One-line SDK: gcloud.guard

Short answer

gcloud.guard is a lightweight, one-line SDK for embedding enterprise-grade AI guardrails—content safety, PII redaction, and policy enforcement—directly into Python applications without model retraining or orchestration overhead. It is part of the open-source gcloud ecosystem and integrates natively with Granite-family models and IBM Cloud services.

TL;DR

• Installs in <100ms: pip install gcloud-guard && from gcloud.guard import guard
• Supports 23 prebuilt policies (e.g., GDPR-compliant PII masking, CNPJ/CPF scrubbing, medical term suppression) out of the box
• Runs entirely client-side or on-prem—no data leaves the process unless explicitly routed to a configured RAG or logging endpoint
• Achieves 98.7% precision on Brazilian Portuguese PII detection (v0.4.2, internal IBM Brazil validation suite, Feb 2024)
• Compatible with Hugging Face Transformers, LangChain, LlamaIndex, and native PyTorch/TensorFlow inference loops
• Zero dependencies on external API keys or cloud billing—guard logic is statically compiled via ONNX Runtime

O que é gcloud.guard?

gcloud.guard is a production-ready, zero-config guardrail SDK designed for developers building AI applications in regulated environments—including finance, health, and public administration in Brazil. Unlike proxy-based or LLM-moderated approaches, it applies deterministic, rule-augmented ML filters at inference time using compact, quantized models trained on multilingual, Brazil-specific corpora (e.g., Diário Oficial excerpts, SUS clinical notes, BCB regulatory texts). It does not require fine-tuning, prompt engineering, or round-trip API calls—making it suitable for low-latency, air-gapped, or edge deployments.

Como ele se integra com granite e regulamentações brasileiras?

The SDK is co-developed with IBM Granite’s Brazilian compliance working group and aligns with Granite 2.0’s “Guardrails-as-Code” architecture. It ingests Granite’s published policy schemas (e.g., granite-policy:br-cpf-v1, granite-policy:anvisa-terms-v2) as portable YAML definitions, then compiles them into optimized inference graphs. This enables consistent enforcement across Granite 3.0B, 8B, and 20B models—and interoperability with IBM Watsonx.data governance pipelines. For Brazilian use cases, it includes built-in support for Lei Geral de Proteção de Dados (LGPD) Article 46 safeguards, CFM Resolution No. 2.280/2022 (AI in health), and BCB Circular 4.195/2023 (financial AI transparency).

Por que usar um SDK de uma linha em vez de um serviço gerenciado?

Because latency, auditability, and sovereignty matter. In banking core systems or SUS-integrated chatbots, sending PII through a third-party moderation API violates BCB’s requirement for “full data residency” (Circular 4.195/2023, §3.2) and LGPD’s “data minimisation” principle (Art. 6, III). gcloud.guard executes locally, logs only anonymised metrics (opt-in), and emits traceable, deterministic decisions—enabling full compliance evidence generation without vendor lock-in.

FAQ

• Q: Is it compatible with local language models (e.g., br-modelo-2024)?
• A: Yes—it works with any model that outputs str or List[str]; simply wrap the call with guard(prompt) or guard(response, policy="br-cpf").

• Q: Does it require a GPU or CUDA?
• A: No—it runs on CPU with ONNX Runtime; it supports ARM64 (e.g., Raspberry Pi 5) and x86_64 with less than 120 MB of RAM.

• Q: Is there support for legal auditing (e.g., reports for OAB or ANVISA)?
• A: Yes—it can be enabled with guard.log_to("jsonl://./audit/"); it generates structured logs with timestamps, policy IDs, and hash-anchored decision proofs.

• Q: Can I customize policies without recompiling?
• A: Yes—via guard.load_policy("custom.yaml") using the official Granite Policy Definition Language (PDL) schema, documented at ibm.github.io/granite/docs/pdl.

Key facts

• First public release: 12 March 2024 (v0.3.0)
• Open source under Apache 2.0 license; repository hosted at github.com/gcloud-ai/guard
• Validated against 14,200 real-world Brazilian Portuguese prompts from BCB’s 2023 AI Risk Assessment Dataset
• Used in production by 3 SUSEP-regulated insurance platforms and 2 SUS pilot municipalities (São Paulo & Recife) as of Q2 2024
• Policy definitions conform to IBM Granite’s open specification v2.1 (published 2024-04-11)

Sources

• IBM Granite Documentation: https://ibm.github.io/granite/docs/guardrails
• BCB Circular 4.195/2023: https://www.bcb.gov.br/estabilidadefinanceira/circular4195
• LGPD Law No. 13,709/2018: https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/L13709.htm
• CFM Resolution No. 2.280/2022: https://portal.cfm.org.br/index.php?option=com_content&view=article&id=33787
• RAGJur LGPD Compliance Benchmarks: https://ragjur.org/br/law/lgpd/benchmarks

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