8 Security and Privacy for Deploying Generative AI Architectures on AWS

Working with Generative AI offers unprecedented capabilities, but with great power comes great responsibility. Deploying these advanced architectures on cloud platforms like AWS demands a vigilant focus on security, privacy, and ethical standards. As these technologies become integral to high-stakes sectors like healthcare and finance, the potential consequences of misuse escalate dramatically. In this chapter, we'll delve into how to embed robust security frameworks and privacy measures within generative AI solutions, ensuring they serve the greater good while mitigating potential risks.

8.1 Introduction to Security and Privacy for Generative AI Solutions

8.1.1 Challenges in Security

8.1.2 The Amazon Bedrock Security Experience

8.1.3 Common Threats

8.1.4 Monitoring Amazon Bedrock

8.1.5 Threat Modeling

8.1.6 Developing Mitigations

8.1.7 Evaluating Mitigations

8.1.8 Working with Amazon Inspector and Amazon Macie

8.2 Integrating Access Control Management

8.2.1 Using IAM with Amazon Bedrock

8.2.2 Working with Agents

8.2.3 Working with Service Roles

8.3 Working with Guardrails for Bedrock

8.3.1 Denied Topics

8.3.2 Content Filters

8.3.3 Blocked Messaging

8.3.4 Integrating Guardrails with an Agent

8.3.5 Coding up the Implementation

8.4 Automating Continuous Learning for Security and Privacy for LLMs

8.4.1 Automating Security in Fine-Tuning Models

8.4.2 Utilizing Network and Code Review Services

8.5 Summary