Read this article with insights from S2E to discover how to address privacy, compliance, and fabricated content risks in Conversational Generative AI.
Conversational generative AI has changed the digital landscape forever. This technology harnesses the power of advanced AI models based on deep learning to generate human-like text in response to user prompts. Thanks to their ability to automate and streamline complex tasks in various business processes, virtual assistants are now more popular than ever. However, this remarkable progress has introduced a new set of challenges. Specifically, there are some concerns in terms of privacy, compliance, and fabricated content risks.
In questo articolo analizzeremo queste problematiche ed esploreremo GenerativeShield, una soluzione all'avanguardia progettata per affrontare questi problemi e fornire un ambiente sicuro e conforme per la progettazione di assistenti alimentati da AI. In this article, we will explore the challenges surrounding conversational generative AI and explore GenerativeShield, a cutting-edge solution designed to address those issues and provide a secure and compliant environment for GenAI-powered assistant design.
Conversational Generative AI vs. privacy and compliance: How to take the technology leap with No risks for brand reputation?
Conversational generative AI, also known as conversational GenAI, refers to a subset of artificial intelligence that focuses on creating human-like text as a response to human input. Under the hood, this technology relies on deep learning models—such as GTP-3, GPT-4, and LaMDA—to generate contextually reliable content.
Over the past few months, conversational generative AI has gained tremendous popularity. The reason is its ability to automate and simplify content generation and support through chat-like interactions. In particular, it empowers companies to achieve faster and more efficient results in corporate data retrieval, customer service, and many other business processes.
However, the rapid rise of conversational generative AI has led to significant privacy and compliance challenges. Some of the main concerns that businesses have with this technology are:
- How to prevent AI-powered assistants from accidentally disclosing sensitive information?
- How to ensure compliance with data protection regulations like GDPR when using generative AI?
- How to mitigate the risks of AI models producing fabricated content?
- How to tailor an AI conversational assistant to specific goals?
- What data security measures are required to protect confidential information when AI is involved in conversations?
- How can organizations be sure answers are crafted based on verifiable information?
None of GenAI’s current services offer reliable solutions to address these issues. The quest for a solution to these challenges inspired S2E to create GenerativeShield, a tool that aims to strike a balance between the efficiency of AI and the safeguarding of business and sensitive data. Learn more about it in the next section.
GenerativeShield: What it is and what it has to offer
GenerativeShield is an embeddable SaaS platform that enables companies to leverage conversational generative AI capabilities while ensuring data security and confidentiality. Its primary goal is to empower organizations to develop their own private intelligent assistants and seamlessly integrate them into their corporate systems to address their specific requirements.
In detail, GenerativeShield supports the creation of intelligent assistants. Each assistant is designed to achieve a specific scope, with its own area of expertise and capabilities. The context, behavior, and tone of voice of those assistants can be configured during the initial design through a guided procedure that involves no code at all.
By feeding your corporate knowledge base to these virtual assistants, they will learn how to deliver accurate and contextually relevant responses. These unique agents powered by GenAI can be used for customer service, internal team support, and much more.
To avoid vendor lock-in concerns, the platform does not rely on a single GenAI model but supports a wide range of top-tier models. Check out the infographic to learn more about its software architecture.
How GenerativeShield enrures privacy and compliance in Conversational Generative AI, minimizing fabrication risks
It is time to find out how GenerativeShield succeeds in solving the privacy, compliance, and fabrication issues posed by conversational generative AI.
Mitigate the risk of sensitive data exposure with prompt moderation
GenerativeShield offers a prompt moderation feature to reduce the risk of sensitive data disclosure when interacting with the platform. This feature allows companies to define specific filters designed to identify and alert users when sensitive data is detected within prompts.
In other words, organizations have the flexibility to conceive their own prompt moderation policies. Companies can also specify whether sensitive data should be blocked or whether users should be granted approval to share data. This level of control ensures that data privacy is maintained without compromising the user experience.
This alert-based filtering system raises awareness about the importance of data confidentiality within the corporate user base, promoting a culture of responsible data handling when injecting information into prompts.
Keep in mind that moderation can also be applied to responses. In this way, organizations have greater control over the quality and content provided to users by virtual assistants. For example, they may decide that responses should not contain personal information, discrimination, crimes, financial data, and more.
AI-Powered Conversational Agents equipped with enterprise-grade level security and confidentiality
GenerativeShield supports the design of AI-powered conversational assistants tailored to specific business needs. Companies can use the platform to define a new assistant customized to their goals. The result will be a GenAI-based embeddable conversational agent that provides accurate answers backed by the company’s data.
Keep in mind that GenerativeShield is fully GDPR-compliant. Also, it offers a built-in secure infrastructure and data privacy system so that organizations do not have to worry about that.
Since a specific model may not best fit a specific use case, the platform integrates some of the most powerful GenAI models. As of this writing, the available models include OpenAI GPT-3.5 and GPT-4, Aleph Alpha, Ai21 Labs, and Cohere. This model-agnostic nature makes sure that the assistants are not bound to a single AI model, providing full freedom on which models to use and eliminating switching costs.
Design, Set, and deploy GenAI-Powered Conversational Assistants in production with No Code required
When designing a new agent in the GenerativeShield platform, companies can set its tone, personality, and behavior to ensure that it aligns with their brand and can best support the specific users it should assist.
The next step is to use the intuitive environment in the platform to upload corporate knowledge bases just by dragging and dropping them. Once done, the particular KBs can be linked and matched to the existing agents. Each AI-powered assistant will use this data to provide precise answers to corporate-specific queries. Using a variety of vector search techniques, the assistant can also explain where she found the information it used to craft the response. Note that the process of defining assistants does not involve a single line of code! This means that even non-technical users can use GenerativeShield.
What is crucial to point out is that the SaaS solution is not another independent platform. Quite the contrary! Once an assistant has been deployed, it can be embedded and integrated into any enterprise environment through either a widget extension or via API. Achieving mutually enhanced cooperation in a composable architecture is actually one of the key goals of GenerativeShield.
Summary: main features and benefits of GenerativeShield
In short, the main features offered by GenerativeShield are:
- Self-service mode for every use case: Build AI conversational assistants that can cover any particular business use case, with many AI models available.
- No-code AI design approach: Easy-to-use user interface to achieve AI agent design goals easily and intuitively.
- AI integration: Integrate AI-powered agents into corporate systems via API or widgets.
These lead to the following benefits:
- Increased efficiency and productivity: Use GenAI agents to automate tasks through their precise and context-aware responses.
- Eliminate risk exposure: Reduce the risks of sensitive data exposure via an integrated security and compliance system while making sure that organizations remain compliant with data protection regulations
- Faster time-to-market: Accelerate implementation of GenAI-powered conversational agents.
If you need customizable, secure, regulatory-compliant, and embeddable conversational AI assistants, GenerativeShield is the right solution for you!.
Conclusion
In this article, we delved into the realm of generative conversational AI, exploring the profound impact it is having in various domains, but also highlighting its privacy and fabrication concerns. To address these challenges, we explored GenerativeShield, an innovative no-code SaaS platform that makes it easy to design and deploy AI-powered embeddable conversational agents that are secure, customizable, and data protection compliant. Thanks to it, organizations can navigate the rapidly changing AI landscape with confidence, without compromising ethics or compliance.
Fonte: Codemotion
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