Why AI and Osquery Struggle to Work Together

System monitoring tools like osquery give IT and security teams powerful insights through SQL-based queries, while AI promises to make complex technologies more accessible through natural language. Yet combining these technologies reveals a fundamental challenge: general AI struggles to generate the specialized SQL queries that make Osquery truly valuable for security and monitoring teams. This raises a critical question for organizations: Can AI be effectively tailored to make osquery more accessible without sacrificing its powerful capabilities?

What is osquery?

Osquery is an open-source security tool that turns operating system information into SQL tables, allowing users to monitor their systems with familiar database queries. Created by Meta (formerly Facebook), it works by exposing system data — processes, users, network connections, file integrity — as virtual database tables that can be queried with standard SQL commands. Instead of learning multiple platform-specific tools, teams can write a single SQL query that works across Windows, macOS, and Linux environments. This flexibility, combined with its ability to provide deep visibility into system behaviors, has led to widespread adoption across industries, making osquery a cornerstone tool for security teams and system administrators seeking comprehensive infrastructure monitoring.

The Benefits and Drawbacks of osquery

Osquery offers significant advantages through its use of SQL as a standardized query language, eliminating the need to learn multiple proprietary tools. Its cross-platform functionality means security teams can deploy consistent monitoring across Windows, Linux, and macOS environments with identical queries. The structured data osquery returns enables detailed system insights that support both operational monitoring and security investigations. However, these benefits come with meaningful challenges. Effective use requires SQL proficiency, creating a barrier for team members without database expertise. Beyond basic syntax, writing truly useful security and monitoring queries demands deep understanding of both SQL and system architecture. Finally, organizations struggle with query management at scale — deploying, updating, and maintaining complex queries across thousands of endpoints becomes a significant operational burden that limits osquery’s practical value despite its technical capabilities.

The Role of AI and LLMs

Large Language Models (LLMs) are sophisticated AI systems trained on vast text datasets, enabling them to understand and generate human language with remarkable accuracy. These models excel at pattern recognition, contextual understanding, and translating natural language into structured formats like code. In system monitoring, LLMs offer promising capabilities — they can generate SQL queries from plain English requests, help document existing queries, and potentially automate routine monitoring tasks.

The key insight is that AI’s role is not to autonomously determine what is “suspicious” or “unusual,” but to assist administrators in efficiently translating their specific, predefined monitoring requirements into precise SQL queries. For example, an admin might specify:

A list of approved Chrome extensions to check against installed extensions
Specific memory consumption thresholds for identifying resource-intensive processes
Defined port ranges or connection patterns that warrant investigation

Specialization is Key: Why AI Needs Context

General-purpose AI systems struggle with osquery because effective security and IT monitoring requires domain-specific expertise that goes beyond correct SQL syntax. While a general AI can write queries that run without errors, IT and security teams need queries that target relevant threats, consider system-specific nuances, and align with industry best practices. This gap points to the need for Artificial Specialization Intelligence (ASI) — AI systems specifically trained on security operations data and expert-crafted monitoring queries.

The Bigger Question: Enhancing Query Creation

The goal is to create an interface that makes query development more accessible while maintaining the precision required by IT and security professionals. Instead of vague prompts like “show me suspicious connections,” the approach should focus on enabling administrators to quickly translate their specific monitoring requirements into accurate, targeted queries.

An ideal system would allow an admin to specify:

Exact criteria for what constitutes an interesting network connection
Precise lists of approved software and configurations
Specific performance thresholds for system resources
Detailed compliance and security parameters

Why SQL Still Matters

Despite the appeal of simplified interfaces, SQL remains critically important in the osquery ecosystem for several practical reasons. First, customization is essential — IT and security teams often need to adjust queries for specific environments or emerging threats, requiring visibility into the underlying SQL. Second, transparency builds trust — professionals need to understand exactly what data is being collected and how results are being generated, especially when making critical decisions. Third, SQL’s structured nature enables efficient scaling across thousands of endpoints, with queries optimized for performance to avoid operational impact.

How aiquery Solves This Problem

aiquiry.io addresses these challenges by seamlessly integrating specialized AI with osquery in a unified platform. Rather than using general-purpose AI, aiquery employs Artificial Specialization Intelligence trained specifically on security operations and system monitoring use cases. This specialized approach ensures generated SQL queries aren’t just syntactically correct but provide a starting point for IT and security teams to refine and customize.

The platform recognizes that effective monitoring depends on the admin’s specific knowledge. It provides an interface where users can input their precise monitoring requirements, with AI assisting in translating those requirements into initial SQL query drafts. These queries can be immediately reviewed, modified, and tailored to exactly match the organization’s unique monitoring needs.

Beyond query generation, aiquery solves the scalability problem by managing query execution across distributed infrastructure from a single interface, handling scheduling, results aggregation, and alerting. This comprehensive approach makes osquery more accessible by reducing the technical barrier to entry, enabling both SQL experts and less technical users to collaborate on system monitoring while maintaining the performance and precision required by professional IT and security teams.

Conclusion

The fundamental challenge in combining AI with osquery isn’t technical integration but effectiveness — general AI can write SQL that runs but lacks the specialized knowledge to generate queries that truly matter for security monitoring. The future lies in creating tools that empower administrators by making query creation more efficient, while preserving the precision and control that security teams require.

By focusing on AI as a collaborative tool that assists in translating specific administrative requirements into precise queries, organizations can unlock the full potential of osquery — making powerful system monitoring accessible without compromising on depth or accuracy.