Setting the file. One moment.
Skill 29 · Knowledge Base For Startups
Subchapter 29.86
references/learn/genai/a-seven-step-checklist-to-get-your-generative-ai-application-security-ready.mdMarkdown12 KBView on GitHub
Generative AI (opens in a new tab) has seen explosive growth in recent years, with applications transforming how startups create content, analyze data, and make critical decisions. Organizations are increasingly using the power of generative AI models to build custom applications. As such, the startups experimenting and wielding generative AI must make security and responsible usage a top priority.
In this post, we’ve developed a seven-item checklist outlining the essential security and compliance measures you should consider when moving your generative AI-powered applications from experimentation to production.
Implementing the checklist will help your startup mitigate risks, protect data, and maintain user trust. While checking off more items improves defense, you don’t need to complete every checkpoint, as this will depend on what your application needs.
The security controls required will vary depending on the type of model (pre-trained, fine-tuned, or custom) you’re using to build your application. Our focus will be on applications built using pre-trained models, which address most customer use cases.
Establishing a comprehensive governance and compliance framework is the foundation for responsible AI deployment. People and process are key, so start by forming a cross-functional AI governance committee with subject matter experts from legal, IT security, and relevant business units. This committee should create and enforce specific policies for your generative AI application, covering data handling, model selection, and usage guidelines.
Next, develop a compliance checklist tailored to your industry regulations (such as GDPR (opens in a new tab) or PCI DSS (opens in a new tab)). This should cover data privacy measures, consent management, and transparency requirements. Implement a regular compliance review, such as quarterly audits, to make sure you adhere to developing standards.
Further reading:
Finally, set up a documentation system to track decisions, changes, and compliance status of your generative AI application. Include features like version control for policies, audit logs for model changes, and a dashboard for compliance status. This system will not only help in maintaining compliance but also provide necessary evidence during external audits.
It’s crucial to understand specific limitations and requirements to maintain compliance and avoid potential legal issues. Before integrating a pre-trained model into your application, review the EULA and data usage policies of your chosen LLM provider. Pay close attention to clauses on data handling, model outputs, and restrictions on commercial use.
As well as ensuring compliance, keeping an eye out for new updates can also bring exciting opportunities. For instance, the Meta Llama 3.1 license (opens in a new tab) is considered more permissive than its predecessors, opening up new use cases such as analyzing lengthy documents and building advanced multilingual chatbots for global use.
When developing and deploying your generative AI application, you will need robust access controls to protect your system and data. This includes setting up user authentication, authorization, and data access policies, all while adhering to the principle of least privilege (PoLP). The idea behind PoLP is that users and services only get the access they need to do their jobs.
For retrieval augmented generation (RAG) implementations, it’s crucial to manage access to the knowledge bases used to augment LLM responses. You can simplify this by using Amazon Bedrock Knowledge Bases (opens in a new tab) with metadata filtering (opens in a new tab), which provides built-in access controls. If you’re managing your own RAG, use Amazon Kendra (opens in a new tab) to filter responses based on user permissions (opens in a new tab).
Once you’ve implemented access controls, you can now focus on evaluation mechanisms to assess and mitigate risks associated with user inputs and model outputs in your generative AI application. This helps protect against vulnerabilities such as prompt injection attacks, inappropriate content generation or hallucinations.
You can simplify this process using Amazon Bedrock Guardrails (opens in a new tab). Guardrails allows you to configure defenses for prompt input and model output that can be applied across LLMs on Amazon Bedrock, including fine-tuned models and even generative AI applications outside of Amazon Bedrock.
As an additional precaution, implement a verified prompt catalog (a pre-approved set of prompts for common tasks) using Amazon Bedrock Prompt Management (opens in a new tab) to manage prompts effectively and protect the LLM from malicious instructions.
Make sure you protect the data your model uses and responds to (such as user queries, additional contexts, and knowledge bases used in RAG systems) through encryption.
If you’re handling sensitive data, you can also implement data masking or blocking using Bedrock Guardrail’s sensitive information filters (opens in a new tab).
Now that your data is secured, you can focus on protecting your generative AI infrastructure. When using proprietary data, make sure to set up a secure perimeter to prevent exposure to the public internet.
LLMs use significant computing power, making them targets for abuse. To prevent this, you can set limits on how much users can access your application:
These measures will protect your infrastructure while ensuring your system performs well and runs consistently.
Once you’ve secured your set-up and data, you can now look to securing system monitoring. This includes implementing response mechanisms to quickly detect and address security issues.
Having these clear steps in place for security issues will help you respond faster and keep your AI application safe and stable.
This seven-step checklist is an essential guide for moving your generative AI application from prototype to production. Addressing and actioning each item will help you build and deploy responsibly, protecting both your organization and your users.
Generative AI is evolving at a rapid pace, so it’s important to keep up to date with the latest developments in AI security to keep your application (and your startup) at the forefront of innovation and trust.
Check out AWS Community – Generative AI (opens in a new tab) to catch the latest updates!