Growlithe: A Developer-Centric Compliance Tool for Serverless Applications
Bibliographic record
Abstract
Serverless applications consist of functions written in heterogeneous programming languages, use diverse data stores and communication services, and evolve rapidly. Consequently, it is challenging for serverless tenants to protect their application data from inadvertent leaks due to bugs, misconfigurations, and human errors. Cloud security tools, such as Identity and Access Management (IAM), lack observability into a tenant's application, whereas the state-of-the-art dataflow tracking tools require support from the cloud platform and incur significant runtime overheads. We present Growlithe, a tool that integrates with the serverless application development toolchain and enables continuous compliance with data policies by design. Growlithe allows declarative specification of access and data flow control policies over a language- and platform-independent dataflow graph abstraction of a serverless application, and enforces these policies through a combination of static analysis and runtime enforcement. We used Growlithe with applications using Python and JavaScript functions that can be hosted on AWS Lambda and Google Cloud Functions platforms. We empirically demonstrate that Growlithe is cross-cutting, portable and efficient, and enables developers to easily adapt their application and policies to evolving requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".