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Azure Confidential Computing

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What is SecuredAI?

SecuredAI is a privacy-first AI security platform that helps organizations safely use generative AI by automatically protecting sensitive information before it leaves the user's device. Rather than relying on manual redaction or blocking AI usage altogether, the platform intelligently detects confidential information within prompts and replaces it with context-aware placeholders that preserve the meaning of the conversation. Once the AI model generates a response, SecuredAI restores the original information locally on the user's device so users receive fully contextualized answers without exposing sensitive data to the AI provider. Its Zero-Knowledge Vault architecture encrypts sensitive information locally using a master key that is never stored on SecuredAI's servers, ensuring the platform cannot access or recover customer data. Detection capabilities include names, email addresses, phone numbers, organization names, account numbers, personal identifiers, and custom patterns that organizations can configure for their own sensitive information. Context-preserving obfuscation allows AI models to continue producing accurate responses while preventing exposure of confidential values during processing. The platform currently integrates with OpenAI and DeepSeek, with additional support planned for Grok, Claude, Magic, and other AI ecosystems. SecuredAI is designed for organizations operating in healthcare, financial services, legal, and enterprise environments where privacy, regulatory compliance, and secure AI adoption are essential. By eliminating the tradeoff between usability and security, the platform enables teams to benefit from AI while reducing the risks associated with data leaks, unauthorized disclosure, and shadow AI usage. Its local restoration technology distinguishes it from conventional redaction tools by preserving productivity while maintaining strong privacy protections.

What is Azure Confidential Computing?

Azure Confidential Computing significantly improves data privacy and security by protecting information during processing, rather than just focusing on its storage or transmission. This is accomplished through the use of hardware-based trusted execution environments that encrypt data in memory, allowing computations to proceed only once the cloud platform verifies the environment's authenticity. As a result, access from cloud service providers, administrators, and other privileged users is effectively restricted. Furthermore, it supports scenarios like multi-party analytics, enabling different organizations to collaborate on encrypted datasets for collective machine learning endeavors without revealing their individual data. Users retain full authority over their data and code, determining which hardware and software have access, and can seamlessly migrate existing workloads using familiar tools, SDKs, and cloud infrastructures. In essence, this innovative approach not only enhances collaborative efforts but also greatly increases trust and confidence in cloud computing environments, paving the way for secure and private data interactions across various sectors.