Just finished skimming the latest "AI Security Framework" PDF dropped by one of the big-name consultancies. It's 127 pages. I estimate 115 of those pages are filler—vague diagrams, endless definitions of "AI" and "risk," and management-speak about "governance pillars" and "ethical alignment." It’s security theater scaled for C-level anxiety, not for engineers who have to implement actual isolation.

The core technical content, where it exists, is a superficial recitation of basic infosec concepts clumsily mapped to AI buzzwords. For example:

* It spends 20 pages on "AI Risk Taxonomy" but defines "model extraction" without a single mention of actual mitigation techniques like rigorous rate limiting, output perturbation, or—heaven forbid—kernel-level syscall filtering around the inference endpoint.
* It recommends "strong access controls" for training data, but its most technical prescription is "use IAM roles." No discussion of filesystem namespaces, mandatory access control (like SELinux contexts for training pods), or integrity measurement.
* The "Secure Deployment" chapter suggests "monitoring for drift" but treats the model as a black box. There's zero insight into instrumenting the serving container itself: no eBPF probes for anomalous model-serving process behavior, no seccomp-bpf profiles to block unnecessary syscalls like `ptrace` or `module_load`, no use of `clone3` with `CLONE_NEW*` flags for finer-grained sandboxing.

If this were a real framework for our domain, a section on "Inference Runtime Isolation" would look concrete. It would prescribe specific, testable configurations. For instance, a minimal seccomp profile for a PyTorch model server might start with:

```c
{
"defaultAction": "SCMP_ACT_ERRNO",
"architectures": ["SCMP_ARCH_X86_64"],
"syscalls": [
{"names": ["read", "write", "close", "fstat"], "action": "SCMP_ACT_ALLOW"},
{"names": ["mmap", "munmap", "brk", "mprotect"], "action": "SCMP_ACT_ALLOW"},
{"names": ["rt_sigreturn", "rt_sigaction"], "action": "SCMP_ACT_ALLOW"},
{"names": ["clock_gettime", "getpid"], "action": "SCMP_ACT_ALLOW"}
]
}
```

And then it would discuss the trade-offs of adding `clone`/`clone3` (probably deny), `io_uring_setup` (deny unless necessary), and how to handle `openat` (restrict to a predefined model directory). It would talk about layering this with a non-root user, dropped capabilities (`CAP_SYS_MODULE`, `CAP_SYS_PTRACE`, `CAP_NET_RAW` all gone), and a minimal `cgroup` for memory/CPU.

Instead, we get: "Implement robust security boundaries." It's useless.

My question to the forum: is anyone working on actual, low-level isolation primitives for AI workloads that go beyond cloud IAM and network ACLs? I'm looking at:
* eBPF-based runtime enforcement on model inference (e.g., blocking unexpected `execve` after load).
* Secure patterns for `agent_isolations` where an LLM-based agent has tightly constrained subprocess execution.
* Static analysis for ML frameworks to generate minimum-capability profiles.

The big consultancies are selling PowerPoint. We need to build the actual chassis.