Richard Davies, UK Managing Director at Netcompany, explains how businesses can leverage Search Augmented Generation to protect their data from the security risks posed by generative AI.
As the desire to deploy generative AI deepens, control and management of data will be essential for businesses to build systems that deliver value without compromising safety. However, concerns about data vulnerabilities remain a fear for many businesses and their customers.
A recent report assessing concerns about generative AI found that organizations are most concerned about data privacy and cyber issues (65%) and employees making decisions based on inaccurate information (60%) , employee misuse and ethical risks (55%). . But these concerns don’t have to stifle AI innovation.
Search Augmentation Generation (RAG) tools have the ability to screen and fine-tune the inputs and outputs of large-scale language models (LLMs), acting as a great intermediary between business processes and AI models.
Think of it like a sandbox that filters the objects allowed inside. RAG tools enable employees to securely capture information from external, innovative AI models while capturing internal information that aligns with organizational processes.
So what should companies be aware of when deploying RAG tools for generative AI? And what are the benefits of RAG tools for ensuring security and privacy?
Putting the workforce first
Companies need to think strategically in the early stages of AI implementation to prevent unnecessary risks. One way to prevent that is to communicate early with employees about how and where generative AI can help.
Employees are already experimenting with AI tools, so companies can’t afford to act too late.
Without guidance in place, individuals naturally end up unknowingly putting themselves and their businesses at risk. Security must also be easy and accessible, especially since 66% of employees say they prioritize day-to-day operations over cybersecurity.
RAGs can be integrated within your business systems and exist alongside your current tools and processes. No one wants the hassle of going back and forth to get answers. AI enhances productivity, not hinders it.
Establishing ownership and trust
Collaboration is an essential element of safely using generative AI, whether or not you use RAGs. The actions of just one individual can destroy plans for maintaining security. A famous case involves a Samsung employee uploading sensitive code to his ChatGPT, accidentally leaking trade secrets.
Compliance with the General Data Protection Regulation (GDPR) is an essential part of implementing generative AI.
Businesses need to understand how their data is processed by AI, where it is stored, and who can access it. This should also be incorporated into the internal responsibilities assigned within the business.
Organizations must establish a framework that prioritizes privacy in data management, proactively providing employee education and training, and minimizing unnecessary stored data.
Businesses need to quickly establish what kind of data they own and who should own what. Data owners can determine what information is not sensitive and advise what data can be declassified. This may mean editing your name, location, company name, etc.
Enhancing your LLM with search enhancement generation tools that can identify sensitive data specific to your business, you can also deploy advanced algorithms that can automatically anonymize sensitive information.
With RAG, companies no longer need to share raw data with model providers. Instead, it can be processed internally first to ensure that only the intended data is sent.
avoid hallucinations
Like the brain, when an AI model is missing information, it tends to try to fill in the gaps to find an explanation, even if it’s not completely accurate.
Search extension generation allows business documents and data to be combined with information from external sources and incorporated into the system. Having additional sources of knowledge retrieval is one of the best solutions to AI producing false information known as hallucinations.
It is important to ensure that the output is based on reality and not speculation. This enhancement gives teams more control over the datasets from which language models are extracted.
When language models are not strong, hallucinations become more common as the technology attempts to understand unclear patterns and reconcile inconsistencies.
Depending on the industry, companies need to be aware that phantom outputs can further exacerbate security risks for their organizations. Reliance on inaccurate data in socially important areas can lead to poor decision-making, unauthorized access to systems, misallocation of resources, and inaccurate analysis.
Aspects such as inaccurate analysis can also lead to employees acting on incorrect recommendations.
Optimizing search extension generation for reliable answers
As with other techniques, improving the RAG model is a continuous process. RAG tools excel at ingesting business documents and data and can provide users with relevant and specific responses.
However, it can be less effective if users don’t need a specific answer and are looking for all answers. For example, when a user asks, “Tell me about our content on technology infrastructure,” and when you ask, “Show me a list of all design documents.”
To address this challenge, RAG models must be designed to recognize user intent and search by user intent.
Understanding intent allows RAG tools to tailor responses based on the nature of each query and share the most appropriate information.
Companies can also introduce other ways to query data. Semantic search is currently the most popular approach, but it can struggle with ambiguity and focus too much on precisely matching each query to existing knowledge.
Teams can optimize semantic search in combination with other methods such as keyword-based search.
Generative AI Sandbox
If companies want to overcome fear of AI in the future, they need to take back control of the data used to fuel it.
If leaders can implement a way to manage and own data, they can better implement sandbox systems like RAG, allowing teams to safely innovate in a way that fits within existing business processes.
