paig
Protects Generative AI applications by ensuring security, safety, and observability
GraphCanon updated Sep 11, 2026 · GitHub synced Sep 11, 2026
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Decision brief
PAIG is an open-source security tool for generative AI applications that focuses on compliance and guardrails.
Good fit when
- You should use PAIG when you are working with generative AI applications where strict adherence to compliance protocols is necessary.
- Use it if your project requires extensive observability features to monitor the performance of GenAI components.
Avoid when
- Avoid using PAIG if your application does not require stringent safety and security measures specific to generative AI systems.
- Do not use PAIG when working on non-generative AI projects as it is specifically tailored for GenAI applications.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (401d since push)
- As of Sep 11, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 11, 2026
- Security (OSV)
- No lockfile
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/privacera/paigSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
PAIG is an open-source tool for securing GenAI apps, focusing on compliance and guardrails.
Capability facts
- Languages
- css
Source: github.language · Sep 11, 2026
Categories
Tags
README
Quick Start To quickly try out PAIG, you can use the Google Colab Notebook or the downloadable Jupyter Notebook. Here is the link to the <a href="https://docs.paig.ai/index.html" target=" blank" Quick Start Guide & Documentation</a License PAIG is licensed under the Apache Licens...
For agents
This page has a .md twin and JSON over the API.