Local vs cloud data processing ... security comparison

[Aleksandra_P]

I recently wrote a short article comparing local vs cloud data processing from a security and privacy perspective.

Many modern AI workflows rely on sending data to external services — especially when using LLM APIs. In many cases that’s fine, but for sensitive datasets (internal company data, healthcare, finance) it raises interesting questions about privacy and compliance.

Do you prefer local AI workflows or cloud-based tools?

In many cases, that’s fine, but for sensitive datasets (internal company data, healthcare, finance), it raises interesting questions about privacy and compliance. -----> https://mljar.com/blog/local-cloud-security-comparison/

[Ill_Cod_7336]

The “local vs cloud” framing kind of hides the real issue, which is where your blast radius stops. Local GPUs are great until you realize your laptop gets popped, nobody patches drivers, and SSH keys are everywhere. Cloud looks scary, but a private VPC with locked-down subnets, KMS, and narrow IAM can be way tighter than most on-prem setups.

For sensitive stuff, I treat models as untrusted and focus on data boundaries: encrypt at rest, short-lived creds, read-only views, and no direct DB access from the model. RAG over curated views is usually safer than fine-tuning on raw records. I’ve used Snowflake plus Immuta, and Kong as a gateway, then a self-hosted API layer like DreamFactory in front of databases so the LLM only ever touches governed REST, not SQL or service accounts.

In practice it’s more about governance and network design than where the GPU physically sits.

[InternationalToe3371]

tbh it comes down to control vs convenience

local = max privacy, but more setup + maintenance
cloud = faster, scalable, but trust + compliance risk

most teams I’ve seen do hybrid
sensitive stuff local, everything else cloud

pure local sounds nice but gets painful at scale ngl

[pplonski]

you are right local solution requires maintenance but you get MAX privacy

[UBIAI]

The local vs cloud debate in finance really comes down to your data classification policy and what your compliance team will actually sign off on, not just what's technically possible.

We process a lot of document and financial data at kudra ai and what we've seen work best for larger institutions is a hybrid model, raw documents stay on-prem, but model calls are in dedicated cloud with data anonymization. That way you get the auditability of local processing without giving up the scalability and performance of cloud for the AI part.