The chain of trust ends the moment your OS finishes booting.

If you operate an AI data center, GPU cloud, or shared AI infrastructure, the same question comes up every time a machine changes hands: when a machine moves between tenants, how do you know it is still trustworthy?

The textbook answer is to bring the system back to a known-good state from the ground up. That means a full reboot, firmware re-validation, and a fresh chain of trust from hardware through the operating system.

Those paths assume something boot-time security cannot verify: that nothing in the runtime environment has been tampered with. A kernel rootkit, an eBPF backdoor, a user-space implant, or a malicious runtime library can leave a machine fully under attacker control while it continues to pass every boot-time integrity check the cluster runs.

In a multi-tenant GPU cluster, that gap is real. A single compromised node can persist silently across tenants, move laterally across the fabric, and observe or exfiltrate everything that runs on it: model weights resident in GPU memory, training data, and inference traffic. Boot-time security never sees any of it.

The Limits of the Chain of Trust

AI infrastructure relies on a layered chain of trust that begins in hardware, extends through firmware and boot integrity, and ultimately reaches the operating system and the workloads running on top of it. This model assumes that if a platform starts from a trusted state, the system can continue operating securely throughout its lifetime.

In practice, however, this assumption does not always hold. Modern attackers increasingly target the system after the chain of trust has already been established, manipulating the runtime environment and the integrity of the running kernel and system state.

Kernel rootkits, user-space implants, eBPF-based backdoors, and runtime library injections allow adversaries to manipulate the live system without modifying boot artifacts or files on disk.

By altering the behavior of the running kernel or injecting logic into user-space processes, attackers can hide processes, intercept syscalls, conceal network activity, and maintain long-term persistence while remaining largely invisible to traditional monitoring tools.

What is Runtime Integrity?

Runtime integrity is the assurance that a system remains trustworthy while it is actively running, not just when it boots. It focuses on protecting and validating the live execution environment: the kernel, loaded modules, memory-resident components, user-space processes, shared libraries, and the mechanisms that connect them.

Runtime integrity asks a simple but critical question: is the system still behaving as the trusted system we expect, or has its live state been altered by an attacker?

Modern systems are dynamic by design, which makes this problem difficult. The kernel loads code and manages memory structures, processes start and stop, libraries are linked at runtime, and technologies such as eBPF intentionally allow new logic to be loaded into the kernel after boot.