Detecting Insider Data Exfiltration Via Dlp

Detecting Insider Data Exfiltration Via Dlp

Overview

Cybersecurity skill for detecting insider data exfiltration via dlp. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting insider data exfiltration via dlp"

• "When investigating security incidents that require detecting insider data exfilt"

• "When building detection rules or threat hunting queries for this domain"

• "When SOC analysts need structured procedures for this analysis type"

• When investigating security incidents that require detecting insider data exfiltration via dlp

• When building detection rules or threat hunting queries for this domain

• When SOC analysts need structured procedures for this analysis type

• When validating security monitoring coverage for related attack techniques

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• Familiarity with security operations concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Define Detection Scope — Identify the specific insider data exfiltration techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for insider data exfiltration.
3. Build Detection Queries — Write dlp queries targeting insider data exfiltration indicators. Use platform-specific query language for optimal performance.
4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Tools

• dlp — Primary tool for this skill
• SIEM Platform — Central log aggregation and query execution
• Sigma Rules — Vendor-agnostic detection rule format
• MITRE ATT&CK Navigator — Technique mapping and coverage analysis

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All insider data exfiltration procedures executed completely and documented
• Findings validated against multiple data sources
• False positives identified and filtered
• Results documented with evidence and timestamps
• Recommendations provided with risk-based prioritization

Anti-Rationalization Table