-
Notifications
You must be signed in to change notification settings - Fork 1
Adoption And Impact
Krishna Kishor Tirupati edited this page Jul 23, 2026
·
1 revision
This page describes how PolicyAware adoption and impact can be tracked over time.
The goal is to collect useful product feedback while maintaining a verifiable evidence trail of open-source usage, user engagement, and real-world impact.
- PyPI downloads and release history.
- GitHub stars, forks, issues, pull requests, and discussions.
- GitHub traffic screenshots.
- Public Show and Tell threads.
- Private Google Form feedback with quote permissions.
- External articles, comments, mentions, or references.
- User stories showing how PolicyAware helped identify AI governance, sensitive-data, tool-governance, RAG, audit, or compliance gaps.
- Private structured feedback form: https://docs.google.com/forms/d/e/1FAIpQLSc2QcQydjXZ0YF9bbVSpudoM5y8noxIP5jU-acVmjlyvf6Slg/viewform
- GitHub Discussions: https://github.com/ktirupati/policyaware/discussions
- Testimonials and Show and Tell: https://github.com/ktirupati/policyaware/discussions/categories/show-and-tell
- GitHub Issues: https://github.com/ktirupati/policyaware/issues
| Month | PyPI Downloads | GitHub Stars | Forks | Issues | Discussions | External Mentions | Notes |
|---|---|---|---|---|---|---|---|
| 2026-07 | TBD | TBD | TBD | TBD | TBD | TBD | Feedback and testimonial collection setup added. |
- Home
- Capabilities
- Copy-Paste Examples
- Comparison
- SEO And Distribution
- Feedback And Testimonials
- Adoption And Impact
- Contributing
- Roadmap
- Ready-To-Use YAML
- Data Protection
- Policy Enforcement
- Gateway Orchestration
- Risk Classification
- Model Routing and Providers
- Tool Governance
- Evaluation
- Audit and Observability
- Guardrails Integrations
- ML-Assisted Signals
- Local Code Scan
- Installation
- Quick Start
- Architecture
- CLI Reference
- ML Integrations
- Provider Adapter Examples
- YAML Policy Templates