The rise of generative AI is transforming enterprises, but with it comes unprecedented risks to sensitive data. According to PwC, 35% of organizations are already deploying AI solutions, and the global AI market is expected to reach $190.61 billion by 2025.
As AI adoption accelerates, so does the complexity of managing and securing data pipelines linked to these powerful models. Traditional data protection methods struggle to keep pace with the dynamic nature of LLMs, leading to increased exposure of valuable and sensitive data. This is where Normalyze steps in—offering a cutting-edge Data Security Posture Management (DSPM) platform designed to safeguard AI environments from evolving risks, ensuring businesses can confidently embrace AI technologies without compromising on security.
Normalyze is leading the DSPM market with both AI-driven capabilities within our platform and solutions specifically designed to address AI use cases in customer environments. Regardless of the maturity of those use cases, the Normalyze platform is built to support teams as they grow and evolve their AI sophistication.
Normalyze’s DSPM platform scans data environments in the cloud, on premises, and in IaaS, PaaS and Saas environments for known and shadow data stores, mapping AI data pipelines and identifying the risks of LLM access to sensitive information. This proactive approach allows organizations to protect data from unauthorized access, data poisoning, and other emerging threats as AI technologies evolve.
Context-Aware Data Discovery
Utilizes LLMs to understand nuanced contexts, such as distinguishing between personal names and similar entities (e.g., “John Smith” vs. “Smith Street”), enabling more precise identification of sensitive data types.
Comprehensive Sensitive Data Detection
Identifies sensitive data across AI and machine learning environments, including in complex, unstructured data formats and advanced workflows like Retrieval-Augmented Generation (RAG), ensuring full visibility into where sensitive data is processed.
Hybrid Data Classification Accuracy
Combines the power of regular expressions, NLP models, and LLMs to improve accuracy and reduce false positives, especially in context-sensitive or unstructured data. This hybrid approach ensures cost-efficient, performance-optimized data classification.
Model and Workflow Protection
Monitors and flags sensitive data imports into machine learning models (Azure ML, Google Vertex, AWS Bedrock) and RAG workflows, providing advanced security and ensuring compliance across AI processes.
Advanced LLM Security
Secures LLM applications across four critical interfaces (training data, Retrieval Augmentation Generation, user prompts, and prompt responses) using dedicated APIs, preventing unauthorized access or output of sensitive data.
Real-Time API-Driven Security
Provides APIs for real-time scanning and classification of data, allowing organizations to detect and prevent sensitive data leaks during AI interactions, such as chatbot queries or model training, ensuring privacy and compliance.
Normalyze’s DSPM platform delivers efficient, cost-optimized data classification by leveraging a multi-stage process that minimizes reliance on LLMs. By combining regular expressions, NLP models, and LLMs for more complex data, the platform optimizes both cost and performance.
As AI continues to evolve, so do the threats that accompany it. Normalyze’s robust DSPM platform provides the foundational visibility, control, and protection that organizations need to fully unlock the potential of AI—safely and securely. Whether you're just beginning your AI journey or scaling complex AI-driven workflows, Normalyze is the trusted partner that ensures your data remains secure every step of the way.
Visit www.normalyze.ai for a copy of the Gartner Cool Vendors in Data Security report today.