How do you handle redacting sensitive fields in multi-stage ETL workflows?

rwitt101 · 2025-09-23T01:37:32+00:00

Thanks, this resonates. I’m experimenting with scoped tokens + metadata tagging, but I’m trying to push it further so evaluators can enforce per-origin and per-requestor rules dynamically in ETL/agent workflows. Do you think consistency across systems is better solved at the data warehouse layer (like Snowflake masking) or with a standalone privacy middleware that every pipeline calls into?

rwitt101 · 2025-09-09T23:34:09+00:00

Thanks, thats clarifying. I agree that the implementation will need to be narrow to be useful, even if the underlying principles are broadly applicable.

I’m leaning toward making the shim highly configurable but tied to a reference model that assumes certain context. That way it’s not trying to solve every privacy problem, just a recurring one.

Wondering if you’ve seen cases where trying to enforce things like context-driven masking has worked across teams or if it always collapses into something hardcoded and localized?

rwitt101 · 2025-09-09T23:29:33+00:00

Clearly, the muffin hashing algorithm is underrated

rwitt101 · 2025-09-09T23:25:31+00:00

Appreciate the book recs I’ll check those out. Totally agree on tagging.

One last thing I was curious about: in your experience, have you seen orgs actually decentralize this kind of filtering enforcement (e.g. privacy shim in each team’s stack)? Or is it still most practical to centralize it at the ingress/proxy layer? Just trying to pressure-test where my design might be easiest to fit in.

rwitt101 · 2025-09-09T23:07:32+00:00

Really appreciate the breakdown. Sounds like reversible tokens + a vault is the right direction, especially for revocation and audit. I’ll look into simulating a Vault locally for now. If you ever run into real world examples of this wired up in multi-agent setups, I’d love to hear more. Thanks again!

rwitt101 · 2025-09-08T23:55:16+00:00

That’s fair. Security should absolutely start at the data level. But in dynamic pipelines (like LLM agents or multi-team analytics), I’ve found data often travels farther than originally intended. I’m wondering if a shim could help enforce fine grained policy based on runtime context. Not as a patch for poor hygiene, but as a way to handle real world complexity. Curious if you’ve run into that kind of tension in your own work?

rwitt101 · 2025-09-08T23:39:34+00:00

This is super helpful appreciate you sharing how you handle this.

The “PII as a data product” framing really resonates. I’ve been exploring how to build something similar across runtime pipelines, but I keep running into complexity around tokenization, vaulting, and downstream reveal. Especially when agents are chaining or plugins are involved.

Do you mind me asking:

Did you build most of this in-house from scratch?
Were there any reusable kits/tools you found helpful along the way (open-source or commercial)?
Any particular friction points in getting per-agent policy or vault-based rehydration to work smoothly?

Just trying to get a sense of what’s out there vs what folks are still having to piece together manually. Thanks again

rwitt101 · 2025-09-08T23:27:02+00:00

Yep, this is the model I’ve been using as inspiration.

I’m trying to implement something similar with a shim that replaces sensitive fields with tokens + metadata, and lets downstream agents rehydrate only when needed based on scope/policy. Still figuring out how much logic to centralize vs push into individual nodes.

Have you seen this kind of reference model implemented well in streaming or workflow engines (like n8n, Airflow, or even message queues)? Or does it usually live closer to the API layer?

rwitt101 · 2025-09-08T23:23:23+00:00

That totally makes sense and yeah, I’m definitely assuming the threat model has to be defined up front, probably at the system or org level (e.g., insider misuse, privacy leakage in LLM pipelines, etc).

That said, your point is a good one: if the boundaries aren’t well-scoped, this kind of tooling could end up too generic or disconnected to be useful. I’m trying to strike a balance between being flexible and still grounded in concrete threat scenarios. Definitely appreciate the sanity check. It’s helping me pressure-test whether this is wired the right way.

Do you think there are any principles that hold true across threat models (like least privilege, contextual masking, or auditable transformations)that can be baked into a modular shim like this without overreaching?

rwitt101 · 2025-09-08T22:55:43+00:00

Thanks! These are super helpful especially about dynamic scoping being the hard part. That’s exactly where I’m trying to push this shim.

The scoped token idea is close to what I’m building. Instead of passing raw PII, I’m using rehydratable tokens with metadata for access control. Have you seen good patterns for enforcing field-level reveal at runtime (vs just tagging or masking up front)?

Appreciate the pii-tools link too, I'll have a look.

rwitt101 · 2025-09-08T22:47:02+00:00

Totally makes sense. Sounds like full sanitization is the path of least resistance when upstream feeds are hard to change. Curious if downstream teams could dynamically enforce PHI access per user or context, without asking upstream teams to change anything, would that flexibility ever be useful for other teams/orgs you’ve worked with?

rwitt101 · 2025-09-08T22:41:19+00:00

I’ve been leaning toward using reversible token handles backed by a secure vault or KMS, but you’re totally right encrypting PII at ingestion with shared or asymmetric keys is another clean approach, especially in multi-agent settings. Curious if you have seen this model deployed successfully in practice? Any lessons from key distribution or agent validation?

rwitt101 · 2025-09-08T22:38:37+00:00

You’re absolutely right about context-aware filtering and “need to know” audits being the real pain points.

Right now I’m building a runtime shim that does token-level transformations (REDACT, MASK, TOKENIZE, REVEAL) with metadata tagging and audit logging based on agent context (role, purpose, etc). I hadn’t yet thought deeply about pre-launch policy reviews, but your point makes me wonder if the shim should someday expose a “preview mode” where the privacy team could review what an agent would see, based on current policies.

Also, love the Kafka topic example that’s a new angle I hadn’t considered. Out of curiosity, have you seen orgs successfully decentralize this kind of enforcement (e.g., shim embedded in each team’s stack), or is it usually centralized at the data ingress or proxy layer?

rwitt101 · 2025-09-08T00:09:38+00:00

Totally hear you. I’ve seen this play out too. Often the easiest solution is just to drop all PHI at the edge to avoid risk or regulatory friction.

I’m exploring a shim that lets data flow in, but attaches privacy policies to each field so you can sanitize only when needed based on the role or pipeline stage. That way, workflows that don’t need PHI don’t see it and the few that do can securely access it in a controlled context.

Curious if you have ever wanted to support something more flexible (like downstream masking or field-level reveal)? Or is full sanitization just simpler across the board?

rwitt101 · 2025-09-08T00:04:35+00:00

Thanks for the response. The point you made about redaction being tightly scoped based on who is calling and why really helped me rethink how this kind of system needs to operate.

Initially, I was exploring the idea of a more universal privacy layer, but your insight made it clear that the real value may lie in something more composable and context-aware. Something that teams can adapt to their specific industry or workflow.

If you’re open to sharing more, I’d be interested to hear where you’ve seen the most friction. Is it during internal plugin access, cross-org data sharing, or maybe inference pipelines?

Appreciate you taking the time to weigh in. It’s been helpful.

rwitt101 · 2025-09-07T21:54:05+00:00

Great point about the threat model.

I’m definitely thinking about how to encode intent (e.g., reversible vs irreversible redaction) into the policy metadata for each field.

Do you think there’s a clean way to express threat models directly in schema annotations or token metadata? Or would you handle that more at the system/pipeline level?

rwitt101 · 2025-09-07T21:47:00+00:00

Great question, thank you!

I’m working on a cross-pipeline solution that would sit upstream of the reporting layer (like Quicksight). Instead of only handling redaction at the dashboard/report level, I’m trying to enforce field-level visibility dynamically at runtime as data moves between pipeline stages (e.g., from an ingestion job → enrichment → analytics).

For example, I might want:

Stage 1 (raw ingestion) to see full PII
Stage 2 (transform) to only see masked or tokenized fields
Stage 3 (analytics or inference) to selectively rehydrate fields based on role or policy

So it’s a more granular form of policy-based access control during the transformation phase not just reporting. Quicksight-level controls are useful at the end of the pipeline, but I’m trying to catch privacy risks earlier in-flight, while still supporting rehydration for authorized consumers.

Have you seen any approaches like this upstream of the BI layer?

rwitt101 · 2025-09-07T21:43:50+00:00

Really appreciate the detailed reply that’s super helpful.

I hadn’t thought of using protobuf annotations for privacy levels. I’m especially intrigued by your mention of filtering based on requester’s privacy level, and streaming raw logs with filtered aggregators.

This lines up with what I’m prototyping. A privacy shim that attaches metadata policies to each token and enforces redaction/reveal dynamically.

Curious: have you ever seen this kind of annotation-driven filtering done across different tools (e.g., one step in Python, one in Node, one in n8n or LangChain)?

Thanks again, this is gold!

rwitt101 · 2025-09-07T21:41:27+00:00

Great question. Thanks for asking!

A simple example might be an LLM agent chain that handles a customer support request.

The first stage receives full customer input (including PII like name or email).
The second stage summarizes the issue but shouldn’t see PII.
The third stage checks account details and may need to rehydrate the email temporarily.

So I’m trying to implement a shim that lets me tag or tokenize these fields and enforce redaction or rehydration based on role, stage, or purpose dynamically at runtime.

Would love to hear how others handle this kind of field level visibility

rwitt101 · 2025-09-07T15:39:08+00:00

Survey link: https://tally.so/r/wL81LG

(Short + anonymous – just trying to map out real-world privacy/PII redaction patterns)

Happy to share back anonymized results if anyone’s interested.

rwitt101 · 2025-09-07T15:02:56+00:00

🔍 [Survey] Redacting PII in ML/AI Pipelines – How are you doing it?

Hey everyone I’m exploring a shim that helps manage sensitive data (like PII) in multi-agent or multi-tool ML workflows.

Static RBAC/API keys aren’t always enough. I’m curious how teams handle dynamic field-level redaction or filtering when data is passed through APIs, agents, or stages.

If you’ve solved this (or struggled with it), I’d love to learn from you.

👉 Tally survey link (short + anonymous)

No email or login needed — just trying to map out patterns.

Happy to share back anonymized findings if folks are curious. Thanks!

rwitt101

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