The Reality of Security Logs

Enterprise systems generate massive amounts of logs every day. Logs from servers, network devices, applications, and security appliances can reach thousands to hundreds of thousands of entries. The problem is that most of these logs accumulate but are never read.

For small and mid-sized companies with limited security staff, there's no one to analyze logs in real time, and even reviewing accumulated logs after the fact is overwhelming. When an attack occurs, signals in the logs are often missed.

Limitations of Traditional SIEM

Traditional SIEM (Security Information and Event Management) is rule-based. It works by pre-defining rules like "alert when this pattern appears."

The limitations of this approach are clear:

  • Rule creation burden: New attack patterns require continuous rule creation
  • Unknown attack detection: Undefined patterns cannot be detected
  • Alert fatigue: Meaningless warnings drown out actual threats
  • Lack of context: Hard to identify situations where individual events look normal but the combination is anomalous

What AI Does in Log Analysis

AI-based security monitoring shifts from a "match against rules" approach to a "distinguish normal from abnormal" approach.

1. Real-Time Log Analysis

Logs collected from various sources (Syslog, Webhook, files, etc.) are processed in real time. The AI model understands log semantics and identifies anomalies at the intent level, going beyond simple string matching.

2. Threat Situation Summarization

Instead of "2 out of 3,000 logs are abnormal," it summarizes "what attack was attempted, which systems were affected, and how far it progressed" in a human-readable format. This significantly reduces the time and effort security staff need for decision-making.

3. Context-Based Judgment

Rather than looking at single logs, it examines the temporal flow of logs. Identifying threats in sequences like login failure → privilege escalation attempt → file access is the core.

4. Policy-Based Active Response

Based on analysis results, it executes responses within pre-defined policy boundaries. For example, the AI can propose or execute actions like IP blocking or account lockout after approval. The key is that it's designed to operate only within authorized permission boundaries.

Considerations for AI Security Monitoring Deployment

Data collection scope: Which systems' logs to collect determines analysis quality. Starting with critical assets (servers, network, authentication systems) is practical.

False positive management: AI also produces false alarms. Initially, include a human verification step and improve accuracy through feedback.

Response permission design: Distinguish between actions the AI can execute directly and those requiring human approval. The scope of automated response should be expanded gradually according to the organization's risk tolerance.

Operational integration: Analysis results must connect to existing security operations procedures (incident response manuals) to lead to actual response.

LogOnAI's Flow

Noonjoy's AI security solution LogOnAI operates with the following flow:

  1. Collection: Log collection from various channels including Syslog and Webhook
  2. Classification: Noise removal through security rule-based filtering
  3. Analysis: AI identifies anomalies and threat scenarios
  4. Summarization: Organizes attack situation and impact scope in a human-readable format
  5. Response: Policy-based action proposal/execution, result logging

If you want to introduce AI for log analysis and security monitoring, check the LogOnAI service page for details or contact us to share your current security operations status.