Security Monitoring & SIEM: Complete Implementation Guide

Organizations with mature SIEM implementations detect breaches 87 days faster and reduce incident costs by 55%. Real-time security monitoring transforms reactive security into proactive threat hunting and rapid incident response.

SIEM Fundamentals

What is SIEM?

Security Information and Event Management combines:

• Security Information Management (SIM): Log aggregation, analysis, reporting
• Security Event Management (SEM): Real-time monitoring, correlation, alerting

Core Capabilities:

• Log collection and normalization
• Real-time event correlation
• Threat detection and alerting
• Incident investigation
• Compliance reporting
• Forensic analysis

SIEM Architecture

Data Collection Layer:

• Log forwarders/agents
• Syslog receivers
• API integrations
• Network traffic analysis
• Cloud service connectors

Processing Layer:

• Log parsing and normalization
• Event enrichment
• Correlation engine
• Machine learning analytics
• Threat intelligence integration

Storage Layer:

• Hot storage (recent data, fast access)
• Warm storage (30-90 days)
• Cold storage (long-term retention)
• Archive (compliance requirements)

Presentation Layer:

• Dashboards and visualizations
• Alert management
• Investigation workspace
• Reporting engine
• API access

Log Source Integration

Essential Log Sources

Infrastructure:

• Firewalls (allowed/blocked connections)
• IDS/IPS (intrusion attempts)
• VPN concentrators (remote access)
• Load balancers (traffic patterns)
• Routers/switches (network flow)

Endpoints:

• Windows Event Logs
• Linux/Unix syslogs
• MacOS logs
• Endpoint protection
• Application logs

Identity & Access:

• Active Directory
• LDAP authentication
• Single Sign-On (SSO)
• Multi-Factor Authentication (MFA)
• Privileged Access Management (PAM)

Applications:

• Web servers (IIS, Apache, Nginx)
• Databases (SQL Server, Oracle, MySQL)
• Email servers (Exchange, Gmail)
• Business applications (CRM, ERP)
• Custom applications

Cloud Services:

• AWS CloudTrail
• Azure Activity Logs
• Google Cloud Audit Logs
• Office 365 logs
• SaaS application logs

Log Collection Best Practices

Volume Planning:

Estimate daily log volume:
- 1,000 endpoints × 50 KB/day = 50 MB
- 10 servers × 500 MB/day = 5 GB
- 5 firewalls × 2 GB/day = 10 GB
Total: ~15 GB/day = 5.5 TB/year

Add 30% growth buffer
Storage requirement: 7.2 TB/year

Log Forwarding:

• Use encrypted channels (TLS)
• Implement buffering (prevent loss)
• Configure failover destinations
• Monitor forwarder health
• Validate data integrity

Log Retention:

• Hot storage: 30-90 days
• Warm storage: 91-365 days
• Cold storage: 1-7 years (compliance)
• Define retention policies per source
• Automate archival processes

Use Cases & Detection Rules

Security Use Cases

Brute Force Detection:

Rule: Multiple Failed Login Attempts
Trigger: >5 failed logins from same IP within 5 minutes
Action: Alert SOC, block IP temporarily
Priority: High

Logic:
IF failed_login_count > 5
AND time_window = 5 minutes
AND source_ip = same
THEN generate_alert("Brute Force Detected")

Lateral Movement:

Rule: Unusual Internal Network Access
Trigger: Workstation accessing multiple servers
Logic:
IF source_type = "workstation"
AND destination_count > 10
AND time_window = 10 minutes
AND protocol IN (SMB, RDP, SSH)
THEN alert("Potential Lateral Movement")

Data Exfiltration:

Rule: Large Data Transfer to External IP
Trigger: >1GB transferred outbound from single host
Logic:
IF bytes_out > 1073741824
AND destination = external
AND time_window = 1 hour
THEN alert("Potential Data Exfiltration")

Privilege Escalation:

Rule: Account Added to Admin Group
Trigger: User added to privileged group
Logic:
IF event_id = 4728 (Windows)
AND group IN ("Domain Admins", "Enterprise Admins")
THEN alert("Privilege Escalation Detected")

Compliance Use Cases

PCI DSS:

• Access to cardholder data
• Administrative actions
• Security control changes
• Authentication events
• File integrity monitoring

HIPAA:

• PHI access tracking
• Unauthorized access attempts
• Data modification/deletion
• Audit log review
• Emergency access

SOC 2:

• System availability
• Access controls
• Change management
• Incident response
• Monitoring effectiveness

Threat Detection

Correlation Rules

Rule Types:

Simple Match: Single event triggers alert

Failed SSH login from blacklisted IP

Threshold: Event count exceeds limit

>100 login attempts in 1 minute

Time-Based: Events within time window

Login from NYC at 9am, then London at 9:05am (impossible travel)

Sequence: Events in specific order

1. VPN connection
2. Privilege escalation
3. Database access
4. Large file transfer

Statistical: Deviation from baseline

User typically accesses 5 files/day
Today accessed 500 files (anomaly)

Machine Learning Detection

Anomaly Detection:

• User behavior analytics (UBA)
• Entity behavior analytics (UEBA)
• Peer group analysis
• Baseline deviation
• Time series analysis

ML Use Cases:

• Insider threat detection
• Account compromise
• Anomalous data access
• Unusual network traffic
• Zero-day malware

Model Training:

• Historical data (90+ days)
• Continuous learning
• False positive reduction
• Model validation
• Regular retraining

Threat Intelligence Integration

Intelligence Sources:

• Commercial threat feeds
• Open-source intelligence (OSINT)
• Industry ISACs/ISAOs
• Government advisories
• Internal threat data

IOC Matching:

• Malicious IP addresses
• Known bad domains
• File hashes (malware)
• Attack patterns (TTPs)
• CVE exploits

Automated Enrichment:

Alert: Connection to suspicious IP
Enrich with:
- Geo-location
- Threat intelligence score
- Historical activity
- Related campaigns
- WHOIS information

Alert Management

Alert Tuning

Reduce False Positives:

• Whitelist known-good activity
• Adjust thresholds based on environment
• Add contextual filters
• Implement time-based rules
• Use exclusion lists

Alert Prioritization:

Critical: Confirmed breach, active exploitation
High: High confidence threat, privileged accounts
Medium: Suspicious activity, further investigation needed
Low: Policy violations, informational

Escalation Matrix:

Critical → Immediate (SOC + CISO + Incident Response)
High → 15 minutes (SOC Team Lead)
Medium → 1 hour (Tier 2 Analyst)
Low → 4 hours (Tier 1 Analyst)

Alert Workflow

Tier 1 (Triage):

• Initial alert review
• Gather context
• Check false positive indicators
• Escalate if needed
• Close benign alerts

Tier 2 (Investigation):

• Deep analysis
• Correlation with other events
• User/asset context
• Timeline reconstruction
• Escalate to Tier 3/IR

Tier 3 (Incident Response):

• Containment actions
• Forensic investigation
• Remediation
• Post-incident review
• Lessons learned

SIEM Platforms

Enterprise SIEM Solutions

Splunk:

• Powerful search language (SPL)
• Extensive app ecosystem
• Machine learning toolkit
• Scalable architecture
• High cost at scale

IBM QRadar:

• Strong correlation engine
• Integrated threat intelligence
• Risk-based prioritization
• Compliance reporting
• Complex deployment

Microsoft Sentinel:

• Cloud-native SIEM
• Azure integration
• AI-powered analytics
• Pay-as-you-go pricing
• Growing ecosystem

Elastic (ELK Stack):

• Open-source option
• Flexible and customizable
• Strong search capabilities
• Cost-effective
• Requires expertise

Cloud SIEM Options

Sumo Logic:

• Cloud-native
• Predictive analytics
• Easy deployment
• Good for cloud environments

Exabeam:

• UEBA focus
• Automated investigation
• Incident response
• Timeline visualization

LogRhythm:

• Integrated SOAR
• Case management
• Threat lifecycle management
• SmartResponse automation

Investigation & Forensics

Investigation Workflow

1. Alert Triage:

• Review alert details
• Check alert history
• Validate indicators
• Assess severity

2. Initial Analysis:

Questions to answer:
- What happened?
- When did it happen?
- Who was involved?
- What systems affected?
- Is it still active?

3. Data Collection:

• Related log events
• Network traffic captures
• Endpoint artifacts
• User activity timeline
• System configurations

4. Timeline Construction:

09:15 - Phishing email received
09:17 - User clicked link
09:18 - Malware downloaded
09:20 - C2 connection established
09:25 - Lateral movement began
09:30 - Data accessed

5. Impact Assessment:

• Systems compromised
• Data accessed/exfiltrated
• Business impact
• Regulatory implications
• Remediation required

Search & Analysis

Common Search Patterns:

Failed Login Analysis:

source="authentication"
event="failed_login"
| stats count by user, src_ip
| where count > 5

Malware Detection:

source="endpoint"
action="file_write"
path="C:\\Users\\*\\AppData\\*"
| lookup threat_intel hash
| where threat_score > 70

Data Exfiltration:

source="firewall"
action="allowed"
dest_port IN (21, 22, 443)
bytes_out > 1000000000
| stats sum(bytes_out) by src_ip, dest_ip

Metrics & Reporting

Security Metrics

Operational Metrics:

• Mean Time To Detect (MTTD): <24 hours
• Mean Time To Respond (MTTR): <4 hours
• Alert volume (daily/weekly)
• False positive rate: <10%
• Investigation closure rate

Effectiveness Metrics:

• Threats detected
• Incidents prevented
• Compliance violations identified
• Coverage percentage (log sources)
• Detection rule effectiveness

Compliance Reporting:

• Access reports (who accessed what)
• Change audits (system modifications)
• Authentication reports (login activity)
• Administrative actions
• Policy violations

Dashboard Design

SOC Analyst Dashboard:

• Open high/critical alerts
• Alert queue by severity
• Recent incident timeline
• Top sources/destinations
• Failed authentication attempts

Executive Dashboard:

• Security posture score
• Incident trends
• Compliance status
• Risk indicators
• Budget/resource utilization

Compliance Dashboard:

• Control effectiveness
• Audit findings
• Remediation status
• Policy violations
• Reporting calendar

Best Practices

Implementation:

• Start with high-value use cases
• Integrate critical log sources first
• Define clear alert criteria
• Establish baseline behavior
• Continuous tuning

Operations:

• 24/7 monitoring coverage
• Regular rule reviews
• Playbook development
• Team training
• Incident drills

Optimization:

• Tune false positives aggressively
• Archive unused data
• Optimize storage costs
• Automate common tasks
• Regular performance reviews

Getting Started

Month 1: Foundation

• Select SIEM platform
• Deploy infrastructure
• Integrate top 10 log sources
• Create initial dashboards
• Basic alert rules

Month 2: Detection

• Expand log source coverage
• Develop use cases
• Build correlation rules
• Integrate threat intelligence
• Train SOC team

Month 3: Optimization

• Tune alert accuracy
• Automate responses
• Develop playbooks
• Implement UEBA
• Measure effectiveness

Conclusion

Security monitoring and SIEM provide essential visibility into security events, enable rapid threat detection, and support effective incident response. Successful implementations require careful planning, continuous tuning, and skilled analysts.

Start with clear use cases, integrate high-value log sources, and iteratively improve detection capabilities. Combine technology, process, and people for comprehensive security monitoring.

Next Steps:

1. Define monitoring requirements
2. Select SIEM platform
3. Prioritize log sources
4. Develop initial use cases
5. Deploy and iterate