eBPF Filtering

eBPF programs attach to XDP (eXpress Data Path) hooks at the network driver level, processing packets before they reach the kernel's TCP/IP stack. This enables:

• Dropping malicious traffic at line rate (millions of packets per second)
• Rewriting headers for transparent proxy routing
• Extracting metadata for observability without syscall overhead

For AI workloads, this means model endpoint traffic is filtered at the lowest possible layer, reducing attack surface before authentication even begins.

Unlike traditional kernel modules, eBPF programs are verified by the kernel's in-kernel verifier and loaded atomically at runtime without reboots or service interruption. This enables:

• Just-in-time insertion of filtering rules in response to threat detection
• Hot-swapping observability probes during active inference sessions
• Zero-downtime policy updates across GPU cluster nodes

The verifier guarantees no infinite loops, out-of-bounds memory access, or kernel panics, making runtime injection safe for production AI infrastructure.

eBPF programs execute on specific kernel events (network packets, syscalls, tracepoints) and run to completion without context switching. Key characteristics:

• JIT-compiled to native CPU instructions for near-hardware speeds
• No userspace-to-kernel copy for filtering decisions
• Tail calls allow chaining multiple programs without stack overflow

In zero-trust AI networking, this enables per-request authentication checks on model serving endpoints without adding measurable latency to inference pipelines.

eBPF enables deep kernel and network observability without installing sidecar agents or modifying application code. Programs can:

• Trace all TCP connections to model endpoints with process-level granularity
• Measure latency histograms for every API call to inference services
• Detect anomalous traffic patterns indicative of data exfiltration

This aligns with Continuous Verification principles by providing real-time telemetry on every access request without relying on application-layer instrumentation that could be tampered with.

eBPF programs attached to TC (Traffic Control) hooks can enforce Layer 7 network policies between pods and services within a Kubernetes cluster. Capabilities include:

• Filtering HTTP/gRPC requests based on headers and payloads
• Enforcing mTLS identity checks at the kernel level
• Implementing Least Privilege Access by default-deny rules between GPU workloads

This provides the enforcement backbone for Micro-Segmentation in AI training clusters, ensuring a compromised data preprocessing container cannot laterally access the model registry.

eBPF maps are generic key-value data structures shared between eBPF programs and userspace, enabling stateful filtering without external databases. Use cases include:

• Maintaining connection tracking tables for stateful firewall rules
• Storing IP reputation lists updated by threat intelligence feeds
• Caching JWT validation results to accelerate Continuous Verification

Maps persist across program reloads and support atomic operations, making them suitable for high-concurrency filtering scenarios in distributed AI inference fleets.

A security technique that divides a data center into distinct logical segments down to the individual workload level. eBPF programs are the ideal enforcement mechanism for micro-segmentation policies because they can filter traffic at the socket level with minimal overhead.

• Enforces granular east-west traffic controls
• eBPF enables identity-based filtering without network address translation
• Operates at Layer 7 with deep packet inspection capabilities

The network component responsible for activating and executing access decisions. When implemented with eBPF, a PEP runs directly in the Linux kernel's networking stack, inspecting every packet without context switching to userspace.

• eBPF transforms the host itself into a distributed PEP
• Eliminates the need for inline firewall appliances
• Enforces policy at line rate with negligible latency impact