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Security Monitoring

Overview​

The Security Monitoring system provides real-time visibility into security threats, attack patterns, and system performance. This comprehensive monitoring solution helps security teams detect, analyze, and respond to threats effectively.

Security Dashboard​

Real-Time Metrics​

The security dashboard displays key performance indicators:

  • Total Queries: Number of queries processed
  • Blocked Queries: Number of queries blocked by security
  • Block Rate: Percentage of queries blocked
  • Injection Attempts: Number of injection attacks detected
  • False Positives: Legitimate queries incorrectly blocked

Threat Distribution​

Visual representation of threat levels:

  • Safe: No security concerns
  • Low: Minor security patterns
  • Medium: Moderate threats
  • High: Significant threats
  • Critical: Severe threats requiring immediate action

Attack Pattern Analysis​

Identification of common attack patterns:

  • Direct injection attempts
  • Role manipulation attacks
  • Context poisoning attempts
  • Jailbreak attempts
  • Social engineering attacks

Monitoring Components​

1. Security Event Logging​

from packages.rag.input_sanitization import SecurityEvent

# Log security event
event = SecurityEvent(
    timestamp=time.time(),
    user_id="user123",
    session_id="session456",
    query="malicious query",
    threat_level=ThreatLevel.HIGH,
    injection_type=InjectionType.DIRECT_INJECTION,
    confidence=0.85,
    detected_patterns=["ignore instructions"],
    sanitized_query="sanitized query",
    action_taken="BLOCK_QUERY",
    risk_score=0.5
)

monitor.log_event(event)

2. User Risk Scoring​

# Get user risk score
risk_score = monitor.get_user_risk_score("user123")

# Update risk score based on behavior
if threat_detected:
    monitor.update_user_risk_score("user123", risk_increment)

3. Attack Pattern Detection​

# Analyze attack patterns
patterns = monitor.get_attack_patterns()

# Get trending patterns
trending = monitor.get_trending_patterns(hours=24)

Alert System​

Alert Types​

High-Risk Users​

if len(high_risk_users) > threshold:
    create_alert(
        severity="medium",
        title="Multiple High-Risk Users Detected",
        description=f"Found {len(high_risk_users)} users with elevated risk scores"
    )

Injection Attempt Spikes​

if injection_attempts > threshold:
    create_alert(
        severity="high",
        title="Injection Attempt Spike Detected",
        description=f"Detected {injection_attempts} injection attempts in the last hour"
    )

Critical Threats​

if critical_threats > threshold:
    create_alert(
        severity="critical",
        title="Critical Security Threats Detected",
        description=f"Detected {critical_threats} critical threats in the last hour"
    )

Alert Configuration​

# Configure alert thresholds
alert_thresholds = {
    'high_risk_users': 5,
    'injection_attempts_per_hour': 10,
    'critical_threats_per_hour': 3,
    'false_positive_rate': 0.1
}

Notification Channels​

  • Email: Direct email notifications
  • Webhook: HTTP webhook notifications
  • Slack: Slack channel notifications
  • Dashboard: Real-time dashboard alerts

Metrics and Analytics​

Key Performance Indicators​

Detection Accuracy​

def calculate_detection_accuracy():
    total_queries = get_total_queries()
    correctly_detected = get_correctly_detected()
    return correctly_detected / total_queries

False Positive Rate​

def calculate_false_positive_rate():
    total_blocked = get_total_blocked()
    false_positives = get_false_positives()
    return false_positives / total_blocked

Response Time​

def calculate_response_time():
    response_times = get_response_times()
    return statistics.mean(response_times)

Trend Analysis​

def analyze_query_volume_trends():
    hourly_data = get_hourly_query_data(days=7)
    return calculate_trend(hourly_data)

Attack Pattern Evolution​

def analyze_attack_evolution():
    patterns = get_attack_patterns_by_time()
    return identify_emerging_patterns(patterns)

User Behavior Analysis​

def analyze_user_behavior():
    user_activities = get_user_activities()
    return identify_anomalous_behavior(user_activities)

Dashboard Features​

Real-Time Updates​

The dashboard automatically refreshes every 30 seconds to show:

  • Current security metrics
  • Recent security events
  • Active alerts
  • User risk scores

Interactive Charts​

Query Volume Chart​

  • Total queries over time
  • Blocked queries over time
  • Trend analysis

Threat Distribution Pie Chart​

  • Distribution of threat levels
  • Percentage breakdown
  • Color-coded segments

Attack Patterns Bar Chart​

  • Top attack patterns
  • Pattern frequency
  • Trend indicators
  • Filter by time range
  • Filter by user ID
  • Filter by threat level
  • Search specific patterns

Monitoring Best Practices​

1. Continuous Monitoring​

  • Monitor 24/7 for security events
  • Set up automated alerts
  • Regular review of metrics
  • Proactive threat hunting

2. Performance Optimization​

  • Monitor response times
  • Track resource usage
  • Optimize detection rules
  • Scale as needed

3. Data Retention​

  • Retain security logs for compliance
  • Archive historical data
  • Implement data lifecycle management
  • Ensure data privacy

4. Alert Tuning​

  • Adjust alert thresholds
  • Reduce false positives
  • Implement alert correlation
  • Regular alert review

Integration​

SIEM Integration​

def send_to_siem(event):
    siem_data = {
        'timestamp': event.timestamp,
        'user_id': event.user_id,
        'threat_level': event.threat_level.value,
        'query': event.query,
        'action_taken': event.action_taken
    }
    
    siem_client.send_event(siem_data)

Log Aggregation​

def aggregate_logs():
    logs = get_security_logs()
    aggregated = aggregate_by_pattern(logs)
    return aggregated

API Integration​

# Get dashboard data via API
@app.route('/api/dashboard-data')
def get_dashboard_data():
    return jsonify(security_system.get_security_dashboard_data())

# Get security charts via API
@app.route('/api/security-charts')
def get_security_charts():
    return jsonify(dashboard.get_security_charts())

Troubleshooting​

Common Issues​

High False Positive Rate​

  • Adjust detection thresholds
  • Refine detection patterns
  • Implement user feedback
  • Retrain ML models

Missing Threats​

  • Lower detection thresholds
  • Add new detection patterns
  • Update ML models
  • Implement behavioral analysis

Performance Issues​

  • Optimize detection rules
  • Implement caching
  • Use faster inference
  • Scale resources

Alert Fatigue​

  • Tune alert thresholds
  • Implement alert correlation
  • Use alert prioritization
  • Regular alert review

Monitoring Health​

System Health Checks​

def check_system_health():
    checks = {
        'detection_accuracy': check_detection_accuracy(),
        'response_time': check_response_time(),
        'false_positive_rate': check_false_positive_rate(),
        'system_uptime': check_system_uptime()
    }
    
    return all(checks.values())

Performance Monitoring​

def monitor_performance():
    metrics = {
        'cpu_usage': get_cpu_usage(),
        'memory_usage': get_memory_usage(),
        'disk_usage': get_disk_usage(),
        'network_usage': get_network_usage()
    }
    
    return metrics

Compliance and Reporting​

Security Reports​

Generate comprehensive security reports:

  • Executive summary
  • Threat analysis
  • Performance metrics
  • Recommendations

Compliance Monitoring​

Track compliance with:

  • GDPR requirements
  • SOC 2 controls
  • ISO 27001 standards
  • Industry regulations

Audit Trail​

Maintain detailed audit trails:

  • All security events
  • User actions
  • System changes
  • Access logs