Learn why GenAI needs Guardrails, and how to implement them!

Data Privacy and Hallucinations

With Generative AI set to automate so much of companies' workloads in the near future, it's important to consider risks which it may pose to organizations looking to adopt it.

One of the largest concerns is around data privacy when dealing with sensitive information, in addition to hallucinations. As outlined in blogposts on Alignment and Hallucinations, some of these issues are major liabilities for an organization that doesn't take steps to secure their LLM pipelines.

Generative AI becomes dangerous if it provides access to sensitive IP to end-users or even employees who do not have clearance, and incredibly inconveniencing if it starts concocting untrue information or falsehoods. Both risks pose such a roadblock for enterprise AI adoption that many companies which could profit unbelievably from AI are slow to gamble on these.

Guardrails Solution:

Safeguard Sensitive Data and Preserve Integrity

Large language models are often fine-tuned or "trained" on thousands if not hundreds of thousands of documents in order to provide value to businesses. Because of the sheer volume of training data required, it is often not possible to know what information the model has learned, nor how much of it may be sensitive information, e.g. financial, legal or personal information. It is, however, possible to restrict the responses provided by the model to only provide non-sensitive information.

We offer a solution for ensuring that when a question is asked, the model only replies based on data in a document store (or Vector Store) that management has deemed safe for consumption by the end-user.

This approach gives companies a way to mitigate sensitive or copyrighted data from entering a chat with an end-user, while also eliminating hallucinations by ensuring that all AI replies are grounded in correct information, rather than generating random data which may be false.

Small Language Models as a Security Guardrail

For particularly sensitive chat-applications, it is also possible to leverage the capabilities of smaller language models to identify malicious requests based on intent. This is the Generative AI analogue to "input sanitation" for HTTP requests in cybersecurity practice for protecting input forms on web-pages from malicious code injections by hackers. Generative AI chatbots are quickly replacing input forms on webpages, and so they also need a certain level of security. While hackers can't perform a SQL injection on a chatbot, they could prompt the chatbot with a request for sensitive data.

Benefits to Using Guardrails

Perhaps the largest roadblock to GenAI adoption currently is its data governance and information integrity concerns. By implementing Guardrails, a company can scale its AI capabilities without limit, while ensuring optimal user experience. EquoAI provides services for optimization of LLM response quality, in addition to balancing this need with security and data governance concerns.

Without having to worry about sensitive information being leaked by an LLM, it is now possible for companies to take full-advantage of their training data for training and developing their own in-house Generative models. Additionally, this mitigates the risk of facing lawsuits.

Sensitive IP being leaked is also now not a concern- your models will not be at risk of compromising your proprietary data.

One fourth and final key benefit to Guardrails is the fact that in regions such as the EU which have strict data-governance and privacy compliance rules, a business can now scale its Generative AI applications limitlessly without worrying about breaking one of these laws. Enormous productivity gains can be reaped, much backroom work automated, and money saved on incurring large fines. A business in the US, UK, Canada or elsewhere can also benefit from this relief once more stringent regulations around AI are put in place in these countries

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