37a867c485
- Complete Cloudflare Terraform configuration (DNS, WAF, tunnels, access) - WAF Intelligence MCP server with threat analysis and ML classification - GitOps automation with PR workflows and drift detection - Observatory monitoring stack with Prometheus/Grafana - IDE operator rules for governed development - Security playbooks and compliance frameworks - Autonomous remediation and state reconciliation
371 lines
13 KiB
Python
371 lines
13 KiB
Python
from __future__ import annotations
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import os
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from dataclasses import dataclass, field
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from mcp.waf_intelligence.analyzer import AnalysisResult, RuleViolation, WAFRuleAnalyzer
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from mcp.waf_intelligence.compliance import ComplianceMapper, FrameworkMapping
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from mcp.waf_intelligence.generator import GeneratedRule, WAFRuleGenerator
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# Optional advanced modules (Phase 7)
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try:
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from mcp.waf_intelligence.threat_intel import (
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ThreatIntelCollector,
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ThreatIntelReport,
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ThreatIndicator,
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)
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_HAS_THREAT_INTEL = True
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except ImportError:
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_HAS_THREAT_INTEL = False
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ThreatIntelCollector = None
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try:
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from mcp.waf_intelligence.classifier import (
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ThreatClassifier,
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ClassificationResult,
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)
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_HAS_CLASSIFIER = True
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except ImportError:
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_HAS_CLASSIFIER = False
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ThreatClassifier = None
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@dataclass
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class WAFInsight:
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"""Single high-quality insight across analysis + generation + compliance."""
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violation: RuleViolation | None
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suggested_rule: GeneratedRule | None
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mappings: List[FrameworkMapping]
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@dataclass
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class ThreatAssessment:
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"""Phase 7: Comprehensive threat assessment result."""
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analysis_result: Optional[AnalysisResult] = None
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threat_report: Optional[Any] = None # ThreatIntelReport when available
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classification_summary: Dict[str, int] = field(default_factory=dict)
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risk_score: float = 0.0
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recommended_actions: List[str] = field(default_factory=list)
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generated_at: datetime = field(default_factory=datetime.utcnow)
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@property
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def risk_level(self) -> str:
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if self.risk_score >= 0.8:
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return "critical"
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elif self.risk_score >= 0.6:
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return "high"
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elif self.risk_score >= 0.4:
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return "medium"
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else:
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return "low"
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class WAFIntelligence:
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"""
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Quality-first orchestration layer:
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- analyze WAF config
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- propose a few rules
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- attach compliance mappings
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- Phase 7: integrate threat intel and ML classification
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"""
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def __init__(
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self,
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workspace_path: Optional[str] = None,
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enable_threat_intel: bool = True,
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enable_ml_classifier: bool = True,
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) -> None:
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self.workspace = Path(workspace_path) if workspace_path else Path.cwd()
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# Core components
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self.analyzer = WAFRuleAnalyzer()
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self.generator = WAFRuleGenerator()
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self.mapper = ComplianceMapper()
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# Phase 7 components (optional)
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self.threat_intel: Optional[Any] = None
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self.classifier: Optional[Any] = None
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if enable_threat_intel and _HAS_THREAT_INTEL:
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try:
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self.threat_intel = ThreatIntelCollector()
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except Exception:
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pass
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if enable_ml_classifier and _HAS_CLASSIFIER:
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try:
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self.classifier = ThreatClassifier()
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except Exception:
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pass
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def analyze_and_recommend(
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self,
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path: str,
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*,
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limit: int = 3,
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min_severity: str = "warning",
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) -> List[WAFInsight]:
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analysis: AnalysisResult = self.analyzer.analyze_file(
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path,
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min_severity=min_severity,
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)
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top_violations = analysis.top_violations(
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min_severity=min_severity,
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limit=limit,
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)
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insights: List[WAFInsight] = []
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for violation in top_violations:
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mappings = self.mapper.best_effort_from_violation(violation.message)
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scenario = violation.message
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rules = self.generator.generate_from_scenario(scenario, limit=1)
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suggested = rules[0] if rules else None
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insights.append(
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WAFInsight(
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violation=violation,
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suggested_rule=suggested,
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mappings=mappings,
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)
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)
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return insights
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# ─────────────────────────────────────────────────────────────────────────
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# Phase 7: Advanced threat intelligence methods
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# ─────────────────────────────────────────────────────────────────────────
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def collect_threat_intel(
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self,
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log_paths: Optional[List[str]] = None,
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max_indicators: int = 100,
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) -> Optional[Any]:
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"""
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Collect threat intelligence from logs and external feeds.
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Args:
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log_paths: Paths to Cloudflare log files
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max_indicators: Maximum indicators to collect
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Returns:
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ThreatIntelReport or None if unavailable
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"""
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if not self.threat_intel:
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return None
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# Default log paths
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if log_paths is None:
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log_paths = [
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str(self.workspace / "logs"),
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"/var/log/cloudflare",
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]
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return self.threat_intel.collect(
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log_paths=log_paths,
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max_indicators=max_indicators,
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)
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def classify_threat(self, payload: str) -> Optional[Any]:
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"""
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Classify a payload using ML classifier.
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Args:
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payload: Request payload to classify
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Returns:
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ClassificationResult or None
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"""
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if not self.classifier:
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return None
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return self.classifier.classify(payload)
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def full_assessment(
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self,
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waf_config_path: Optional[str] = None,
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log_paths: Optional[List[str]] = None,
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include_threat_intel: bool = True,
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) -> ThreatAssessment:
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"""
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Phase 7: Perform comprehensive threat assessment.
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Combines:
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- WAF configuration analysis
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- Threat intelligence collection
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- ML classification summary
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- Risk scoring
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Args:
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waf_config_path: Path to WAF Terraform file
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log_paths: Paths to log files
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include_threat_intel: Whether to collect threat intel
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Returns:
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ThreatAssessment with full analysis results
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"""
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assessment = ThreatAssessment()
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risk_factors: List[float] = []
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recommendations: List[str] = []
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# 1. Analyze WAF configuration
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if waf_config_path is None:
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waf_config_path = str(self.workspace / "terraform" / "waf.tf")
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if Path(waf_config_path).exists():
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assessment.analysis_result = self.analyzer.analyze_file(
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waf_config_path,
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min_severity="info",
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)
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# Calculate risk from violations
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severity_weights = {"error": 0.8, "warning": 0.5, "info": 0.2}
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for violation in assessment.analysis_result.violations:
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weight = severity_weights.get(violation.severity, 0.3)
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risk_factors.append(weight)
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# Generate recommendations
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critical_count = sum(
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1 for v in assessment.analysis_result.violations
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if v.severity == "error"
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)
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if critical_count > 0:
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recommendations.append(
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f"🔴 Fix {critical_count} critical WAF configuration issues"
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)
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# 2. Collect threat intelligence
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if include_threat_intel and self.threat_intel:
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try:
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assessment.threat_report = self.collect_threat_intel(
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log_paths=log_paths,
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max_indicators=50,
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)
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if assessment.threat_report:
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indicators = assessment.threat_report.indicators
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# Count by severity
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severity_counts = {"critical": 0, "high": 0, "medium": 0, "low": 0}
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for ind in indicators:
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sev = getattr(ind, "severity", "low")
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severity_counts[sev] = severity_counts.get(sev, 0) + 1
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# Add to classification summary
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assessment.classification_summary["threat_indicators"] = len(indicators)
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assessment.classification_summary.update(severity_counts)
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# Calculate threat intel risk
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if indicators:
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critical_ratio = severity_counts["critical"] / len(indicators)
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high_ratio = severity_counts["high"] / len(indicators)
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risk_factors.append(critical_ratio * 0.9 + high_ratio * 0.7)
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if severity_counts["critical"] > 0:
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recommendations.append(
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f"🚨 Block {severity_counts['critical']} critical threat IPs immediately"
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)
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except Exception:
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pass
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# 3. ML classification summary (from any collected data)
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if self.classifier and assessment.threat_report:
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try:
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attack_types = {"sqli": 0, "xss": 0, "rce": 0, "clean": 0, "unknown": 0}
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indicators = assessment.threat_report.indicators
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pattern_indicators = [
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i for i in indicators
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if getattr(i, "indicator_type", "") == "pattern"
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]
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for ind in pattern_indicators[:20]: # Sample first 20
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result = self.classifier.classify(ind.value)
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if result:
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label = result.label
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attack_types[label] = attack_types.get(label, 0) + 1
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assessment.classification_summary["ml_classifications"] = attack_types
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# Add ML risk factor
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dangerous = attack_types.get("sqli", 0) + attack_types.get("rce", 0)
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if dangerous > 5:
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risk_factors.append(0.8)
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recommendations.append(
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f"⚠️ ML detected {dangerous} dangerous attack patterns"
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)
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except Exception:
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pass
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# 4. Calculate final risk score
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if risk_factors:
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assessment.risk_score = min(1.0, sum(risk_factors) / max(len(risk_factors), 1))
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else:
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assessment.risk_score = 0.3 # Baseline risk
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assessment.recommended_actions = recommendations
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return assessment
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def generate_gitops_proposals(
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self,
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threat_report: Optional[Any] = None,
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max_proposals: int = 5,
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) -> List[Dict[str, Any]]:
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"""
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Generate GitOps-ready rule proposals.
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Args:
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threat_report: ThreatIntelReport to use
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max_proposals: Maximum proposals to generate
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Returns:
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List of proposal dicts ready for MR creation
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"""
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proposals: List[Dict[str, Any]] = []
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if not threat_report:
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return proposals
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try:
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# Import proposer dynamically
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from gitops.waf_rule_proposer import WAFRuleProposer
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proposer = WAFRuleProposer(workspace_path=str(self.workspace))
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batch = proposer.generate_proposals(
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threat_report=threat_report,
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max_proposals=max_proposals,
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)
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for proposal in batch.proposals:
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proposals.append({
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"name": proposal.rule_name,
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"type": proposal.rule_type,
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"severity": proposal.severity,
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"confidence": proposal.confidence,
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"terraform": proposal.terraform_code,
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"justification": proposal.justification,
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"auto_deploy": proposal.auto_deploy_eligible,
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})
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except ImportError:
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pass
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return proposals
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@property
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def capabilities(self) -> Dict[str, bool]:
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"""Report available capabilities."""
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return {
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"core_analysis": True,
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"rule_generation": True,
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"compliance_mapping": True,
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"threat_intel": self.threat_intel is not None,
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"ml_classification": self.classifier is not None,
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}
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