Cloaked AI - 2026 Pricing, Features, Reviews & Alternatives

Cloaked AI is a data-in-use encryption software solution that enables organizations to protect sensitive vector embeddings in AI systems while maintaining full query and search functionality. Developed by IronCore Labs, this application-layer encryption platform addresses security vulnerabilities in generative AI workflows that involve vector databases in machine learning operations. The product serves enterprises in healthcare, financial services, legal, and other regulated industries that require data protection for AI projects utilizing private, confidential, or personally identifiable information. The platform implements searchable encryption at the application layer to support AI recommendation systems, retrieval augmented generation architectures, biometric systems, and semantic search applications without exposing sensitive data to unauthorized access, breaches, or cloud misconfigurations.

The platform operates through an open-source software development kit available in Python, Java, Kotlin, and Rust that encrypts vector embeddings and associated metadata before storage in any vector database including Weaviate, Elastic, Pinecone, Qdrant, and pgvector. The encryption process requires a few lines of code to call an encrypt function before storing vectors and to call the same function when encrypting search queries. Cloaked AI offers three metadata encryption options in which random encryption protects data that does not require querying, deterministic encryption supports exact match filters, and pass-through leaves fields unencrypted. The software supports both vector encryption for AI search data and model encryption for training datasets with encrypted inputs that ensure models are usable only with the relevant encryption key. All encryption and decryption operations occur within the customer infrastructure to guarantee that IronCore Labs never accesses unencrypted data, encryption keys, or sensitive information. The solution can operate in standalone mode with offline functionality and local key management or integrate with SaaS Shield for centralized key management in multi-tenant environments.

Cloaked AI integrates with existing AI and machine learning infrastructure through its database-agnostic architecture that allows organizations to choose vector databases based on performance, cost, or feature requirements rather than security limitations. The software addresses multiple AI security use cases such as recommendation systems that suggest related items based on historical data, retrieval augmented generation systems that answer queries using private knowledge bases, encrypted models trained on protected data, biometric recognition systems, anomaly and fraud detection platforms, reverse image search applications, and semantic text search engines. The encryption implementation guards against vector inversion attacks that expose source data including diagnoses, biometric identifiers, and personal information embedded in vector representations. Encryption processing time is minimal compared to network latency in most deployments, although key management strategies influence overall system performance depending on whether keys are cached locally or fetched from remote services.

The platform is distributed under a dual-license model that combines an AGPL open-source license for GPL-compatible applications with commercial licenses for proprietary software implementations. The ironcore-alloy software development kit provides core functionality with transparent algorithm selection and publicly available source code on GitHub for review by security and cryptography researchers. Cloaked AI supports compliance with GDPR, HIPAA, and emerging AI regulations through technical measures that maintain data protection throughout its lifecycle while preserving essential AI functionality in production systems. The solution addresses privacy and security concerns that often impede AI project deployment by enabling the use of hosted vector database services and cloud infrastructure without exposing sensitive data to foreign subpoenas, service provider employees, or breaches of infrastructure encryption.