README.md

SAFE-M-32: Continuous Vector Store Monitoring

Overview

Mitigation ID: SAFE-M-32
Category: Detective Control
Effectiveness: Medium-High
Implementation Complexity: Medium
First Published: 2025-09-13

Description

Continuous Vector Store Monitoring is a detective control that provides real-time monitoring and alerting for suspicious activities in vector databases. This mitigation detects potential contamination attempts by monitoring embedding patterns, access patterns, and content anomalies.

Mitigates

• SAFE-T2106: Context Memory Poisoning via Vector Store Contamination
• SAFE-T1702: Shared-Memory Poisoning
• SAFE-T1805: Context Snapshot Capture

Technical Implementation

Core Principles

1. Real-time Monitoring: Continuous observation of vector store operations
2. Anomaly Detection: Identify unusual patterns and behaviors
3. Alerting: Immediate notification of suspicious activities
4. Forensics: Complete audit trail for investigation

Implementation Examples

class VectorStoreMonitor:
    def __init__(self):
        self.anomaly_detector = AnomalyDetector()
        self.alert_system = AlertSystem()
        self.recovery_system = RecoverySystem()
    
    def monitor_insertions(self, embedding: np.ndarray, metadata: dict):
        """Monitor embedding insertions for anomalies"""
        if self.anomaly_detector.detect_anomaly(embedding, metadata):
            self.alert_system.alert("Suspicious embedding insertion detected")
            self.recovery_system.quarantine_embedding(embedding)
    
    def monitor_queries(self, query: str, results: List[str]):
        """Monitor query results for suspicious patterns"""
        if self.anomaly_detector.detect_result_anomaly(query, results):
            self.alert_system.alert("Suspicious query results detected")
            self.log_suspicious_activity(query, results)

Benefits

• Early Detection: Identifies attacks before they cause damage
• Real-time Response: Immediate action on suspicious activities
• Forensic Capability: Complete audit trail for investigation
• Compliance: Meets monitoring and logging requirements

Limitations

• False Positives: May generate alerts for legitimate activities
• Resource Usage: Monitoring adds overhead to operations
• Maintenance: Requires regular tuning and updates

Version History

Version	Date	Changes	Author
1.0	2025-09-13	Initial documentation	Sachin Keswani