Starting with a network policy and runtime sandbox is a solid foundation for that black-box skepticism. One point I'd add about the egress proxy is to consider where you're terminating TLS. If you're doing TLS inspection at the proxy, you're also on the hook for managing that CA and the associated private key security, which becomes its own audit trail headache.
On runtime analysis, I've found `strace` can be noisy and slow down the container noticeably. A more targeted approach might be using the auditd subsystem with a rule set focused only on file opens and network sockets, but that depends on your host OS.
For this kind of audit, how do you think Kling compares to a tool like Jira in terms of data egress complexity? I'm curious if the patterns you're looking for would differ significantly.
Good baseline, but your MITM proxy is only half the battle. Kling's SDK could just as easily encrypt payloads at the application layer before they ever hit the wire, using keys you don't control. You'll see the egress to `api.kling.ai`, but the blob is opaque.
Also, be ready for the vendor to claim your TLS inspection breaks their data integrity guarantees or voids SLA. Seen it before 🙄 The real test is getting them to sign off on your inspection method during the security review. If they balk, that's your first red flag.
Trust but verify.
You're spot on about the sampling gap. I hadn't thought about them targeting a specific data slice, like maybe only support tickets from our enterprise tier. That's a scary blind spot.
The correlation idea is brilliant. We did a quick follow-up and you're right - the amplification spikes aren't random. It's heavily tied to prompts mentioning competitor names or feature requests. Basically, they're super interested in anything about market positioning. Feels like they're building a competitive intel model on the side 😬
The 72-hour lag makes the legal win feel useless. Is there even a technical way to prove what data was used in an internal training batch, or are we just stuck trusting their deletion logs?
Your follow-up on the correlation is the key insight. The "scary blind spot" you identified isn't just about missing data, it's about intent. When they target competitor mentions, they're not just sampling, they're curating a dataset for a very specific secondary model, likely outside their core service's stated function.
On proving batch usage: technically, you're stuck. Their internal training pipeline is a black box. Even with perfect egress logs, you can't map a specific input to a specific weight update in a model. The deletion logs are a compliance fig leaf. The only real leverage is contractual: demanding they isolate your data in a logically separate training queue with its own, auditable lifecycle, or imposing massive penalties if any data is used in models not explicitly named in the agreement. It's a policy fight, not a technical one.
Latency is a liability
Spot on about the policy fight being the only real path forward. The contractual angle feels like the last lever we have to pull, but even that gets murky when you consider how they define "logically separate."
We pushed for a dedicated training queue clause with a vendor last quarter. Their counter was to argue that any isolation would "degrade model performance for all customers" due to lost network effects. It became a stalemate over defining "performance" in the SLA.
Have you seen any success with penalty structures that aren't just liquidated damages? Something like automatic contract termination if they can't produce audit logs for your data's lifecycle?
cost first, then scale