The advent of user-friendly Large Language Models (LLMs) has marked a significant milestone in the AI adoption journey for many organizations. Leveraging AI to support critical services no longer requires a team of data scientists and months of training and testing. Powerful AI models are now just a click away, accessible via APIs. However, despite the allure of being just "an API call away," there are critical infrastructure considerations for successfully implementing Enterprise Generative AI initiatives.

Infrastructure Overview

AWS Bedrock is a robust service that streamlines access and billing for several Generative AI models, including third-party products like Claude, ChatGPT, and Stable Diffusion, as well as AWS native solutions like Titan. These models are easily accessible to data scientists and analysts using SageMaker notebooks, and to developers for integration into applications running anywhere.

Enterprises can expose customer solutions through AWS API Gateway, allowing them to set up custom tokens and usage thresholds, thus adopting pay-for-what-you-use models for their customers. Serverless solutions like Lambda and Fargate can access Generative APIs through Bedrock’s SDK, which is available in multiple languages, including Python and Java.

Bedrock responses are key-value pairs, which can be stored in AWS’s DynamoDB—a managed NoSQL database solution—for ingestion into other workflows, archival purposes, or to support massively parallel operations without additional operational overhead. Bedrock provides an easy on-ramp for enterprises to access Generative AI, and its seamless integration with hundreds of other AWS services supports the rapid development of flexible, AI-backed solutions for any use case.

Security Considerations

Like cloud computing, managed generative AI solutions operate on a shared responsibility model when it comes to security. While robust AI risk management and compliance standards frameworks like NIST AI 100-1 and ISO/IEC 22989 exist, organizations should also assess the unique risks associated with their specific use cases. This includes potential exploitation by malicious actors and measures to safeguard data integrity.

Beyond established frameworks, organizations must understand the specific risks inherent in their AI solutions, similar to any other software product. Key questions to consider include: What are the risks associated with your solution? How could bad actors exploit it? What safeguards can you implement to protect against these risks? And importantly, how are you protecting your data?

AWS Bedrock offers Guardrails, an AI safety tool that allows enterprises to create limits around the responses users receive from Generative AI solutions. Additionally, organizations can develop custom logic to analyze information from an AI model before presenting it to end users. Both approaches are useful for minimizing risk to organizations and end users.

Ensuring Data Integrity

Protecting data integrity is crucial, especially when integrating additional data to tailor AI solutions to organizational needs. Generative AI and LLMs often perform well out of the box, but to customize your solution to your organization’s specific requirements, you may need to provide supplementary data for contextualization. When this data contains personally identifiable information (PII), responsible stewardship of this sensitive information is critical.

Different models from different vendors have varying terms of service, making it important to select a model vendor that can be trusted with your data. Moreover, organizations must scrutinize how their solutions handle data, including the storage and access protocols for model queries.

Beyond ensuring the reliability of your chosen model vendor with your data, consider how your solution utilizes data. Are model queries stored in your environment? What kinds of PII or other sensitive information could these queries contain? Who will have access to them? How do you restrict the type of information delivered from the model before it reaches the end user? For example, if a model is trained on all of your organization's standard operating procedures, including some designated as Sensitive but Unclassified, how do you ensure that your model does not disseminate this information to unauthorized individuals?

Managing Costs

AI models offer immense capabilities, but they come with significant costs. Whether engaging directly with an AI provider or licensing models through platforms like AWS's Bedrock, expenses can escalate rapidly if not monitored closely. Starting small can be beneficial, allowing organizations to gauge the costs associated with AI solutions through small-scale implementations before committing to widespread adoption.

Conclusion

Generative AI and LLMs present powerful opportunities for enhancing organizational capabilities. Bedrock offers a low-friction integration point to allow a host of services and solutions to take advantage of AI models. However, organizations must remain vigilant in securing AI solutions and addressing the specific vulnerabilities inherent in AI, particularly concerning data protection. Adequate budgeting for AI model access and monitoring usage are essential to avoid unexpected financial burdens.

Getting Started

AI technology is impressive, serving as a tool that, when utilized correctly, can significantly enhance operations. However, its misuse can result in organizations failing to realize promised benefits. Beginning with a focused pilot project with clearly defined success metrics is advisable, similar to previous technological advancements like containerization and cloud migrations. Organizations should carefully evaluate whether AI is the best tool for addressing a specific need, considering that a well-developed code-based solution might offer a more efficient and cost-effective alternative. To learn more about initiating AI adoption within your organization or to explore our efforts in supporting Federal customers with AI implementations, contact Simple Technology Solutions.