AWS Security Blog

Category: Amazon Machine Learning

AI AuthZ

Enhancing data privacy with layered authorization for Amazon Bedrock Agents

Customers are finding several advantages to using generative AI within their applications. However, using generative AI adds new considerations when reviewing the threat model of an application, whether you’re using it to improve the customer experience for operational efficiency, to generate more tailored or specific results, or for other reasons. Generative AI models are inherently […]

Network perimeter security protections for generative AI

Generative AI–based applications have grown in popularity in the last couple of years. Applications built with large language models (LLMs) have the potential to increase the value companies bring to their customers. In this blog post, we dive deep into network perimeter protection for generative AI applications. We’ll walk through the different areas of network […]

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Hardening the RAG chatbot architecture powered by Amazon Bedrock: Blueprint for secure design and anti-pattern mitigation

Mitigate risks like data exposure, model exploits, and ethical lapses when deploying Amazon Bedrock chatbots. Implement guardrails, encryption, access controls, and governance frameworks.

Context window overflow: Breaking the barrier

Have you ever pondered the intricate workings of generative artificial intelligence (AI) models, especially how they process and generate responses? At the heart of this fascinating process lies the context window, a critical element determining the amount of information an AI model can handle at a given time. But what happens when you exceed the […]

Figure 1: Generative AI Scoping Matrix

Securing generative AI: data, compliance, and privacy considerations

Generative artificial intelligence (AI) has captured the imagination of organizations and individuals around the world, and many have already adopted it to help improve workforce productivity, transform customer experiences, and more. When you use a generative AI-based service, you should understand how the information that you enter into the application is stored, processed, shared, and […]

Data flow diagram for a generic Scope 1 consumer application and Scope 2 enterprise application

Securing generative AI: Applying relevant security controls

This is part 3 of a series of posts on securing generative AI. We recommend starting with the overview post Securing generative AI: An introduction to the Generative AI Security Scoping Matrix, which introduces the scoping matrix detailed in this post. This post discusses the considerations when implementing security controls to protect a generative AI […]

Generate AI powered insights for Amazon Security Lake using Amazon SageMaker Studio and Amazon Bedrock

In part 1, we discussed how to use Amazon SageMaker Studio to analyze time-series data in Amazon Security Lake to identify critical areas and prioritize efforts to help increase your security posture. Security Lake provides additional visibility into your environment by consolidating and normalizing security data from both AWS and non-AWS sources. Security teams can […]

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Securing generative AI: An introduction to the Generative AI Security Scoping Matrix

Generative artificial intelligence (generative AI) has captured the imagination of organizations and is transforming the customer experience in industries of every size across the globe. This leap in AI capability, fueled by multi-billion-parameter large language models (LLMs) and transformer neural networks, has opened the door to new productivity improvements, creative capabilities, and more. As organizations […]