breach-detection-system

name	breach-detection-system
description	Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"data-breach-response","tags":"breach-detection, siem, dlp, anomaly-detection, insider-threat, classification"}

Implementing Breach Detection System

Overview

Effective breach detection is the prerequisite for timely Art. 33 notification. The GDPR does not prescribe specific detection technologies, but Art. 32 requires appropriate technical and organisational measures, and Art. 33(1) creates a de facto obligation to detect breaches promptly — a controller cannot notify within 72 hours if it takes months to discover a breach. This skill covers the technical architecture for personal data breach detection, including SIEM integration, DLP alerting, behavioral analytics, and insider threat monitoring.

Breach Classification Taxonomy

Level 1: CIA Triad Classification

Type	Definition	Detection Method
Confidentiality	Unauthorized disclosure or access to personal data	DLP alerts, access log anomalies, data exfiltration detection
Integrity	Unauthorized modification of personal data	File integrity monitoring, database audit logs, checksum validation
Availability	Loss of access to or destruction of personal data	System health monitoring, backup verification, ransomware detection

Level 2: Attack Vector Classification

Vector	Description	Primary Detection
External attack	Unauthorized access from outside the network perimeter	IDS/IPS, firewall logs, authentication failures
Insider threat	Authorized user acting beyond their authorized scope	UEBA, DLP, access pattern analysis
Third-party compromise	Breach originating from a processor or vendor	Vendor monitoring, API anomaly detection
Accidental disclosure	Human error leading to data exposure	DLP content inspection, email gateway filters
System failure	Technical failure causing data loss or corruption	Infrastructure monitoring, backup validation
Physical breach	Loss or theft of physical devices containing data	Asset tracking, device encryption verification

Level 3: Data Sensitivity Classification

Level	Data Types	Detection Priority
Critical	Art. 9 special categories, Art. 10 criminal data, financial credentials	Real-time alerting, immediate escalation
High	Government identifiers, financial account data, authentication credentials	Real-time alerting, 15-minute escalation
Medium	Contact details, employment records, purchase history	Near-real-time (5-minute batching), 1-hour escalation
Low	Publicly available business contact data, non-personal metadata	Batch processing (hourly), daily review

SIEM Integration Architecture

Event Source Configuration

Stellar Payments Group deploys Splunk Enterprise Security as the primary SIEM platform. The following log sources are ingested for breach detection:

Source	Log Type	Ingestion Method	Events/Day
Active Directory	Authentication, authorization, group changes	Splunk Universal Forwarder	2.4M
AWS CloudTrail	API calls, console logins, resource changes	S3-to-Splunk via SQS	8.7M
PostgreSQL audit	Query logs, schema changes, failed authentications	syslog-ng to Splunk HEC	14.2M
Palo Alto PA-5260	Firewall sessions, URL filtering, threat prevention	syslog to Splunk	45.3M
CrowdStrike Falcon	Endpoint detections, process execution, file writes	Falcon SIEM Connector	6.1M
Microsoft 365	Email audit, SharePoint access, Teams DLP	Microsoft Graph API via Splunk Add-on	3.8M
Okta	SSO authentications, MFA events, admin changes	Okta Event Hook to Splunk HEC	890K
AWS GuardDuty	Threat intelligence findings, anomaly detections	CloudWatch to Splunk	12K

Correlation Rules for Breach Detection

Rule 1: Mass Data Access Anomaly

Trigger: Single user account accesses more than 500 unique personal data records within a 30-minute window.
Data sources: PostgreSQL audit logs, application access logs.
Severity: High
Action: Create incident ticket, alert SOC analyst, notify DPO on-call.
False positive mitigation: Whitelist batch processing service accounts (reviewed quarterly). Flag whitelisted accounts if access occurs outside scheduled batch windows.

Rule 2: Data Exfiltration Indicator

Trigger: Outbound data transfer exceeding 50MB to an unclassified external destination from a system containing personal data.
Data sources: Palo Alto firewall, CrowdStrike network telemetry, DLP alerts.
Severity: Critical
Action: Automated network isolation of source endpoint, SOC analyst investigation, DPO notification.
False positive mitigation: Baseline normal data transfer patterns per endpoint. Whitelist approved SaaS destinations (Salesforce, Workday, etc.).

Rule 3: Privileged Account Anomaly

Trigger: Database administrator account performs SELECT queries on personal data tables outside of change management windows OR from an unrecognized source IP.
Data sources: PostgreSQL audit logs, Okta authentication logs, IP geolocation.
Severity: High
Action: Alert SOC lead and database security team. Log full query text for forensic review.

Rule 4: Ransomware Behavior Pattern

Trigger: More than 20 file rename/encrypt operations per second on file servers or database storage volumes, OR known ransomware file extension creation (.lockbit, .encrypted, .crypt).
Data sources: CrowdStrike Falcon, Windows file audit logs, storage IOPS monitoring.
Severity: Critical
Action: Automated isolation of affected system, activate incident response team, preserve forensic image.

Rule 5: Authentication Brute Force

Trigger: More than 50 failed authentication attempts against personal data systems within 10 minutes from a single source, OR more than 200 failed attempts across multiple accounts from the same source within 30 minutes (password spray).
Data sources: Active Directory, Okta, application authentication logs.
Severity: Medium (escalates to High if followed by successful authentication).
Action: Block source IP at firewall, SOC investigation, check for compromised credentials.

DLP Alert Configuration

Email DLP Policies

Policy Name	Detection Content	Action	Severity
PII Outbound	Regex: German ID (Personalausweisnummer), IBAN, credit card numbers, health insurance numbers	Block + encrypt + alert DPO	High
Bulk PII	More than 10 rows of structured personal data (name + email/phone/address) in email body or attachment	Block + alert sender manager + SOC	Critical
Special Category	Keywords/patterns matching health diagnoses, genetic markers, biometric templates, trade union references	Block + alert DPO	Critical
Cross-Border Transfer	Personal data sent to recipients in non-adequate countries without approved transfer mechanism	Hold for review + alert privacy team	Medium

Endpoint DLP Policies

Policy Name	Detection Content	Action
USB Copy PII	Copy of files containing personal data to removable media	Block (exceptions via DPO-approved ticket)
Cloud Upload PII	Upload of personal data files to non-approved cloud services	Block + alert SOC
Print PII Bulk	Print job containing more than 50 records of personal data	Alert line manager + SOC
Screenshot PII	Screen capture of application displaying personal data records	Log + alert SOC (for pattern analysis)

Anomaly Detection and UEBA

Behavioral Baselines

User and Entity Behavior Analytics (UEBA) establishes normal patterns for each user and system account:

Baseline Metric	Normal Range	Anomaly Threshold	Detection Window
Daily record access count	Per-user historical average	3x standard deviation above mean	Rolling 30-day baseline
Access time patterns	Historical working hours	Access outside 95th percentile time range	Rolling 90-day baseline
Data download volume	Per-user historical average	2x standard deviation above mean for 2+ consecutive hours	Rolling 14-day baseline
Geographic access location	Historical IP geolocation	New country not seen in 90-day history	Rolling 90-day baseline
Application access pattern	Historical application mix	Access to new personal data application not in 60-day history	Rolling 60-day baseline

Insider Threat Indicators

Composite risk scoring for insider threat detection combines multiple low-severity indicators:

Indicator	Weight	Source
Access outside normal hours to personal data systems	15	Okta + application logs
Bulk data download from HR or customer databases	25	DLP + database audit
Use of personal email or cloud storage from corporate device	10	CrowdStrike + proxy logs
Notice period or performance improvement plan status	20	HR system integration (Workday)
Access to data outside role scope	20	Role-based access comparison
Disabling or circumventing security controls	30	Endpoint agent tampering alerts
Repeated failed access to restricted personal data	10	Application access logs

Composite scores above 60 trigger enhanced monitoring. Scores above 80 trigger SOC analyst investigation and DPO notification.

Integration with Privacy Incident Response

Automated Workflow

1. SIEM correlation rule or DLP policy triggers an alert.
2. Alert is enriched with contextual data: user identity, data classification of affected system, data subject count estimate.
3. If the affected system is classified as containing personal data (tagged in the CMDB), the alert is automatically duplicated to the privacy incident queue in ServiceNow.
4. Privacy incident coordinator performs initial triage within 30 minutes.
5. If personal data breach is confirmed, the Art. 33 72-hour clock activation is triggered and the DPO is notified.

Detection-to-Notification Timeline Target

Phase	Target	Owner
Detection to alert	Under 5 minutes	SIEM platform (automated)
Alert to triage	Under 30 minutes	SOC analyst
Triage to breach confirmation	Under 4 hours	SOC lead + privacy coordinator
Breach confirmation to DPO notification	Under 1 hour	Privacy incident coordinator
DPO notification to 72-hour clock start	Immediate	DPO

Monitoring and Tuning

Monthly Review Cadence

1. Review false positive rates for all breach detection correlation rules. Target: under 15% false positive rate.
2. Analyze detection gap assessments against known breach scenarios from EDPB Guidelines 01/2021.
3. Update behavioral baselines following organizational changes (new hires, departures, role changes).
4. Validate that all new personal data systems have been onboarded to the SIEM and DLP platforms.
5. Test end-to-end detection-to-notification workflow with a simulated breach scenario quarterly.