fix(mock-data): Fake-Daten bei leerer DB entfernt — ISMS 0%, Dashboard keine simulierten Trends, Compliance-Hub keine Fallback-Zahlen

- ISMS Overview: 14% → 0% bei leerer DB, "not_started" Status, alle Kapitel 0%
- Dashboard: 12-Monate simulierte Trend-Historie entfernt
- Compliance-Hub: Hardcoded Fallback-Statistiken (474/180/95/120/79/44/558/19) → 0
- SQLAlchemy Bug: `is not None` → `.isnot(None)` in SoA-Query
- Hardcoded chapter_7/8_status="pass" → berechnet aus Findings

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend-compliance/compliance/api/dashboard_routes.py

@@ -164,16 +164,14 @@ async def get_executive_dashboard(db: Session = Depends(get_db)):
     else:
         traffic_light = "red"
 
-    # Generate trend data (last 12 months - simulated for now)
+    # Trend data — only show current score, no simulated history
     trend_data = []
-    now = datetime.utcnow()
-    for i in range(11, -1, -1):
-        month_date = now - timedelta(days=i * 30)
-        trend_score = max(0, min(100, score - (11 - i) * 2 + (5 if i > 6 else 0)))
+    if total > 0:
+        now = datetime.utcnow()
         trend_data.append(TrendDataPoint(
-            date=month_date.strftime("%Y-%m-%d"),
-            score=round(trend_score, 1),
-            label=month_abbr[month_date.month][:3],
+            date=now.strftime("%Y-%m-%d"),
+            score=round(score, 1),
+            label=month_abbr[now.month][:3],
         ))
 
     # Get top 5 risks (sorted by severity)
@@ -304,21 +302,16 @@ async def get_compliance_trend(
 
     current_score = ((passing + partial * 0.5) / total) * 100 if total > 0 else 0
 
-    # Generate simulated historical data
+    # Trend data — only current score, no simulated history
     trend_data = []
-    now = datetime.utcnow()
-
-    for i in range(months - 1, -1, -1):
-        month_date = now - timedelta(days=i * 30)
-        variation = ((i * 7) % 5) - 2
-        trend_score = max(0, min(100, current_score - (months - 1 - i) * 1.5 + variation))
-
+    if total > 0:
+        now = datetime.utcnow()
         trend_data.append({
-            "date": month_date.strftime("%Y-%m-%d"),
-            "score": round(trend_score, 1),
-            "label": f"{month_abbr[month_date.month]} {month_date.year % 100}",
-            "month": month_date.month,
-            "year": month_date.year,
+            "date": now.strftime("%Y-%m-%d"),
+            "score": round(current_score, 1),
+            "label": f"{month_abbr[now.month]} {now.year % 100}",
+            "month": now.month,
+            "year": now.year,
         })
 
     return {