ReclaimLLM offers advanced AI data security and Data Loss Prevention (DLP) solutions specifically designed for developer tools like Cursor, Claude Code, and other AI coding assistants. It aims to prevent sensitive information such as .env credentials, API keys, and Personally Identifiable Information (PII) from being leaked to Large Language Models (LLMs).

• Pre-execution Hook DLP Secret Masking: ReclaimLLM utilizes native hooks to inspect tool inputs and file paths matching .env, *.env, and .envrc locally before execution. Sensitive data like API keys, passwords, and tokens are replaced with placeholders (e.g., `[REDACTED:API_KEY]`) before they are processed by the model or transmitted over the network.

• Automated Sensitive Session Flagging: Sessions are automatically flagged as sensitive if they contain environment variables, infrastructure secrets, cloud IAM configurations, database connection strings, or PII. These flagged sessions are quarantined from public views and highlighted for security review.

• Zero-Training Policy: ReclaimLLM strictly enforces a zero-training policy, ensuring that user code, prompts, and session history are never used for model training by ReclaimLLM or any third-party services.

• Enterprise LLM Gateway: Provides a governed gateway with team-scoped credential isolation, allowing for centralized management of provider credentials and enforcement of model policies.

• Private VPC Deployment: Offers deployment options within a private VPC using Docker and Kubernetes Helm charts, ensuring that session data remains within the organization's perimeter.

• AES-256 Session Blob Encryption: All captured session data is encrypted at rest using AES-256 encryption, with customer recovery keys available for enhanced security.

• Session Observability and Audit Trails: Provides comprehensive session observability, including real-time audit trails, step-by-step replay of sessions, and detailed analysis of token spend and consumption by team, developer, and model.

• Context Compression: Implements active context compression techniques to reduce prompt tokens, leading to potential cost savings and improved efficiency.

ReclaimLLM provides a unified security console for real-time session auditing and sensitive data classification, making it an essential tool for CISOs and security teams to maintain compliance and protect intellectual property.