Palo Alto Parser

The Palo Alto Parser in Observo AI allows users to parse the incoming Palo Alto event, extracting and structuring various native log types for streamlined analysis.

Purpose

The Palo Alto parser is designed to extract, transform, and standardize raw log data from Palo Alto Networks devices—such as firewalls and threat prevention systems—so it can be efficiently ingested into data pipelines. It parses complex, device-specific log formats to isolate critical information such as source/destination IPs, actions, threat signatures, and timestamps, ensuring that the data is in a consistent, SIEM-friendly structure. This standardized output not only enables advanced analytics and machine learning for pattern recognition and sentiment analysis but also supports automation and real-time threat detection across your security infrastructure.

Usage

Select Syslog Parser transform. Add Name (required) and Description (optional).

General Configuration:

• Bypass Transform: Defaults to disable. When enabled, this transform will be bypassed entirely, allowing the event to pass through without any modifications.

• Add Filter Conditions: Defaults to disable. When enabled, it allows events to filter through conditions. Only events that meet the true condition will be processed; all others will bypass this transform. Based on AND/OR conditions, "+Rule" or "+Group" buttons.

Palo Alto Parser: Enabled: Defaults to enabled, meaning it does evaluate all events. Toggle Enabled off to prevent event processing to feed data to the downstream Transforms.

PaloAlto Log Type: Select Default.

Field Name: Field name on whose value the PaloAlto parser will apply. Default: message.

New Field Name: Specify the name of the destination field for storing the parsed data. Default: palo_alto.

Advanced:

• Metadata Field Name: Specify the path which will be used for storing auxiliary data. This data will be used by PaloAltoSerializer later on. Default: _ob.palo_alto.

Examples

Restructure Palo Alto Raw Log to comprise Header/Body

Scenario: Create a separate header and body from a Palo Alto raw event.

Palo Alto Parser

PaloAlto Log Type	Field Name	New Field Name	Metadata Field Name
Default	message	palo_alto_data	palo_alto_metadata

Input

{
  "message": "<14>Jun 15 14:30:00 PA-FW 1,2023/06/15 14:30:00,001234567890,TRAFFIC,start,2023/06/15 14:29:59,10.1.1.1,8.8.8.8,192.168.1.100,8.8.8.8,Allow_Outbound,Trust,Untrust,tcp,443,443,443,2048,4,2023/06/15 14:29:58,2,any,allow"
}

Output

{
  "timestamp": "2023-06-15T14:30:00Z",
  "source_ip": "192.168.1.100",
  "palo_alto_metadata": {
    "log_type": "TRAFFIC",
    "syslog_prefix": "<14>Jun 15 14:30:00 PA-FW"
  },
  "palo_alto_data": {
    "receive_time": "2023/06/15 14:30:00",
    "serial": "001234567890",
    "type": "TRAFFIC",
    "subtype": "start",
    "time_generated": "2023/06/15 14:29:59",
    "src": "10.1.1.1",
    "dst": "8.8.8.8",
    "natsrc": "192.168.1.100",
    "natdst": "8.8.8.8",
    "rule": "Allow_Outbound",
    "from": "Trust",
    "to": "Untrust",
    "proto": "tcp",
    "port": 443,
    "bytes": 2048,
    "packets": 4,
    "start": "2023/06/15 14:29:58",
    "elapsed": 2,
    "category": "any",
    "action": "allow"
  }
}

Palo Alto Parser Best Practices

Implementing a Palo Alto parser within the Observo AI pipeline for SIEM integration involves several best practices to ensure efficient data processing and enhanced security monitoring:

1. Structured Parsing & Normalization

   • Consistent Field Mapping: Map extracted fields to SIEM-compatible schemas, ensuring uniformity across different data sources.

   • Data Type Standardization: Convert fields like timestamps and IP addresses into standardized formats to facilitate seamless integration and analysis.

2. Advanced Machine Learning & Pattern Recognition

   • Anomaly Detection: Utilize Observo AI's ML models to identify deviations from established patterns, enabling early detection of potential security threats.

   • Behavioral Analysis: Analyze user and entity behaviors to detect unusual activities that may indicate compromised accounts or insider threats.

3. Data Enrichment & Summarization

   • Contextual Augmentation: Enhance logs with additional context such as geolocation data, threat intelligence feeds, and user roles to provide a comprehensive view of security events.

   • Log Summarization: Aggregate similar log entries to reduce data volume and highlight significant events, improving SIEM performance and reducing alert fatigue.

4. Efficient SIEM Integration & Automation

   • Optimized Data Routing: Implement smart routing mechanisms to direct relevant parsed data to appropriate SIEM modules, ensuring efficient resource utilization.

   • Automated Response Triggers: Set up automated actions within the SIEM based on parsed data insights, such as initiating incident response protocols or adjusting security policies.

5. Continuous Learning & Adaptation

   • Feedback Loops: Incorporate mechanisms for the parser to learn from false positives/negatives, refining its accuracy over time.

   • Regular Updates: Stay informed about updates in Palo Alto log formats and Observo AI capabilities to ensure the parser remains effective and aligned with current technologies.

By adhering to these best practices, organizations can enhance their security posture, streamline log management, and optimize the performance of their SIEM systems through the effective use of Observo AI's Palo Alto parser.

Related Functions

• AWS VPC Flow Logs Parse: Parse AWS VPC Flow Logs to JSON, extracting and structuring the logs for streamlined analysis.

• AWS ALB (Application Load Balancer) Parser : Extract, normalize, and structure log data from ALB access logs