Building Trust at Scale

The Next Generation of Audit Logging at Palantir

Palantir
17 min readadvanced
--
View Original

Overview

The article discusses Palantir's commitment to building trust at scale through enhanced audit logging capabilities. It details the evolution of their audit logging system to audit.3, which improves the speed, structure, and accessibility of audit logs, thereby reinforcing accountability and transparency for organizations handling sensitive data.

What You'll Learn

1

How to implement a next-generation audit logging system using audit.3

2

Why structured audit logs enhance security and compliance efforts

3

When to transition from batch processing to streaming data architectures

Prerequisites & Requirements

  • Understanding of audit logging principles and data security
  • Familiarity with Security Information and Event Management (SIEM) systems(optional)

Key Questions Answered

What are the key features of the audit.3 logging system?
The audit.3 logging system introduces a new schema that categorizes audit events, improves log delivery speed, and enhances accessibility. It allows for near real-time log availability and standardizes logging practices across Palantir products, making it easier for security teams to monitor and analyze user actions.
How does audit.3 improve upon the previous audit.2 system?
Audit.3 addresses the limitations of audit.2 by eliminating batch processing delays and introducing a structured logging schema. This redesign allows for immediate access to logs, reduces complexity, and ensures consistent event categorization, thereby enhancing the efficiency of security investigations.
What challenges did Palantir face in audit logging at scale?
Palantir faced challenges such as processing hundreds of terabytes of audit logs daily and the limitations of the previous audit.2 system, which relied on batch processing and had an inconsistent log schema. These issues hindered timely access to audit data and made it difficult for analysts to perform consistent queries.
How does the new telemetry collection streaming pipeline work?
The telemetry collection streaming pipeline is a distributed system of agents that capture audit logs in real-time, routing them efficiently to storage. This architecture allows logs to be available within minutes, transforming them from retrospective analysis tools to real-time operational resources for security teams.

Key Statistics & Figures

Audit log volume
Hundreds of terabytes per day
This volume underscores the scale at which Palantir operates and the need for efficient logging systems.

Technologies & Tools

Logging
Audit.3
Next-generation audit logging system designed to enhance speed, structure, and accessibility of audit logs.
Infrastructure
Telemetry Collection Streaming Pipeline
Distributed system for real-time log collection and processing.

Key Actionable Insights

1
Implementing the audit.3 logging system can significantly enhance your organization's security posture.
By adopting the structured categories and real-time logging capabilities of audit.3, organizations can streamline their security investigations and ensure compliance with regulatory requirements more effectively.
2
Transitioning to a streaming data architecture is crucial for modern security operations.
As organizations increasingly rely on real-time data for decision-making, moving away from batch processing will improve the responsiveness and effectiveness of security teams.
3
Standardizing audit log categories simplifies compliance monitoring.
With a consistent schema, compliance officers can easily track and analyze user actions across different products without needing to adjust their queries for new features.

Common Pitfalls

1
Failing to standardize log event structures can lead to confusion and inefficiencies in security investigations.
Without a consistent schema, analysts may struggle to compile comprehensive queries, resulting in missed insights and delayed responses to security incidents.

Related Concepts

Audit Logging Best Practices
Data Security Frameworks
Real-time Data Processing