Encryption at rest and in transit is no longer sufficient.
The critical vulnerability lies in data in use - when it is analyzed, modeled, or shared. This is precisely where AIDatacy's Privacy-Enhancing Technologies (PETs) become essential.
AIDatacy operationalizes advanced PETs, allowing processing on encrypted data and privacy-first learning in distributed database to be executed across organizational boundaries. Data remains protected throughout its lifecycle- not only at rest or in transit but also in use.
The AIDatacy platform is structured across three integrated technological layers:
Supports secure analytics, federated learning, and encrypted model inference. Enables data processing without decryption or exposure.
Facilitates multi-party computation workflows while maintaining data residency and security boundaries. No raw data is moved or replicated.
Implements fine-grained access control, embedded policy enforcement, and full auditability across all computation nodes and data participants.
AIDatacy's OpenSky Marketplace enables businesses to monetize and collaborate on encrypted audience data - without ever exposing raw data.
Designed with privacy-safe principles, it leverages AIdatacy's Privacy-Enhancing Technologies (PETs) such as Fully Homomorphic Encryption (FHE), Federated Learning and Trusted Execution Environment (TEE) and more ... to enable secure data combination, analytics, and activation across ecosystems.
Unlock your data value with AIDatacy's Privacy-first Data Collaboration Platform using AIdatacy's Privacy-Enhancing Technologies (PETs) such as Fully Homomorphic Encryption (FHE), Federated Learning and Trusted Execution Environment (TEE) and more ...
Key Platform Capabilities
Run SQL-like queries across distributed or encrypted datasets, without moving data or revealing it.
Query logic and results remain protected throughout the process using Homomorphic Encryption or TEE depending on context and use cases.
Train, evaluate, and deploy AI/ML models, including LLM fine-tuning, on sensitive or proprietary datasets using Federated Learning or encrypted compute. Models and data both remain confidential, even during inference.
Define granular permissions and data sharing policies at user, field, or partner level.
AIDatacy enforces governance natively at runtime, making data collaboration policy-aware and auditable by design.
Standardize, align, and map datasets across organizations without exposing structures or values.
Our tools support secure schema sharing and compatibility negotiation, critical for pre-processing in federated or encrypted workflows.
Ensure complete visibility across all data operations with cryptographically verifiable logs and detailed usage traces.
This guarantees proof of accountability and helps meet regulatory and compliance requirements (e.g., GDPR, ISO 27001), while reinforcing trust with partners, clients, and auditors. AIDatacy's system is designed to facilitate audits and streamline the path toward future certifications by maintaining a transparent, tamper-proof record of all activities.
Use Cases
While encryption at rest and in transit is now a standard baseline, a critical vulnerability remains when data is actively processed whether for analysis, model training, or sharing. This “data-in-use” gap creates serious risks: it opens doors to breaches, adds regulatory complexity, and slows down secure collaboration.
AIDatacy bridges this gap by embedding privacy directly into the computation layer. Instead of treating privacy as a passive safeguard limited to storage or transfer, it becomes an active, built-in condition for every analytical or collaborative interaction involving sensitive data.
With AIDatacy, privacy is enforced dynamically at runtime, not through static controls.
Access permissions, usage constraints, and policy rules are applied directly during execution, ensuring continuous protection of data and models, even in distributed, multi-party environments.
Advanced cryptographic methods such as Homomorphic Encryption, Federated Learning, and Trusted Execution Environments, are integrated at the system level. These PETs are abstracted for easy adoption, allowing organizations to enhance privacy without disrupting existing tools or workflows in data science, analytics, or governance.
Built for decentralized and multi-party ecosystems, AIDatacy ensures consistent governance, traceability, and access control across technical infrastructures, organizational silos, and international borders.
