AI Security Data Collection Using eBPF, Proxies, and SDKs
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Solution Overview
Problem
Conventional security solutions for AI-based systems lack the capability to inspect or intercept the unique semantic content, execution context, and multi-stage processing flows inherent to AI-based workloads, leading to fragmented visibility and limited capacity for real-time, preventative controls across heterogeneous computing environments.
Innovation Solution
Implementing a hybrid configuration of extended Berkeley Packet Filter (eBPF) sensors, proxy components, and software development kit (SDK) components to collect granular performance and state data from computing resources, generating a correlated mapping for threat detection and security enforcement across diverse AI environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security solutions are used for AI-based systems, then system simplicity is maintained, but security visibility and threat detection capability are fragmented and limited
Solution Approach 1:
The security system is segmented into multiple specialized components: eBPF sensors for kernel-level data collection, proxy components for network traffic interception, and SDK components for application-layer instrumentation. Each component operates independently at its own layer, collecting security-relevant data without requiring full system redesign, thus improving security visibility while managing complexity through modular architecture.
Solution Approach 2:
The patent implements nested monitoring layers where eBPF sensors operate within the kernel space, proxy components operate at the network layer, and SDK components operate at the application layer. Each layer nests within the system hierarchy, with data flowing from inner layers (kernel) to outer layers (application), creating comprehensive security visibility without requiring complete system replacement.
2Measurement precision
If hybrid configuration of eBPF sensors, proxy components, and SDK components is implemented, then comprehensive security visibility and real-time threat detection are achieved, but device complexity increases
Solution Approach 1:
The patent introduces intermediary components that bridge different system layers: eBPF sensors act as intermediaries between kernel operations and security analysis, proxy components mediate network traffic between applications and external systems, and SDK components serve as intermediaries between application logic and security monitoring. These intermediaries enable precise security measurements without requiring direct modification of core system operations.
Solution Approach 2:
The monitoring framework is designed with universal data collection capabilities that can operate across multiple system layers simultaneously. The same core architecture supports eBPF sensors, proxy components, and SDK components, allowing the system to perform multiple security functions (network monitoring, system call tracking, application-level analysis) through a unified platform, thus managing complexity through reuse.
3Measurement precision
If granular performance data and state data are collected from each computing resource, then threat detection accuracy is improved, but data processing overhead and system performance impact increase
Solution Approach 1:
The patent extracts only security-relevant data from computing resources using eBPF sensors that filter system calls, proxy components that inspect network packets, and SDK components that monitor application events. By taking out only the necessary security data rather than collecting all system data, the system achieves high threat detection accuracy while minimizing data processing overhead and maintaining system throughput.
Solution Approach 2:
The monitoring components perform preliminary filtering and aggregation of data at the source (kernel level, network level, application level) before data leaves the computing resources. eBPF sensors pre-process system call data, proxy components pre-filter network traffic, and SDK components pre-aggregate application events, reducing the volume of data that requires further processing and analysis, thus maintaining productivity while improving detection accuracy.
Data Source
AI summary
Methods and systems for artificial intelligence (AI)-based techniques for data collection techniques for security and safety of artificial intelligence (AI)-based applications are provided. A user query pertaining to one or more artificial intelligence (AI) models of a system is detected. Each of the one or more AI models are supported by services associated with the system, each running via at least one computing resource. Performance data reflecting a performance of an operation pertaining to the user query by each respective service and state data reflecting a state of the computing resource based on the performance of the operation is received. A mapping between the performance data and the state data received from each computing resource is generated. One or more of a threat detection operation or a security enforcement operation is performed based on the generated mapping.