Observed Signal · Jun 17, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Add AWS Bedrock Guardrails to Kong AI Gateway
A developer tutorial shows how to integrate AWS Bedrock Guardrails with a Kong AI Gateway running on Kubernetes using the ai-aws-guardrails plugin. The guide wires every request and response through a Bedrock policy layer so violations are blocked at the gateway rather than in application code. It demonstrates four guardrail types: content filters, prompt-attack protection, PII/sensitive-information handling, and custom denied topics. The article includes example Bedrock JSON (sensitiveInformationPolicyConfig) that blocks emails, card numbers, passwords and AWS keys (or can anonymize them), and a decK plugin snippet for Kong that sets guarding_mode to BOTH to inspect both input and output. The post links to a full step‑by‑step Hashnode guide and recommends pairing gateway guardrails with rate limiting, semantic caching and JWT auth.
Practical, implementable guidance for adding safety and compliance at the gateway level for LLM deployments; useful for engineering teams but not an industry‑shifting platform policy change.
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Key Takeaways & Evidence Grounding
- Tutorial integrates AWS Bedrock Guardrails into a Kong AI Gateway via the ai-aws-guardrails plugin.
- Four guardrail categories configured: Content Filters, Prompt Attack protection, PII/Sensitive Information, and Denied Topics.
- Example Bedrock JSON sensitiveInformationPolicyConfig blocks EMAIL, CREDIT_DEBIT_CARD_NUMBER, PASSWORD, AWS_ACCESS_KEY and AWS_SECRET_KEY.
- Kong decK plugin config sets guarding_mode to BOTH to inspect and block at both input and output.
- Full step‑by‑step guide and complete guardrail JSON and test cases are published on Hashnode.
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Amazon Bedrock Tutorial: From Prompt to AI Agent
This technical tutorial explains how to build AI applications on AWS using Amazon Bedrock and the Strands Agents SDK. It covers making model calls via Bedrock’s Converse API, token and context-window considerations, multi-turn conversation management, tool use (function-calling) workflows, and Retrieval-Augmented Generation (RAG) with Bedrock Knowledge Bases. The post details Bedrock guardrails for content safety, demonstrates retrieve_and_generate for knowledge-base queries (including source citations), and shows end-to-end examples in Python. Finally, it introduces the Strands Agents SDK to simplify agent orchestration—combining models, knowledge bases, tools, and guardrails—using a university FAQ chatbot example. Code samples and a companion repository are provided for hands-on learning.
Practical Guardrails for AI Agents
A developer-published guide details a four-layer set of guardrails to safely run agentic AI tools that can touch files, terminals, or databases. The layers are: (1) agent and editor controls (default read-only/ask mode, allowlist/denylist for commands, scoped workspace, per-chat resets), (2) repository protections (protect main branch, require review and CI, allow commits but not pushes, secret-scanning hooks), (3) data and credentials (provide read-only roles, no production write access, keep secrets out of prompts), and (4) a human-in-the-loop gate for irreversible actions (schema migrations, deletes, deploys, force-pushes, financial actions or messages to real users). The author argues these guardrails preserve developer speed while eliminating paths to unrecoverable damage. Publication date: 2026-06-01.
AI Guard Gateway v0.1.0 Released for LLM Endpoint Security
AI Guard Gateway v0.1.0 is an open-source inverse proxy released to protect exposed AI/LLM inference endpoints from attacks such as endpoint hijacking and prompt injection. Built with a Spec-Driven Development (SDD) approach, the gateway implements mandatory authentication (API keys/JWT), sliding-window rate limiting, prompt-injection detection, automatic PII redaction, and static policy support via Open Policy Agent (OPA). The project includes a pytest test suite for critical routes, a Bandit security scan reporting no High/Medium vulnerabilities, and a CI/CD pipeline integrated with SonarCloud. The code is available on GitHub under the AGPL-3.0 license. The post was published on DEV Community on 2026-07-05 by the author MagoPredator (Fenix).
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