Initial commit - 611 cybersecurity skills across all subdomains

2026-08-26 19:29:41 +03:00 · 2026-02-25 10:47:44 +01:00
commit 22a7ab1462
1765 changed files with 280648 additions and 0 deletions
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+#!/usr/bin/env python3
+"""
+Asset Criticality Scoring Engine
+
+Calculates multi-factor criticality scores for assets and
+applies SLA modifiers to vulnerability remediation timelines.
+
+Requirements:
+    pip install pandas
+
+Usage:
+    python process.py score --csv assets.csv --output scored_assets.csv
+    python process.py apply --assets scored_assets.csv --vulns vulns.csv --output adjusted.csv
+"""
+
+import argparse
+import sys
+
+import pandas as pd
+
+
+WEIGHTS = {
+    "business_function": 0.25,
+    "data_sensitivity": 0.25,
+    "regulatory_scope": 0.15,
+    "network_exposure": 0.15,
+    "recoverability": 0.10,
+    "user_population": 0.10,
+}
+
+TIERS = [
+    (4.5, 1, "Crown Jewels", -0.50),
+    (3.5, 2, "High Value", -0.25),
+    (2.5, 3, "Standard", 0.00),
+    (1.5, 4, "Low Impact", 0.25),
+    (1.0, 5, "Minimal", 0.50),
+]
+
+BASE_SLA = {"Critical": 14, "High": 30, "Medium": 60, "Low": 90}
+
+
+def score_assets(df):
+    """Calculate criticality scores for all assets."""
+    scores = []
+    for _, row in df.iterrows():
+        weighted = sum(
+            row.get(factor, 3) * weight
+            for factor, weight in WEIGHTS.items()
+        )
+        score = round(weighted, 2)
+
+        tier, label, sla_mod = 5, "Minimal", 0.50
+        for threshold, t, l, s in TIERS:
+            if score >= threshold:
+                tier, label, sla_mod = t, l, s
+                break
+
+        scores.append({
+            **row.to_dict(),
+            "criticality_score": score,
+            "tier": tier,
+            "tier_label": label,
+            "sla_modifier": sla_mod,
+        })
+
+    return pd.DataFrame(scores).sort_values("criticality_score", ascending=False)
+
+
+def apply_to_vulns(assets_df, vulns_df):
+    """Apply asset criticality to vulnerability SLAs."""
+    asset_map = {}
+    for _, row in assets_df.iterrows():
+        asset_map[row.get("asset_id", "")] = {
+            "tier": row["tier"],
+            "label": row["tier_label"],
+            "sla_modifier": row["sla_modifier"],
+        }
+
+    results = []
+    for _, vuln in vulns_df.iterrows():
+        asset_id = vuln.get("asset_id", "")
+        asset = asset_map.get(asset_id, {"tier": 3, "label": "Standard", "sla_modifier": 0})
+        severity = vuln.get("severity", "Medium")
+        base_sla = BASE_SLA.get(severity, 60)
+        adjusted_sla = max(1, int(base_sla * (1 + asset["sla_modifier"])))
+
+        results.append({
+            **vuln.to_dict(),
+            "asset_tier": asset["tier"],
+            "asset_label": asset["label"],
+            "base_sla_days": base_sla,
+            "adjusted_sla_days": adjusted_sla,
+        })
+
+    return pd.DataFrame(results)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Asset Criticality Scoring Engine")
+    subparsers = parser.add_subparsers(dest="command")
+
+    s_p = subparsers.add_parser("score", help="Score assets")
+    s_p.add_argument("--csv", required=True)
+    s_p.add_argument("--output", default="scored_assets.csv")
+
+    a_p = subparsers.add_parser("apply", help="Apply to vulnerabilities")
+    a_p.add_argument("--assets", required=True)
+    a_p.add_argument("--vulns", required=True)
+    a_p.add_argument("--output", default="adjusted_vulns.csv")
+
+    args = parser.parse_args()
+
+    if args.command == "score":
+        df = pd.read_csv(args.csv)
+        scored = score_assets(df)
+        scored.to_csv(args.output, index=False)
+        print(f"[+] Scored {len(scored)} assets to {args.output}")
+        print(scored["tier_label"].value_counts().to_string())
+
+    elif args.command == "apply":
+        assets = pd.read_csv(args.assets)
+        vulns = pd.read_csv(args.vulns)
+        result = apply_to_vulns(assets, vulns)
+        result.to_csv(args.output, index=False)
+        print(f"[+] Applied criticality to {len(result)} vulnerabilities")
+
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()