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AI Security Gateway is the control layer between your application and any LLM provider. Change your base_url. Two lines of code. Your prompts are secured before they leave your infrastructure.
Self-host the open-source core (Apache 2.0) or managed cloud service or Hybrid VPC. Change two lines of code (base URL + API key) to route through AISG. No SDK changes, no code refactoring.
Try it free: aisecuritygateway.ai Open source: github.com/aisecuritygateway
AWS Lambda AI Security GatewayAI Security Gateway's answer:
AI Security Gateway offers a unique combination that no competitor matches:
Plus: EU AI Act Article 12 enforcement begins August 2, 2026. AISG ships compliant audit trails today โ hash-chained, tamper-evident, with JSONL export and chain verification. Most competitors have no compliance story at all.
AI Security Gateway's answer:
Software Architects, CISOs, and Engineering Managers at organizations moving LLM implementations from "experimentation" to "production." We serve teams that need to satisfy strict security and compliance reviews while simultaneously reducing the high cost of proprietary AI models. AI Security gateway is designed for developers, startups, and enterprises building applications with LLMs. It is especially valuable for teams moving from experimentation to production, where concerns around data security, compliance, and cost control become critical.
AI Security Gateway's answer:
AI Security Gateway is built using a cloud-native architecture with Python-based API services, containerized deployments (ECS/EKS), and AWS infrastructure components such as NoSQL Databases and CDN. It leverages industry-standard NLP and security frameworks for detection and integrates with multiple LLM provider APIs through a unified, controlled and optimized gateway layer.
AI Security Gateway's answer:
AI Security Gateway is currently being adopted by early-stage startups, independent developers, and AI-focused teams experimenting with production use cases. As the platform evolves, it is designed to support enterprise-grade deployments with advanced governance and compliance requirements.
AI Security Gateway's answer:
AI Security Gateway (AISG) was built after observing a recurring pattern across teams adopting LLMs: developers were unknowingly sending sensitive data such as API keys, logs, and customer information directly to external AI providers, while also struggling with unpredictable costs. Existing solutions either focused on routing or security, but not both. AISG was created as a unified layer to solve these real-world problems โ enabling safe, controlled, and cost-efficient AI adoption. AISG was born out of a realization by its founder, a veteran software architect, that 'Shadow AI' was becoming the biggest security risk in the modern stack. After seeing users accidentally paste company secrets into AI prompts, we engineered a stateless, high-performance firewall to allow innovation to continue without compromising data sovereignty.
AI Security Gateway's answer:
Unlike standard AI gateways that act as passive proxies, AI Security Gateway is an active security and compliance engine. We combine:
Based on our record, AWS Lambda seems to be a lot more popular than AI Security Gateway. While we know about 297 links to AWS Lambda, we've tracked only 1 mention of AI Security Gateway. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
AWS Lambda is a service that runs your code without you managing any servers. You write your code, deploy it to Lambda, and it takes care of the infrastructure โ servers, networking, security, and scaling. - Source: dev.to / 3 months ago
Clay can replace the Lambda and API chain if you'd rather avoid custom code. You set up a Clay table as the enrichment layer, trigger it from Segment via webhook, and it handles the waterfall and CRM push without writing a function. The tradeoff: less control over scoring logic and higher cost per enriched contact. - Source: dev.to / 3 months ago
To show why this matters, take a look at the following example. I have three AWS Lambda functions, Lambda being the serverless compute service, that each handle a different endpoint on the same API. But, almost everything about them is the same. They have the same runtime, the same memory configuration, and nearly the same structure. The only differences are the name, handler, and possibly some environment variables. - Source: dev.to / 3 months ago
Query Expansion and Decomposition: Amazon Bedrock query expansion broadens search; AWS Lambda query decomposition breaks complex queries into sub-queries; AWS Step Functions orchestrates multi-step retrieval. - Source: dev.to / 4 months ago
You need to understand synchronous and asynchronous inference patterns, event-driven architectures using Amazon EventBridge, workflow orchestration with AWS Step Functions, data processing with AWS Lambda, state management with Amazon DynamoDB, and security with AWS Identity and Access Management (IAM). The exam tests your ability to design serverless architectures that scale automatically, handle failures... - Source: dev.to / 4 months ago
I built AI Security Gateway to fix this. It's an open-source proxy that sits between your app and any LLM provider. Every prompt passes through a security layer before it reaches the model. - Source: dev.to / 4 months ago
Amazon API Gateway - Create, publish, maintain, monitor, and secure APIs at any scale
Portkey - Build production-grade & reliable AI apps with Portkey
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.
liteLLM - One library to standardize all LLM APIs
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
OpenRouter - A router for LLMs and other AI models