AI, Chatbots, Large Language Model
Security and Compliance Safeguards for Generative AI Enterprise Solutions
Enterprises Can’t Ignore Generative AI
It’s impossible to ignore the significance of Artificial Intelligence (AI), especially Generative AI, and how it is going to shape the future of business. In PwC’s Annual Global CEO Survey conducted in 2023 it was revealed that:
“44% of US CEOs see Gen AI boosting profit this year [2023]. Generative AI will boost employee efficiency, make products better and boost profits.”
The CEOs surveyed also recognized that competitive intensity was poised to increase, making it more difficult for a CEO to take a wait-and-see approach. However, despite this, many businesses are still apprehensive about how and where they should adopt GenAI and which Large Language Models (LLM) they should use.
Public LLMs or an In-House LLM?
With concerns around the use of public LLMs, such as OpenAI’s ChatGPT, some organizations are exploring the possibility of training their own domain-specific LLM. When considering whether to build their own Large Language Model (LLM) or utilize a public LLM, enterprises must weigh several pros and cons.
While building an in-house LLM allows for customization tailored to the specific needs and objectives of the enterprise, including domain-specific knowledge, training an LLM from scratch requires substantial resources and costs. This is not necessarily a business’s wheelhouse and risks the diversion of resources from the day to day business operations.
Many enterprises are wary of public LLMs for several reasons – security, compliance, and liability, being the biggest factors. With well documented cases of Gen AI hallucinating or responding with bias and inaccuracies, it’s no wonder that senior executives are wary, especially if they operate in regulated and sensitive industries where they handle large volumes of customer data and interactions.
While public LLMs have many advanced features and pre-trained models that can help get Gen AI solutions off the ground very quickly and inexpensively, they definitely raise the risk profile when exposed directly to customers through chatbots and other interaction channels. But it is also a safe assumption that with the pace of innovation in the LLM space, these risks are being addressed by the AI communities with potential solutions on the near term horizon. In the interim, the best path an enterprise can take is to partner with AI experts who understand the risks and can help implement the necessary safeguards while using public LLMs.
The Safeguards for Generative Ai Solutions for Businesses using Public LLMs
Redaction of Sensitive Data
To mitigate the risk of exposing sensitive information when utilizing public LLMs, businesses can implement robust redaction mechanisms. This involves identifying and masking sensitive data elements such as personally identifiable information (PII), financial details, or proprietary business information before feeding inputs into the LLM.
By redacting sensitive data at the input level, businesses can prevent inadvertent exposure of confidential information while still leveraging the capabilities of public LLMs.
Intent Classification Only
The big breakthrough in Generative AI is its ability to understand customer intent and the context of what the customer is looking for. This makes LLMs really good at intent classification and context extraction. This allows companies to adopt a hybrid approach to their use in chatbots, where the LLM can be used to understand and classify the customer intent, but uses a curated or pre-approved answer to respond to the customer. This removes the generative component and thus the risk of hallucination and/or unapproved responses.
The LLMs can also be fine-tuned on domain-specific data to improve their understanding of specialized terminology and industry-specific concepts. By training the model on data relevant to the specific domain or use case, the hybrid chatbot can learn to recognize and classify intents more effectively within that context.
Using RAG and Curated Responses
Utilizing Retrieval Augmented Generation (RAG) techniques and curated responses can further enhance the security and reliability of interactions with public LLMs. RAG combines the strengths of retrieval-based and generative AI approaches, enabling LLMs to generate responses based on indexed knowledge sources while maintaining accuracy and trustworthiness.
Businesses can leverage RAG to ensure that responses generated by public LLMs are based on curated knowledge bases or verified sources, reducing the risk of misinformation or inaccuracies. Additionally, by curating responses and providing predefined templates for common queries, businesses can exert greater control over the output of public LLMs, ensuring consistency and compliance with organizational standards.
Summarization for Internal Consumption
Large Language Models excel with summarization techniques and can be applied with great confidence especially for internal use cases that involve extracting key insights and information from large volumes of text or documents. By implementing summarization algorithms, businesses can condense lengthy documents, articles, or conversations into concise summaries, enabling faster decision-making and information retrieval.
By applying redaction techniques to summarized content, the likelihood of exposing sensitive or irrelevant information when interacting with public LLMs is reduced. This improves overall security and privacy of use date.
Compliance Insights
Generative AI can play a role in ensuring compliance with regulatory standards and industry guidelines. By analyzing vast amounts of data and documents, Generative AI can identify potential compliance risks, detect anomalies, and flag areas of concern for further investigation. GenAI solutions can analyze reams of customer service conversations, for example, and detect potential compliance issues and training opportunities. It can also work in a copilot mode and prompt a customer service agent to respond in ways that meet compliance standards.
Anti-Jailbreaking Guardrails
Implementing anti-jailbreaking guardrails is essential for safeguarding against malicious attacks or unauthorized access attempts. By deploying security measures such as redaction, access controls, domain relevancy checks, and anomaly detection, businesses can prevent unauthorized users from tampering with or compromising public LLMs. Additionally, regular security audits and monitoring can help identify and mitigate potential vulnerabilities or security risks, ensuring the integrity and reliability of LLM-powered systems.
Hosting
In terms of hosting safeguards, established public LLM providers like Google Cloud AI, Microsoft Azure AI, and Amazon Web Services (AWS) offer robust hosting safeguards, including encryption, access controls, and compliance certifications to ensure data security and regulatory compliance. These providers have extensive experience in hosting large-scale AI models and offer comprehensive security measures to safeguard sensitive information. In contrast, startup LLM companies may have fewer resources and less mature hosting infrastructure, potentially posing higher risks in terms of data security and reliability. However, even newer entrants are expected to improve their hosting safeguards over time.
Conclusion
The integration of Generative AI, particularly Large Language Models (LLMs), presents both opportunities and challenges for businesses navigating the AI landscape. To mitigate these risks, businesses can implement several safeguards when utilizing public LLMs and keep in mind that the rapid pace of innovation in the area of Large Language Models will address many of these challenges in the near future.