","text":"...with [PERSON_1] tokens..."} ],
|
||||||
|
"actor": "string",
|
||||||
|
"strict": true // default true
|
||||||
|
}
|
||||||
|
```
|
||||||
|
Response `200`:
|
||||||
|
```json
|
||||||
|
{ "items": [ {"id":"out_1","rehydrated_text":"...real values..."} ],
|
||||||
|
"stats": {"tokens_substituted": 6, "unknown_tokens": []} }
|
||||||
|
```
|
||||||
|
`409 {"error":"unknown_tokens","tokens":["PERSON_9"]}` when `strict` and the text contains a
|
||||||
|
token with no map entry — this is your tripwire for a Claude-hallucinated/smuggled token; do
|
||||||
|
NOT silently pass it through. `410 {"error":"map_expired"}` if the map TTL lapsed.
|
||||||
|
|
||||||
|
## Dictionary: CALLER-SUPPLIED (Option 1 — decided)
|
||||||
|
|
||||||
|
The known-entity dictionary is supplied by the **caller** in each `/scrub` request
|
||||||
|
(`known_entities`), NOT read by Spark Control from the CRM. We chose this over giving the
|
||||||
|
gateway CRM access because it keeps Spark Control **generic and portable** (no coupling to
|
||||||
|
Ten31's schema, so the package still works for another dual-Spark user), needs **no CRM
|
||||||
|
credentials on the gateway** (least privilege — the LP list lives in one place), stays
|
||||||
|
**fresh** (built live from the CRM each call), and matches the reference `scrub()` signature.
|
||||||
|
Our app builds it with its `build_known_entities` (names + name-parts + email local-parts)
|
||||||
|
and sends it scoped to the request. Treat it as **sensitive** (a slice of the LP list): hold
|
||||||
|
it only transiently with the map, never log or forward it. Tokens are per-task (stable within
|
||||||
|
a `task_id`/`map_handle`), which is also the lower re-identification-risk choice. If per-call
|
||||||
|
payload ever becomes a perf issue, an optional gateway-side cached export (Option 3) is the
|
||||||
|
escape hatch — but do not build that coupling now.
|
||||||
|
|
||||||
|
## Local-Qwen NER (how to find Tier-2 entities), cheapest/most-authoritative first
|
||||||
|
|
||||||
|
1. **The caller-supplied `known_entities`.** Tokenize every supplied person/org/fund/email
|
||||||
|
surface deterministically (case- and unicode-fold insensitive, longest-match-first, with
|
||||||
|
hyphenated-surname extension). This is the bulk of the work and needs no model. (This is
|
||||||
|
the deterministic FLOOR — load-bearing but NOT complete; the NER pass below is required.)
|
||||||
|
2. **Rules** for structured PII: regexes for emails, phones, $ amounts, dates, and
|
||||||
|
SSN/account-number shapes (account/SSN shapes route to Tier-1).
|
||||||
|
3. **Local-Qwen NER pass** for residual named entities the map and rules miss (a person/org
|
||||||
|
in free text not yet in the CRM). Call the SAME local Qwen you already serve at
|
||||||
|
`/v1/chat/completions` (enable_thinking=false, temperature 0), with a strict JSON-only
|
||||||
|
extraction prompt returning `{entities:[{text,type,tier}]}`. Never a remote model — the
|
||||||
|
input is exactly the sensitive text we are keeping local. `ner="rules_only"` skips this;
|
||||||
|
`ner="qwen"` forces it; `auto` runs it only on spans unresolved by steps 1–2. Qwen should
|
||||||
|
also flag **descriptive** re-identifiers (e.g. "the family that sold the mining company in
|
||||||
|
Texas"), not just named entities.
|
||||||
|
|
||||||
|
## Map-stays-local (non-negotiable)
|
||||||
|
|
||||||
|
The pseudonym map `{token -> real_value}` is stored ONLY on the Spark, keyed by
|
||||||
|
`map_handle`, in memory or a short-lived local store, TTL-expired (default ~2h). Never
|
||||||
|
returned in full, never written to any log/metric/trace that leaves the box, never in any
|
||||||
|
Claude-bound payload. `/scrub` returns only the opaque `map_handle` and counts. If you add
|
||||||
|
`GET /scrub/map/{map_handle}` for same-box debugging, gate it behind the same auth and keep
|
||||||
|
it off by default.
|
||||||
|
|
||||||
|
## Logging
|
||||||
|
|
||||||
|
One row per call to our `interaction_log` (we give you a write path or ingest an emitted
|
||||||
|
event): `action='redaction.scrub' | 'redaction.rehydrate'`, `actor_type='agent'`,
|
||||||
|
`actor_id=<actor>`, `source='spark_control'`, `payload` = **COUNTS ONLY** (tiers dropped,
|
||||||
|
tokens by type, distinct_entities, descriptive_flags spans WITHOUT real values, model used).
|
||||||
|
The payload MUST NOT contain any real Tier-2 value, any Tier-1 content, or the map.
|
||||||
|
|
||||||
|
## Acceptance tests (must pass before agents route through it)
|
||||||
|
|
||||||
|
1. **Golden-file diff** — fixed inputs, recorded expected scrubbed output; assert NO Tier-1
|
||||||
|
string and NO real Tier-2 identifier appears in any `/scrub` response or Claude-bound
|
||||||
|
payload. (We will share our `backend/redaction/` fixtures + golden files.)
|
||||||
|
2. **Round-trip identity** — scrub → echo tokens → rehydrate reproduces the original real
|
||||||
|
values exactly; token stability holds across items and across a second `/scrub` reusing
|
||||||
|
the same `map_handle`.
|
||||||
|
3. **Re-identification spot-check** — feed ONLY the de-identified prompt to the local Qwen
|
||||||
|
and ask it to name the real people/orgs/amounts; anything it recovers (esp. descriptive
|
||||||
|
re-identifiers and amount+date+sector inference) is flagged and must be driven to zero or
|
||||||
|
escalated to Tier-1/bucketing.
|
||||||
|
4. **Map-leak assertion** — scan every response body, log row, and Claude-bound payload in
|
||||||
|
the suite; assert the map and all real values are absent.
|
||||||
|
5. **Strict-rehydrate tripwire** — a rehydrate input with an unmapped token returns 409.
|
||||||
|
6. **Fail-closed** — `tier1_action="reject"` returns 422 on any Tier-1 input, emits nothing.
|
||||||
|
|
||||||
|
## Notes
|
||||||
|
|
||||||
|
- This does NOT replace **minimization**: the agent must first ask "does Claude need this
|
||||||
|
record content at all?" — often a local retrieval summary suffices. These endpoints are
|
||||||
|
for when the answer is genuinely yes.
|
||||||
|
- Keep placeholders **cache-stable** (deterministic token assignment per task_id/map_handle)
|
||||||
|
so they compose with prompt caching on the Claude side.
|
||||||
|
- We will hand you our in-repo `redaction` module + golden files as the reference; aim for
|
||||||
|
behavioral parity first, then we cut agents over to the gateway as the single enforcement
|
||||||
|
point.
|
||||||
@@ -85,6 +85,11 @@ export CRM_DB_PATH="${CRM_DB_PATH:-$DATA_DIR/crm.db}"
|
|||||||
export SPARK_CONTROL_URL="${SPARK_CONTROL_URL:-https://192.168.1.72:62419}"
|
export SPARK_CONTROL_URL="${SPARK_CONTROL_URL:-https://192.168.1.72:62419}"
|
||||||
export SPARK_CONTROL_VERIFY_TLS="${SPARK_CONTROL_VERIFY_TLS:-false}"
|
export SPARK_CONTROL_VERIFY_TLS="${SPARK_CONTROL_VERIFY_TLS:-false}"
|
||||||
export QDRANT_URL="${QDRANT_URL:-http://192.168.1.87:6333}"
|
export QDRANT_URL="${QDRANT_URL:-http://192.168.1.87:6333}"
|
||||||
|
# Redaction boundary backend for the Architect's grounding step (Workstream D):
|
||||||
|
# local (default) = in-repo deterministic scrubber (backend/redaction/), map in-process.
|
||||||
|
# gateway = Spark Control POST /scrub + /rehydrate, once that ships.
|
||||||
|
# Flip to 'gateway' only after the Spark Control endpoints are live (same contract).
|
||||||
|
export SCRUB_BACKEND="${SCRUB_BACKEND:-local}"
|
||||||
# OPERATOR: how often (minutes) the background sync scheduler re-runs the
|
# OPERATOR: how often (minutes) the background sync scheduler re-runs the
|
||||||
# incremental ingest sync to keep the Qdrant search index fresh. Default 60.
|
# incremental ingest sync to keep the Qdrant search index fresh. Default 60.
|
||||||
export CRM_INGEST_SYNC_INTERVAL_MIN="${CRM_INGEST_SYNC_INTERVAL_MIN:-60}"
|
export CRM_INGEST_SYNC_INTERVAL_MIN="${CRM_INGEST_SYNC_INTERVAL_MIN:-60}"
|
||||||
|
|||||||
@@ -19,8 +19,9 @@ export const PACKAGE_TITLE = 'Ten31 Database'
|
|||||||
// * 0.1.0:51 (entity-resolution fix: people double-count + duplicate queue)
|
// * 0.1.0:51 (entity-resolution fix: people double-count + duplicate queue)
|
||||||
// * 0.1.0:52 (grid/contacts unification: contact_id link + grid as front door)
|
// * 0.1.0:52 (grid/contacts unification: contact_id link + grid as front door)
|
||||||
// * 0.1.0:53 (seed v5 thesis into the Architect Workshop)
|
// * 0.1.0:53 (seed v5 thesis into the Architect Workshop)
|
||||||
// * Current: 0.1.0:54 (unification polish: LinkedIn in grid inline contact editor)
|
// * 0.1.0:54 (unification polish: LinkedIn in grid inline contact editor)
|
||||||
export const PACKAGE_VERSION = '0.1.0:54'
|
// * Current: 0.1.0:55 (Architect grounding boundary: redaction/re-hydration privacy gate)
|
||||||
|
export const PACKAGE_VERSION = '0.1.0:55'
|
||||||
|
|
||||||
export const DATA_MOUNT_PATH = '/data'
|
export const DATA_MOUNT_PATH = '/data'
|
||||||
export const WEB_PORT = 8080
|
export const WEB_PORT = 8080
|
||||||
|
|||||||
@@ -15,8 +15,9 @@ import { v_0_1_0_51 } from './v0.1.0.51'
|
|||||||
import { v_0_1_0_52 } from './v0.1.0.52'
|
import { v_0_1_0_52 } from './v0.1.0.52'
|
||||||
import { v_0_1_0_53 } from './v0.1.0.53'
|
import { v_0_1_0_53 } from './v0.1.0.53'
|
||||||
import { v_0_1_0_54 } from './v0.1.0.54'
|
import { v_0_1_0_54 } from './v0.1.0.54'
|
||||||
|
import { v_0_1_0_55 } from './v0.1.0.55'
|
||||||
|
|
||||||
export const versionGraph = VersionGraph.of({
|
export const versionGraph = VersionGraph.of({
|
||||||
current: v_0_1_0_54,
|
current: v_0_1_0_55,
|
||||||
other: [v_0_1_0_39, v_0_1_0_40, v_0_1_0_41, v_0_1_0_42, v_0_1_0_43, v_0_1_0_44, v_0_1_0_45, v_0_1_0_46, v_0_1_0_47, v_0_1_0_48, v_0_1_0_49, v_0_1_0_50, v_0_1_0_51, v_0_1_0_52, v_0_1_0_53],
|
other: [v_0_1_0_39, v_0_1_0_40, v_0_1_0_41, v_0_1_0_42, v_0_1_0_43, v_0_1_0_44, v_0_1_0_45, v_0_1_0_46, v_0_1_0_47, v_0_1_0_48, v_0_1_0_49, v_0_1_0_50, v_0_1_0_51, v_0_1_0_52, v_0_1_0_53, v_0_1_0_54],
|
||||||
})
|
})
|
||||||
|
|||||||
@@ -0,0 +1,22 @@
|
|||||||
|
import { VersionInfo } from '@start9labs/start-sdk'
|
||||||
|
|
||||||
|
// The Architect grounding boundary (Phase 1 Workstream D): the redaction / re-hydration
|
||||||
|
// privacy gate so the Architect can ground its thesis in REAL LP feedback without sending
|
||||||
|
// any LP identity to Claude. A local-Qwen summary minimizes first, a deterministic scrubber
|
||||||
|
// (hardened across three adversarial leak-hunts) tokenizes residual identifiers, a local-Qwen
|
||||||
|
// NER pass backstops unknown names, Claude reasons only over the de-identified register, and
|
||||||
|
// the draft is re-hydrated locally for human review. Fails CLOSED if the local model is
|
||||||
|
// unreachable. SCRUB_BACKEND=local (default) or gateway (Spark Control /scrub + /rehydrate,
|
||||||
|
// once built). No data migration.
|
||||||
|
export const v_0_1_0_55 = VersionInfo.of({
|
||||||
|
version: '0.1.0:55',
|
||||||
|
releaseNotes: {
|
||||||
|
en_US: [
|
||||||
|
'Adds the Architect grounding boundary: the Architect can now pressure-test the thesis',
|
||||||
|
'against real recurring LP objections WITHOUT any LP name, email, amount, or other',
|
||||||
|
'identifier reaching Claude — everything is de-identified locally first and the draft is',
|
||||||
|
'restored on the server for your review. Sovereignty by construction; fails closed.',
|
||||||
|
].join(' '),
|
||||||
|
},
|
||||||
|
migrations: { up: async () => {}, down: async () => {} },
|
||||||
|
})
|
||||||
Reference in New Issue
Block a user