Generative AI is rapidly transforming the way we work, and organizations are racing to implement the new technology to accelerate productivity and growth, improve operational excellence, increase profitability, and ultimately deliver more value to their customers. Despite its promise, GenAI presents substantial challenges – especially when it comes to utilizing unstructured data, which represents 90% of all enterprise data. The ever-growing volume and variety of unstructured data and its numerous storage repositories can present many risks, including false relevancy in AI Models, inaccurate or outdated Gen AI outputs, and the potential exposure of sensitive data.
“92% of organizations believe GenAI requires new techniques to manage data and risk.” – Cisco
According to a 2023 report by Cisco, 62% of consumers worry about AI use, and 60% have lost trust in organizations due to AI practices. To ensure success with GenAI, trusted data is critical.
DryvIQ can position your organization to unleash the untapped potential in unstructured data by addressing the key concerns associated with AI models – data quality and data security. With a best-in-class unstructured data management platform, use a single system to discover, classify, label, manage, and confidently trust your enterprise data.
Enhance Unstructured Data Quality to Improve GenAI Outputs
Not all enterprise data should be used in the GenAI process, so it’s important to feed LLMs with the right data – but it can be difficult to sort through and find the “right data” at scale. With DryvIQ’s patented AI-driven platform, you can discover and classify unstructured data across the enterprise, then automatically cleanse and catalog your data to improve GenAI training data quality for more accurate and relevant outputs.
- Accessibility: Easily curate use case-specific document sets for training AI models using unstructured data across more than 40 storage repositories while managing and protecting sensitive data.
- Relevance: Reduce noise, biases, and hallucinations in training data by identifying and eliminating duplicate documents, trivial data, and stale data.
Ensure Unstructured Data Security to Prevent Unauthorized Exposure
A critical aspect of trusted data includes carefully managed access rights to prevent unauthorized exposure of sensitive information. This includes managing not just what users can access but also the data that GenAI models are trained on. Insufficient or inaccurate document classification labels and/or permissions greatly increases the risk of confidential information being surfaced through LLM outputs. With DryvIQ, you can analyze and automatically remediate sensitivity labels and user access rights at scale.
- Sensitive Data & Privacy: Detect and continually classify sensitive information, including intellectual property, employee and customer PII, financial information, and any other company-sensitive data.
- Access Management & Exposure Control: Analyze and manage which data users and GenAI models have access to, and proactively update access rights to prevent unauthorized exposure of company confidential information – internally, externally, or via GenAI response.
Power your GenAI journey with trusted data, deliver better outcomes, and ensure your unstructured data remains accessible, analyzable, and actionable with DryvIQ’s Unstructured Data Management platform.
Request a demo to see DryvIQ in action.
