Late morning, it was clear: AWS GenAI Data Day on 15th October 2024 accelerated with Snowflake once again. Dan Hunt, Principal Partner Sales Engineer at Snowflake took the audience through the “Accelerate GenAI with Amazon Bedrock and Snowflake” session where he focused on Snowflake platform integration as a comprehensive toolkit to build and deploy GenAI applications. A key selling point highlighted throughout was the perceived ease of use of Snowflake thanks in part to “Streamlit’s capability to animate data and GenAI models through user-friendly applications.”
Cortex Guard comes to life
Snowflake’s objective as explained by Hunt is to deliver both technical efficiency and trusted security to its customers, whilst promising to maximize the potential for efficient data processing through advanced architectures Arctic Embed and Arctic TILT. These innovations aim to enhance performance by integrating and enabling tilt-based intelligence layering for seamless operations into applications. Cortex Guard is further expected to reinforce security parameters demanded by responsible AI builders by eliminating unwanted responses, ensuring that sensitive business data remains within a customer’s control and is not used for retraining AI models.
Whilst safety measures offered by Cortex Guard filter harmful content to minimize risks, according to reports from Payatu, a team of “ultra-talented cybersecurity professionals, who get out of bed each morning looking for a challenge that can only match up to them,” governance practices must align with (the Open Source Foundation for Application Security), OWASP’s Top 10 vulnerabilities for LLMs. Founders should always take care to test any chosen LLM outputs that may result in security lapses, such as code execution that jeopardises systems, if they are not properly validated. In certain situations, according to the Payatu team who are experts in research powered cyber security services (XSS), this vulnerability may result in remote code execution on backend systems, Cross-Site Request Forgery (CSRF), Cross-Site Scripting and Server-Side Request Forgery (SSRF).
As reported in the San Francisco Standard in December 2024, November 2024 marked the timeframe when “Snowflake (now located at Menlo Park), bolstered its AI position by announcing a partnership with Anthropic to bring the company’s Claude model onto its platform.”
Streamlit integration
Streamlit integration enables rapid application development with minimal Python code, allowing developers to create dropdown forms, interactive graphs, and chat box features efficiently as highlighted by Dan Hunt, Principal Partner Sales Engineer at Snowflake. By deploying Bedrock agents, Snowflake enhances access to the AWS ecosystem, unlocking advanced AI capabilities such as reverse geo-coding, translation models, and document extraction. Such features ensure that tech Founders are able to push forward exciting applications without the lag time of build and deployment.
According to reports in Medium, Streamlit and Bedrock Deployment is facilitated thanks to the data storage capacity levels on Snowflake that can fuel Bedrock’s machine learning models. Streamlit serves as a user-friendly interface, delivering insights through interactive and visually appealing dashboards.
Snowflake Dataset Analysis Through Foundation Models
Native Bedrock integration empowers Snowflake with advanced foundation models, enabling businesses to process and analyze complex datasets efficiently. This integration supports content filtering, translation, and robust data handling. However, reliance on the AWS ecosystem may limit multi-cloud flexibility on occasion according to some technical reports, and the non-deterministic outputs of large language models (LLMs) necessitates well thought out strategies to ensure reliability and transparency are maintained. Testing report outputs for small language models (SLMs) are yet to be determined.
Snowflake and Bedrock Responsible AI Theory in Practice
Generative AI offers transformative potential but also presents risks including biases, harmful outputs, plus privacy challenges. To take full advantage of Responsible AI best practice while minimizing risks through the adoption of principles such as fairness, privacy and governance, Hunt explained how the AWS integration with Snowflake is expected to deliver positive outputs. The Latesale.com team would equally highlight the benefits of implementation field practices through Amazon SageMaker and Bedrock.
Verification parameters for foundational model testing would include assessments based on fairness, relevance, robustness and transparency tools such as invisible watermarking that embeds watermarks into text without altering semantic meaning. These guardrails are destined to improve customer traceability and trust dynamics.
The new watermark detection capability of Amazon Titan Image Generator is currently widely accessible in Amazon Bedrock. By default, every image created by Amazon Titan has an undetectable watermark. Using natural language cues, the watermark detection method enables AI model testers to recognise photographs produced by Amazon Titan Image Generator, a foundation model that helps customers produce realistic, studio-quality images in huge quantities and at a reasonable cost.
Bedrock’s guardrails should also add an additional layer of protection and mitigate risks by filtering harmful content. From a use case point of view, this methodology is workable for FinTech, HealthTech and MedTech start ups, where data sensitivity issues persist. The removal feature of divisive political content from opinion pieces may or may not serve as essential depending on a content creator’s ethos.
Concluding Notes: OWASP Deployment in Action
Educating developers, designers, architects, managers, and organisations about the possible security concerns associated with the deployment and management of Large Language Models (LLMs) and Generative AI systems is the goal of the OWASP Top 10 for Large Language Model systems Project.
A variety of resources are offered by the initiative that Founders can cross check when building AI models. Particularly noteworthy is the OWASP Top 10 list for LLM applications, which identifies the top 10 most serious flaws frequently found in LLM applications and emphasises their possible consequences, ease of exploitation, and frequency in practical applications.