Contemporary distributed systems generate telemetry at a scale and velocity that exceed the analytical capacity of conventional observability frameworks. To address this limitation, Alexandre Genest, CTO of Hilt, architected a kernel-native observability system that captures and analyzes system behavior directly at the operating system level using extended Berkeley Packet Filter (eBPF) instrumentation.
This approach eliminates fundamental visibility gaps inherent to user-space monitoring by operating at the kernel execution layer, where all system interactions occur. Building on this foundation, Genest introduced a behavioral graph modeling framework that represents system activity as a continuously evolving interaction graph, enabling structural and temporal anomaly detection across processes, files, network flows, and inter-process communication.
The system further incorporates causal event reconstruction to distinguish true execution dependencies from coincidental concurrency, and adaptive baseline modeling to maintain detection accuracy in dynamic, large-scale environments. In production deployments, this architecture has demonstrated the ability to surface previously undetectable behavioral anomalies while sustaining telemetry throughput exceeding 10⁶ events per second with negligible performance overhead. Implementation of the system included contributions from engineers Zin Bitar and David Gu.
The Observability Problem
The problem of data movement governance in modern infrastructure is not, at its core, a problem of data volume; it is a problem of observability fidelity. Modern workloads orchestrate data across ephemeral containers, short-lived processes, transient in-memory buffers, and heterogeneous network fabrics. The traditional approach to monitoring this movement, instrumenting applications at the user-space boundary, introduces a fundamental constraint: the monitoring system can only observe what the application chooses to expose. The kernel, however, has no such constraint. Every file descriptor opened, every socket written to, every process forked is a kernel-mediated event. The question is whether telemetry systems can efficiently capture and analyze this ground-truth layer at the scale demanded by contemporary infrastructure.
As Hilt has demonstrated, anchoring observability at the kernel execution layer, integrating kernel-level events into a unified behavioral model, and analyzing system activity through structural and temporal correlations enables comprehensive, low-overhead, real-time governance of data movement across large-scale distributed systems. The contributions of this work are threefold: (1) a production-validated kernel-level instrumentation architecture that maintains minimal CPU overhead across high-throughput hosts; (2) a behavioral modeling framework capturing structural and temporal properties of system activity, suitable for both rule-based and learned anomaly detection; and (3) an adaptive baseline approach that preserves detection sensitivity across infrastructure lifecycle events, including deployments, scaling operations, and container churn.
Limitations of Existing Monitoring Paradigms
User-space telemetry has historically dominated production monitoring due to its portability and deployment simplicity. Solutions in this category instrument applications directly through library hooks, SDK integrations, or operating system API interception, and forward structured log records to centralized aggregators. While effective for application-level observability, this approach exhibits several well-documented limitations that make it structurally inadequate for data movement governance at the system level.
First, user-space instrumentation is bounded by the visibility of the instrumented application. Kernel-level events, transient process creation, ephemeral file handles, low-level IPC, memory-mapped I/O, are inaccessible without explicit kernel-facing hooks. In security contexts, this creates a critical gap: an adversary operating below the instrumentation layer, or a misconfigured process whose logging is incomplete, generates no observable signal. Second, embedding monitoring logic within application processes couples the fate of observability to the fate of the monitored system. Process crashes, resource exhaustion, or deliberate log suppression can silently degrade monitoring coverage without external detection. Third, the operational burden of maintaining instrumentation across polyglot codebases and heterogeneous runtime environments introduces significant engineering cost, frequently resulting in inconsistent coverage and fragmented telemetry schemas.
Because all system interactions are mediated by the kernel, this layer provides a complete and tamper-resistant source of ground-truth telemetry, eliminating the blind spots inherent to application-level instrumentation. Every interaction between a process and the system regardless of whether the process intends to expose it transits a kernel execution path. This is precisely why kernel-level observability is the correct foundation for data movement governance.
Kernel-native monitoring via eBPF addresses these structural deficiencies by relocating the observation point to a layer that is both universal and tamper-resistant. However, raw kernel telemetry introduces its own challenges: event volume is orders of magnitude higher than application-level logging, temporal alignment across subsystems requires non-trivial reconstruction, and attributing low-level events to meaningful logical identities demands sophisticated enrichment pipelines.
System Architecture
Kernel Instrumentation Layer
The instrumentation layer consists of a set of eBPF programs attached to kernel tracepoints, kprobes, and network hooks, providing comprehensive coverage of the data movement surface: process lifecycle events (exec, fork, exit), filesystem interactions (open, read, write, close, rename, unlink), network transmission (connect, accept, send, recv), and inter-process communication (pipe, socket pair, shared memory). eBPF programs execute within the kernel’s verified sandbox, offering safety guarantees equivalent to kernel modules while avoiding the stability risks associated with loadable kernel code.
Each eBPF program extracts structured event records containing system call arguments, process context (PID, TGID, UID, GID, cgroup namespace), and high-resolution timestamps. Records are delivered efficiently to user space via in-kernel buffering, ensuring high-throughput, low-latency telemetry without introducing measurable performance overhead.
Temporal Reconstruction Pipeline
Kernel subsystems maintain independent clock domains and buffer events asynchronously, introducing ordering artifacts in the delivered telemetry stream. The temporal reconstruction pipeline addresses this using a combination of clock skew estimation, buffer delay modeling, and dependency-graph–based ordering. Incoming events are held in a bounded reordering window; events that arrive out of sequence within the window are repositioned based on their kernel-monotonic timestamps. Events that cannot be repositioned within the window are flagged with a provenance marker indicating estimated rather than observed ordering.
Across distributed hosts, clock synchronization is achieved through a hybrid approach combining NTP-derived offsets with observed inter-host event causality. When a process on Host A transmits data received by a process on Host B, the reception event cannot logically precede the transmission event. These causal constraints, derived from network flow telemetry, provide independent ordering anchors that reduce cross-host alignment error to sub-millisecond precision in practice.
Behavioral Graph Representation
The core analytical representation is a heterogeneous directed graph G = (V,E,T), where nodes V represent key system entities like a process and edges E represent observed interactions labeled with event type, direction, and cardinality. T is a temporal annotation function assigning each edge a time-series of interaction timestamps for both structural and behavioral analysis over arbitrary time windows.
This representation enables several analytical modalities unavailable in flat event log formats:
Structural Deviation Detection
Graph topology changes such as new process-to-file edges, unexpected inter-container communication paths, novel outbound network destinations are detected by comparing the current graph state against a baseline subgraph derived from historical telemetry.
Temporal Pattern Analysis
Edge time-series are analyzed for frequency anomalies, burst patterns, and diurnal drift. A file accessed 10,000 times within a two-minute window by a process that historically accesses it at 1 Hz constitutes a high-confidence anomaly signal irrespective of graph structure.
Data Flow Path Reconstruction
By traversing edge sequences correlated in time and causality, the system reconstructs end-to-end data movement paths across process, container, and host boundaries allowing for complete lineage tracking for compliance and forensic investigation.
Identity-Attributed Analysis
Process nodes are enriched with stable logical identity attributes to ensure that behavioral analysis survives infrastructure churn.
Causal Reconstruction of System Workflows
A central challenge in kernel telemetry analysis is distinguishing causally related event sequences from coincidentally concurrent activity. In a busy host, thousands of events may occur within any given millisecond, originating from numerous independent processes. Temporal proximity alone is insufficient for inferring causal relationships: events that occur simultaneously may be entirely independent, while a causal chain may span hundreds of milliseconds across a complex execution pipeline.
The causal reconstruction approach operates on the behavioral graph to infer probable cause-and-effect relationships by combining multiple independent signals derived from execution context, inter-process communication, and observed interaction patterns. These signals establish ordering constraints that extend beyond simple timestamp-based correlation, enabling more accurate reconstruction of system behavior.
The output of causal reconstruction is a directed acyclic representation of inferred dependencies overlaid on the behavioral model. This structure allows system activity to be presented as coherent causal chains rather than disjoint event streams, substantially reducing the analytical burden during incident investigation and providing a foundation for automated root cause analysis.
Adaptive Behavioral Baseline Modeling
Static detection thresholds are well-understood to be inadequate for dynamic infrastructure environments. A microservice that handles low traffic on weekday mornings and high traffic on weekend afternoons will generate dramatically different telemetry volumes across those periods; a static frequency threshold tuned for low-traffic periods will produce intolerable false positive rates during peaks, while one tuned for peaks will miss anomalies during quiet periods.
Our adaptive baseline modeling approach maintains rolling statistical models of behavioral features extracted from the graph representation, parameterized by temporal context including time-of-day, day-of-week, and infrastructure lifecycle state. Baseline parameters are updated incrementally as new telemetry is ingested, with update rates tuned to balance responsiveness to legitimate behavioral shifts against sensitivity to anomalous deviations. Infrastructure events, container deployments, scaling operations, configuration changes, are all propagated to the baseline model as contextual annotations, allowing the system to distinguish expected behavioral transitions from anomalous ones.
Performance Characteristics
A persistent concern with kernel-level telemetry is the overhead introduced by continuous event capture. eBPF programs execute in a constrained environment with strict complexity limits, but they do operate within kernel context on every observed system call. In high-throughput systems making millions of system calls per second, even sub-microsecond per-event overhead can accumulate to meaningful CPU cost.
Independent third-party measurements across production workloads indicate that the presence of the telemetry agent introduces negligible system overhead, with average CPU utilization of approximately 0.1% and memory consumption of 31 MB. In many cases, host system latency is not only unaffected but actually improved on average, with observed average latency reductions of approximately 5.3%, and worst-case added latency remaining near 2%. The observation of negative latency impact is particularly notable, as it indicates that the system not only avoids performance degradation but can, under certain conditions, improve overall execution efficiency. These improvements are attributed to a combination of kernel-level efficiencies and hardware-aware scheduling effects that optimize thread locality and CPU cache utilization.
Telemetry throughput remains more than sufficient to sustain high-volume event capture without degradation of host performance, demonstrating that comprehensive kernel-level observability can be achieved without imposing meaningful resource or latency penalties on production systems.
Production Deployment
Hilt integrates kernel-level instrumentation, temporal reconstruction, causal analysis, and adaptive detection into a single system that operates continuously across distributed environments. The platform is deployed in production environments supporting organizations responsible for billions of dollars in assets, including very latency-sensitive systems where correctness, performance, and reliability are critical.
