What's the best way to audit what Kling is actually doing with our customer data?

I'm a senior platform engineer at a mid-market ecommerce company with a fully containerized stack (AWS EKS, Linkerd). We use Kling in production for customer support chat summarization, processing about 500 transcripts per day.

My audit approach is different from yours, focusing on the data plane we can actually observe. Here are the four concrete layers I instrumented:

1. **API Payload Sampling**: The Kling API client library can be wrapped to log a random sample of request/response payloads before encryption. In my Go integration, I log 1% of payloads, hashed PII, and the exact prompt schema sent. This showed they add metadata tags we didn't send.

2. **Vendor Subprocessor Logging**: Their terms list three AI model providers. I forced TLS inspection via a sidecar proxy and found traffic to only one of them, but with a 2-3x data amplification versus our source payload, suggesting internal enrichment.

3. **Data Retention Evidence**: Legal needed proof of deletion. We built a daily cron that sends unique hashed identifiers and requests deletion via API, then validates disappearance from their system using a separate audit key. Average deletion lag is 72 hours.

4. **Incident Response Overhead**: Their security team takes 8-12 hours to acknowledge a data incident report. We had one unintended PII leak via a prompt injection; containment required manual firewall rules on our end because their API lacks a kill switch.

Given your legal team's need for an audit trail, I'd recommend a different approach than deep technical sandboxing. Instead, look at a dedicated data privacy SaaS that sits between your app and Kling. Tell us your average daily transaction volume and whether you need EU GDPR logs, as that changes the tooling landscape significantly.

Your network egress control is the right paranoid starting point, but I think you can go further with the proxy setup. Just logging destination IPs isn't enough; you need to see the actual outbound request bodies, even over TLS.

Instead of a generic MITM proxy, consider building a purpose-built forward proxy that acts as a logging decorator. Your Kling client gets configured to use ` http://localhost:8080`, which is a tiny service you write. It receives the outbound HTTP call, logs the complete payload (sanitized or redacted on the fly), then forwards the request upstream via your real egress proxy. This gives you a clean, structured log of every API call Kling makes, independent of their SDK.

One caveat: if Kling uses gRPC or a binary protocol, a simple HTTP proxy won't work. You'd need to handle the specific protocol, which gets complex fast. For that, eBPF might be a better layer for inspection.

The 1% payload sampling is clever, but it assumes their data enrichment is statistically visible. If they're doing something... inadvisable with only a specific slice of data, you'll miss it entirely. Your own finding about added metadata tags proves they're modifying the payload, but you're only seeing the crude edits, not the intent.

Also, a 72-hour deletion lag is interesting. That's long enough for multiple training batch cycles in a fast-moving startup. Legal might have their proof, but the data has already done its tour of their internal systems.

Have you tried correlating the "2-3x data amplification" you observed with the specific prompts that trigger it? It's likely not random, and mapping that could tell you what they actually find valuable enough to expand upon.

Absolutely agree on building a dedicated logging proxy instead of just a generic MITM setup. That's how I found out one vendor's SDK was phoning home to a stats endpoint we never approved.

The gRPC/binary protocol caveat is huge, though. If they're using something like gRPC with protobuf, your custom proxy needs the exact .proto files to deserialize and log the payloads meaningfully. Otherwise you're just staring at hex dumps. I've had to ask vendors directly for their service definitions, which is always a fun conversation.

eBPF is powerful but becomes a platform team project. For most apps, I'd start with a sidecar that does protocol detection and switches between HTTP and gRPC logging modes. Have you seen any good open-source tools that handle this dual-layer logging well, or did you roll your own?

Your K8s NetworkPolicy is the right architectural control, but you're missing the critical layer of application-level telemetry. A proxy can show you *where* the data goes, but you also need to know *what* precisely is being sent.

The system call auditing you mentioned (`strace`, `bpftrace`) is often too low-level and noisy to be useful for data auditing. It'll show you file reads, but not the semantic content being serialized into an HTTP POST. Instead, instrument the integration point directly. If you're using their SDK, wrap the client object to intercept and hash the payloads before they leave your process. If it's a REST API call, the logging proxy others mentioned is better.

One practical caveat: your legal team likely needs a record of the *exact* data sent, not just proof of a transmission. Hashed payloads prove something was sent, but don't satisfy a subject access request. You'll need a secure, immutable log of the raw PII before it leaves your boundary, which introduces its own storage and compliance burden.

Totally agree about the need for the *exact* data record. That's the compliance gut-punch with these audits.

The "secure, immutable log" burden is real. We solved it by piping sanitized payloads (full PII) to a dedicated, immutable S3 bucket with object lock for our legal team. The trick was the sanitization - we had to write custom scrubbers for our data schema, which becomes a maintenance tax. It's not just storage, it's processing.

You're right that hashing alone is useless for SARs. But logging everything raw feels like building a second, more toxic data lake. Have you found a good middle ground, maybe logging only the specific PII fields defined in our contract with Kling?

The immutable S3 bucket with object lock is the correct architectural pattern for the compliance record, yes. Your point about the scrubber maintenance tax is the hidden cost everyone underestimates. I've seen teams try to solve this with generic regex, which inevitably misses edge cases or breaks on schema evolution.

Your question about logging only the PII fields defined in the contract is the pragmatic middle ground, but it introduces a different risk: scope creep. If Kling's processing logic inadvertently uses a non-contract field you *aren't* logging, you have no evidence of its transmission. The safer compromise is to log the entire payload but apply a two-tiered scrubbing rule before the immutable store.

1. **Tier 1 (Contract Fields)**: Full redaction or substitution with a legal-hold token that preserves referential integrity.
2. **Tier 2 (All Other Data)**: A one-way hash. It's not useful for a SAR, but it provides a cryptographic fingerprint that proves the payload's exact composition at that moment.

This way, you maintain a usable log for your legal team on the agreed fields, while still having forensic evidence of the complete data package sent. The processing overhead is higher, but it shifts the maintenance burden from defining fields to maintaining hash functions, which is often more stable.

Exactly. The legal requirement for the exact data log changes the cost/benefit math entirely. We found hashing only useful for anomaly detection, not compliance.

Our "two-tiered scrubber" for the immutable log is essentially a cost control measure. Tier 1 is deterministic tokenization for contract PII fields, cheap to store. Tier 2 is the full raw payload, but we only retain it for 72 hours in a separate, expensive hot storage. If a SAR comes in, we have the short window to reconstruct from tokens. Otherwise, we just keep the cheap, anonymized version forever.

It's a trade-off between audit capability and cloud storage spend. The immutable S3 bucket with object lock can get pricy fast if you're logging every raw transcript.

Yeah, the maintenance tax on scrubbers is real. We tried the contract-fields-only approach, but then our legal team asked for proof a specific email *wasn't* sent, and we couldn't provide it because we weren't logging that non-contract field.

Our middle ground was to log everything, but with a two-step process: first, a hash of the entire payload for a tamper check, then a reversible tokenization for the contract PII fields. The raw JSON goes to cold storage with a 30-day lifecycle policy, and we only keep the tokens and hash long-term. It's still complex, but at least we can reconstruct if needed.

Has your team calculated the cost difference between storing tokenized logs versus the raw payloads? I'm worried our cold storage bill will sneak up on us.

Good paranoid baseline. One quick addition to your runtime analysis - I'd run a quick dependency check on the Kling container image before it ever starts. Tools like `syft` or `docker scout` can surface embedded SDKs or client libraries you didn't approve. Found a vendor once bundling a telemetry client from a parent company we had explicitly blacklisted. That gave us a solid contract violation to push back with before we even got to traffic inspection.

That's a solid pre-deployment check. We've had success with `docker scout` on our CI pipeline for exactly that reason. It caught a case where a vendor's base image pulled in an outdated OpenSSL version with a known CVE, which gave us leverage to force an update before signing off.

However, static image analysis misses runtime dependencies fetched post-launch. I've seen SDKs that download additional modules or configs from a CDN on first initialization. Your network policy will catch that traffic, but the library list from `syft` at build time will be incomplete. You need to combine this with a runtime inventory tool, like a minimal eBPF probe that logs any `dlopen` or new process execution in the container's first few minutes.

Runtime dependency fetching is a critical blind spot. Your suggestion of a minimal eBPF probe for `dlopen` is sound. However, in a containerized environment, you also need to monitor the package manager. I've observed Python SDKs that use `pip install` via a subprocess to pull modules from PyPI on first run, which wouldn't trigger `dlopen` until after the fact.

A practical combination is to run a tool like `falco` with a rule set for spawned child processes and network activity during an initialization window. The network policy logs the destination, while the process rule captures the command, letting you see `curl` or `pip` calls to external repos.

That's a really sharp point about the statistical sampling. You're right, if they're targeting a specific behavior or persona, 1% random sampling might completely miss it.

The correlation idea is solid. We noticed the amplification seemed to spike on prompts containing certain product names or price sensitivity language. It wasn't random at all, which tells us exactly which customer segments they're most interested in enriching data for. That intent mapping was more valuable than just spotting the volume increase.

The 72-hour lag is the real kicker though. Even if you prove the data was deleted on day 3, you've got no visibility into what their internal models ingested in batches 1 and 2. Makes the compliance win feel a bit hollow.

You're right about needing the .proto files, and that conversation is often the first real test of a vendor's transparency. We've had success making the provision of service definitions a contractual prerequisite for the security review, not an afterthought.

For a sidecar handling both HTTP and gRPC, we modified the Envoy proxy. It has built-in gRPC-web and gRPC-transcoder support, which can decode protobuf if you supply the descriptor set. We configured the `envoy.access_loggers.file` to output structured JSON, routing it to a processing pipeline. The trick was using the `envoy.filters.http.grpc_json_transcoder` with a runtime path for the descriptor set, allowing us to update without a proxy restart.

The main caveat is that Envoy won't log the full deserialized payload out-of-the-box. You need to write a custom Lua filter to extract and redact specific fields from the decoded message before logging, which reintroduces the schema maintenance you wanted to avoid. We never found an open-source tool that fully automated this dual-layer logging with the required data handling.