AI without Guardrails is an open book
One of the biggest risks when dealing with AI is the absence of built-in governance. When you rely on AI/LLMs to automate processes, talk to customers, handle sensitive data, or make decisions, you’re opening the door to a range of risks:
- data leaks (and prompt leaks as we’re used to see)
- privacy breaches and compliance violations
- data bias and discrimination
- out-of-domain prompting
- poor decision-making
Remember March 2023? OpenAI had an incident where a bug caused chat data to be exposed to other users. The bottom line is that LLMs don’t have built-in security, authentication, or authorization controls. An LLM is like a massive open book — anyone accessing it can potentially retrieve information they shouldn’t. That’s why you need a robust layer of control and context in between, to govern access, validate inputs, and ensure sensitive data remains protected.
There is where AI guardrails, like NeMo (by Nvidia) and LLM Guard, come into the picture. They provide essential checks on the inputs and outputs of the LLM:
- prompt injections
- filtering out biased or toxic content
- ensuring personal data isn’t slipping through the cracks.
- out-of-context prompts
- jailbreaks
https://github.com/leondz/garak is an LLM vulnerability scanner. It checks if an LLM can be made to fail in a way we don’t want. It probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses.
What’s the link with Kafka?
Kafka is an open-source platform designed for handling real-time data streaming and sharing within organizations. And AI thrives on real-time data to remain useful!
Feeding AI static, outdated datasets is a recipe for failure — it will only function up to a certain point, after which it won’t have fresh information. Think about ChatGPT always having a ‘cut-off’ date in the past. AI becomes practically useless if, for example, during customer support, the AI don’t have the latest invoice of a customer asking things because the data isn’t up-to-date.
Methods like RAG (Retrieval Augmented Generation) fix this issue by providing AI with relevant, real-time information during interactions. RAG works by ‘augmenting’ the prompt with additional context, which the LLM processes to generate more useful responses.
Guess what is frequently paired with RAG? Kafka. What better solution to fetch real-time information and seamlessly integrate it with an LLM? Kafka continuously streams fresh data, which can be composed with an LLM through a simple HTTP API in front. One critical aspect is to ensure the quality of the data being streamed in Kafka is under control: no bad data should enter the pipeline (Data Validations) or it will spread throughout your AI processes: inaccurate outputs, biased decisions, security vulnerabilities.
A typical streaming architecture combining Kafka, AI Guardrails, and RAG:
Gartner predicts that by 2025, organizations leveraging AI and automation will cut operational costs by up to 30%. Faster, smarter.