Benjamin Admin
8510af46eb
Phase 0: Quality Audit script (Claude Sonnet, 1750 samples) Phase 1: Object ontology expanded 31 → 74 tokens with descriptions + boundaries Phase 2: 174K controls re-classified via Haiku (10 batches, $50) - Generic tokens removed (documentation, procedure, process) - L2 sub-topics added (108K + 64K controls) - Bad subtopics fixed (stakeholder_*, escalation fragments) Phase 3: Re-clustering K=18704 (37K objects → 16.7K groups) Phase 4: Direct MC generation from canonical tokens (gpre2_direct_mc.py) Phase 5: Regulation-source split (gpre3, dry-run tested) New features: - Tenant-isolated document upload API (rag-service) - BAuA crawler (Playwright, 131 PDFs downloaded) - OSHA Technical Manual crawler (23 chapters) - CE obligation extractor (6141 obligations from Qdrant) RAG ingestion: - 126 BAuA PDFs (TRBS/TRGS/ASR): 27,664 chunks - OSHA Technical Manual: 7,241 chunks - OSHA 1910 Subpart O (full): 745 chunks - EuGH C-588/21 P: 216 chunks - EU 2018/1725: 842 chunks Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
311 lines
12 KiB
Python
311 lines
12 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Phase 0: Quality Audit for Master Control Assignments.
|
|
|
|
Uses Claude Sonnet to validate whether controls are correctly assigned
|
|
to their Master Controls. Samples controls from large and small MCs.
|
|
|
|
Usage:
|
|
python3 /app/scripts/gpre_quality_audit.py
|
|
python3 /app/scripts/gpre_quality_audit.py --large-sample 50 --small-sample 10
|
|
python3 /app/scripts/gpre_quality_audit.py --mc MC-8292 # single MC
|
|
"""
|
|
|
|
import argparse
|
|
import json
|
|
import logging
|
|
import os
|
|
import random
|
|
import time
|
|
from collections import defaultdict
|
|
|
|
import httpx
|
|
from sqlalchemy import create_engine, text
|
|
|
|
logging.basicConfig(
|
|
level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s"
|
|
)
|
|
logger = logging.getLogger("quality-audit")
|
|
|
|
DB_URL = os.getenv(
|
|
"DATABASE_URL",
|
|
"postgresql://breakpilot:breakpilot123@postgres:5432/breakpilot_db",
|
|
)
|
|
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
|
|
ANTHROPIC_MODEL = os.getenv("AUDIT_MODEL", "claude-sonnet-4-20250514")
|
|
ANTHROPIC_URL = "https://api.anthropic.com/v1/messages"
|
|
|
|
SYSTEM_PROMPT = """Du bist ein Compliance-Experte der prüft ob Controls korrekt zu Master Controls zugeordnet sind.
|
|
|
|
Für jeden Control beantworte:
|
|
1. MATCH: Gehört dieser Control thematisch zum Master Control Topic?
|
|
2. CONFIDENCE: Wie sicher bist du? (0.0-1.0)
|
|
3. REASON: Kurze Begründung (max 1 Satz)
|
|
4. SUGGESTED_TOPIC: Falls MATCH=false, welches Topic wäre korrekt?
|
|
|
|
Wichtige Unterscheidungen:
|
|
- "monitoring" = kontinuierliche Überwachung, Alerting, Log-Analyse
|
|
- "training" = Schulung, Awareness, Lernmaterialien
|
|
- "personal_data" = personenbezogene Daten, DSGVO-Betroffenenrechte
|
|
- "procedure" = Verfahren, Prozesse (aber NICHT wenn es spezifisch um Incidents geht)
|
|
- "incident" = Sicherheitsvorfälle, Breach Notification, Recovery
|
|
- "policy" = Richtlinien, Regelwerke, Governance-Dokumente
|
|
- "encryption" = Verschlüsselung, Kryptografie, Key Management
|
|
- "audit_logging" = Protokollierung, Audit Trail, Nachvollziehbarkeit
|
|
|
|
Antworte NUR als JSON-Array, ein Objekt pro Control."""
|
|
|
|
|
|
def call_claude(controls_batch: list[dict], mc_topic: str) -> list[dict]:
|
|
"""Send a batch of controls to Claude for validation."""
|
|
items = []
|
|
for c in controls_batch:
|
|
items.append(
|
|
f"- Control '{c['control_id']}': "
|
|
f"Titel=\"{c['title']}\", "
|
|
f"Objective=\"{c['objective'][:150]}...\", "
|
|
f"Phase={c['phase']}, Action={c['action']}"
|
|
)
|
|
|
|
prompt = (
|
|
f"Master Control Topic: \"{mc_topic}\"\n\n"
|
|
f"Prüfe diese {len(controls_batch)} Controls:\n\n"
|
|
+ "\n".join(items)
|
|
+ "\n\nAntwort als JSON-Array mit Feldern: "
|
|
"control_id, match (bool), confidence (float), reason (str), "
|
|
"suggested_topic (str, nur wenn match=false)."
|
|
)
|
|
|
|
headers = {
|
|
"x-api-key": ANTHROPIC_API_KEY,
|
|
"anthropic-version": "2023-06-01",
|
|
"content-type": "application/json",
|
|
}
|
|
payload = {
|
|
"model": ANTHROPIC_MODEL,
|
|
"max_tokens": 2048,
|
|
"temperature": 0.1,
|
|
"system": SYSTEM_PROMPT,
|
|
"messages": [{"role": "user", "content": prompt}],
|
|
}
|
|
|
|
for attempt in range(3):
|
|
try:
|
|
resp = httpx.post(
|
|
ANTHROPIC_URL,
|
|
headers=headers,
|
|
json=payload,
|
|
timeout=60.0,
|
|
)
|
|
resp.raise_for_status()
|
|
data = resp.json()
|
|
content = data.get("content", [{}])[0].get("text", "")
|
|
usage = data.get("usage", {})
|
|
|
|
# Parse JSON from response
|
|
start = content.find("[")
|
|
end = content.rfind("]") + 1
|
|
if start >= 0 and end > start:
|
|
results = json.loads(content[start:end])
|
|
return results, usage
|
|
logger.warning("No JSON array in response: %s", content[:200])
|
|
return [], usage
|
|
except httpx.HTTPStatusError as e:
|
|
if e.response.status_code == 429:
|
|
wait = 30 * (attempt + 1)
|
|
logger.warning("Rate limited, waiting %ds...", wait)
|
|
time.sleep(wait)
|
|
else:
|
|
logger.error("API error: %s", e)
|
|
return [], {}
|
|
except Exception as e:
|
|
logger.error("Request failed (attempt %d): %s", attempt + 1, e)
|
|
if attempt < 2:
|
|
time.sleep(5)
|
|
return [], {}
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--large-sample", type=int, default=50,
|
|
help="Controls to sample per large MC")
|
|
parser.add_argument("--small-sample", type=int, default=10,
|
|
help="Controls to sample per small MC")
|
|
parser.add_argument("--small-mc-count", type=int, default=50,
|
|
help="Number of small MCs to audit")
|
|
parser.add_argument("--mc", type=str, default=None,
|
|
help="Audit a single MC by ID (e.g., MC-8292)")
|
|
parser.add_argument("--batch-size", type=int, default=10,
|
|
help="Controls per API call")
|
|
args = parser.parse_args()
|
|
|
|
engine = create_engine(
|
|
DB_URL, connect_args={"options": "-c search_path=compliance,public"}
|
|
)
|
|
|
|
# Load MCs to audit
|
|
with engine.connect() as c:
|
|
if args.mc:
|
|
mcs = c.execute(text("""
|
|
SELECT id, master_control_id, canonical_name, total_controls
|
|
FROM master_controls WHERE master_control_id = :mc
|
|
"""), {"mc": args.mc}).fetchall()
|
|
else:
|
|
# Large MCs (>200) + random small MCs
|
|
large = c.execute(text("""
|
|
SELECT id, master_control_id, canonical_name, total_controls
|
|
FROM master_controls WHERE total_controls > 200
|
|
ORDER BY total_controls DESC
|
|
""")).fetchall()
|
|
|
|
small = c.execute(text("""
|
|
SELECT id, master_control_id, canonical_name, total_controls
|
|
FROM master_controls WHERE total_controls BETWEEN 10 AND 200
|
|
ORDER BY RANDOM() LIMIT :cnt
|
|
"""), {"cnt": args.small_mc_count}).fetchall()
|
|
|
|
mcs = list(large) + list(small)
|
|
|
|
logger.info("Auditing %d Master Controls", len(mcs))
|
|
|
|
# Results tracking
|
|
total_checked = 0
|
|
total_match = 0
|
|
total_mismatch = 0
|
|
total_input_tokens = 0
|
|
total_output_tokens = 0
|
|
mc_results: dict[str, dict] = {}
|
|
all_mismatches: list[dict] = []
|
|
|
|
for mc_uuid, mc_id, canonical, total in mcs:
|
|
is_large = total > 200
|
|
sample_size = args.large_sample if is_large else args.small_sample
|
|
|
|
# Sample controls
|
|
with engine.connect() as c:
|
|
controls = c.execute(text("""
|
|
SELECT mcm.control_uuid, mcm.phase, mcm.action,
|
|
cc.control_id, cc.title,
|
|
COALESCE(cc.objective, '') as objective
|
|
FROM master_control_members mcm
|
|
JOIN canonical_controls cc ON cc.id = mcm.control_uuid
|
|
WHERE mcm.master_control_uuid = CAST(:mc AS uuid)
|
|
ORDER BY RANDOM()
|
|
LIMIT :n
|
|
"""), {"mc": str(mc_uuid), "n": sample_size}).fetchall()
|
|
|
|
if not controls:
|
|
continue
|
|
|
|
control_dicts = [
|
|
{"control_uuid": str(r[0]), "phase": r[1], "action": r[2],
|
|
"control_id": r[3], "title": r[4] or "", "objective": r[5] or ""}
|
|
for r in controls
|
|
]
|
|
|
|
logger.info("\n%s: %s (%d total, sampling %d)",
|
|
mc_id, canonical, total, len(control_dicts))
|
|
|
|
mc_match = 0
|
|
mc_mismatch = 0
|
|
|
|
# Process in batches
|
|
for i in range(0, len(control_dicts), args.batch_size):
|
|
batch = control_dicts[i:i + args.batch_size]
|
|
results, usage = call_claude(batch, canonical)
|
|
|
|
total_input_tokens += usage.get("input_tokens", 0)
|
|
total_output_tokens += usage.get("output_tokens", 0)
|
|
|
|
for r in results:
|
|
if r.get("match", True):
|
|
mc_match += 1
|
|
total_match += 1
|
|
else:
|
|
mc_mismatch += 1
|
|
total_mismatch += 1
|
|
mismatch = {
|
|
"mc_id": mc_id,
|
|
"mc_topic": canonical,
|
|
"control_id": r.get("control_id", "?"),
|
|
"confidence": r.get("confidence", 0),
|
|
"reason": r.get("reason", ""),
|
|
"suggested_topic": r.get("suggested_topic", ""),
|
|
}
|
|
all_mismatches.append(mismatch)
|
|
|
|
total_checked += len(results)
|
|
|
|
# Rate limit
|
|
time.sleep(1)
|
|
|
|
accuracy = mc_match / (mc_match + mc_mismatch) if (mc_match + mc_mismatch) > 0 else 1.0
|
|
mc_results[mc_id] = {
|
|
"canonical": canonical, "total": total,
|
|
"checked": mc_match + mc_mismatch,
|
|
"match": mc_match, "mismatch": mc_mismatch,
|
|
"accuracy": accuracy,
|
|
}
|
|
logger.info(" → %d/%d correct (%.1f%%)",
|
|
mc_match, mc_match + mc_mismatch, accuracy * 100)
|
|
|
|
# Final report
|
|
_print_report(mc_results, all_mismatches, total_checked, total_match,
|
|
total_mismatch, total_input_tokens, total_output_tokens)
|
|
|
|
|
|
def _print_report(mc_results, mismatches, checked, match, mismatch,
|
|
input_tok, output_tok):
|
|
"""Print the quality audit report."""
|
|
logger.info("\n" + "=" * 70)
|
|
logger.info("QUALITY AUDIT REPORT")
|
|
logger.info("=" * 70)
|
|
logger.info("Total controls checked: %d", checked)
|
|
logger.info("Correct assignments: %d (%.1f%%)",
|
|
match, match / max(checked, 1) * 100)
|
|
logger.info("Wrong assignments: %d (%.1f%%)",
|
|
mismatch, mismatch / max(checked, 1) * 100)
|
|
|
|
# Cost estimate
|
|
cost_input = input_tok / 1_000_000 * 3.0 # Sonnet input: $3/MTok
|
|
cost_output = output_tok / 1_000_000 * 15.0 # Sonnet output: $15/MTok
|
|
logger.info("\nAPI Usage: %d input + %d output tokens",
|
|
input_tok, output_tok)
|
|
logger.info("Estimated cost: $%.2f", cost_input + cost_output)
|
|
|
|
# Per-MC breakdown (worst first)
|
|
logger.info("\n--- Per-MC Accuracy (worst first) ---")
|
|
sorted_mcs = sorted(mc_results.values(), key=lambda x: x["accuracy"])
|
|
for mc in sorted_mcs:
|
|
flag = "❌" if mc["accuracy"] < 0.9 else "⚠️" if mc["accuracy"] < 0.95 else "✅"
|
|
logger.info(" %s %s (%s): %d/%d = %.1f%% [total: %d]",
|
|
flag, mc["canonical"][:30].ljust(30),
|
|
"large" if mc["total"] > 200 else "small",
|
|
mc["match"], mc["checked"],
|
|
mc["accuracy"] * 100, mc["total"])
|
|
|
|
# Top mismatches
|
|
if mismatches:
|
|
logger.info("\n--- Mismatches (all %d) ---", len(mismatches))
|
|
for m in sorted(mismatches, key=lambda x: -x.get("confidence", 0)):
|
|
logger.info(" %s in %s (%s) → should be '%s': %s",
|
|
m["control_id"], m["mc_id"], m["mc_topic"],
|
|
m["suggested_topic"], m["reason"])
|
|
|
|
# Size-class breakdown
|
|
large_mcs = [m for m in mc_results.values() if m["total"] > 200]
|
|
small_mcs = [m for m in mc_results.values() if m["total"] <= 200]
|
|
|
|
if large_mcs:
|
|
lg_acc = sum(m["match"] for m in large_mcs) / max(sum(m["checked"] for m in large_mcs), 1)
|
|
logger.info("\nLarge MCs (>200): %.1f%% accuracy (%d MCs)",
|
|
lg_acc * 100, len(large_mcs))
|
|
if small_mcs:
|
|
sm_acc = sum(m["match"] for m in small_mcs) / max(sum(m["checked"] for m in small_mcs), 1)
|
|
logger.info("Small MCs (≤200): %.1f%% accuracy (%d MCs)",
|
|
sm_acc * 100, len(small_mcs))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|