The Intelation Trust Framework

Comprehensive Privacy and Data Protection Architecture

Framework Overview

The Intelation Trust Framework consists of Intelation Nexis, which is the foundational privacy and compliance layer delivering real-time processing of the data, governs and regulates dataflows to meet compliance standards, enforcing regulatory and geo-regulatory logic, and real-time visibility into your dataset's privacy and compliance posture.

Intelation Diffyn and Intelation HElios function as advanced Privacy-Enhancing Technology layers, delivering differential privacy and encrypted computation safeguards across key stages of the machine learning workflow. They safeguard data at the feature, gradient, and computation level, with granular configuration parameters that maximize analytical fidelity while preserving rigorous privacy and security constraints.

Layered Protection

Intelation delivers a layered approach to data protection and privacy operations.

Nexis provides the foundational governance layer — identifying sensitive data, enforcing regulatory rules, and maintaining audit-ready logs. Diffyn applies differential privacy to analytics and ML workflows, enabling insight extraction without exposing individuals. HElios enables encrypted computation for zero-trust environments, keeping data protected even during processing. This combination helps organizations meet compliance obligations, minimize data exposure, and safely adopt advanced analytics and AI without compromising privacy or security.

Intelation Nexis

Nexis — A connected, next-generation platform for privacy and compliance.

Value Proposition

Nexis delivers dynamic privacy governance by automatically flagging and detecting PII, enforcing regulatory logic as per what is chosen, and ensuring continuous compliance across pipelines, apps, and AI systems.

Capabilities

Real-time data processing & compliance regulation
Region-aware compliance selection
Automated privacy testing & risk scoring
Immutable audit-grade logs
Role Based Access Management
Integration with PET techniques

Use Cases

Healthcare
Legal
Finance
Government & Public Sector
Pharmaceuticals & Research
Retail & e-commerce

Why Nexis?

Privacy and compliance can't be manual anymore. Nexis brings real-time, automated governance into your data and AI workflows. It transforms privacy from checklist-driven processes into an operational, always-on governance layer with real-time monitoring and immutable audit visibility.

Technical Deep Dive

Real-time PII detection for pipelines and ML

Nexis exposes an API that identifies sensitive information in streaming data, ETL workflows, and model training inputs using transformer-based NER models optimised for accuracy and low latency.

Policy-as-code for automated governance

Developers can define governance and compliance rules using JSON/YAML policy packs, enabling GitOps workflows that control how sensitive data is handled across the organisation.

ML-aware governance hooks

Nexis provides pre-training, pre-processing, and pre-inference hooks to scan datasets, embeddings, and model outputs, ensuring AI pipelines remain compliant automatically.

Webhook-triggered remediation

When Nexis detects sensitive attributes, it can immediately trigger remediation via redaction, masking, differential privacy, or encrypted computation — without manual intervention.

Connectors for modern data stacks

Nexis integrates seamlessly with Kafka, Snowflake, Databricks, S3, GCS, BigQuery, and other core data systems, allowing governance to be embedded directly in data engineering workflows.

Intelation Diffyn

Diffyn - Attain Privacy with engineered randomness.

Value Proposition

Diffyn operationalizes differential privacy by injecting mathematically calibrated noise into datasets which can then be used in machine learning workflows. Injecting the noise will still preserve analytical accuracy whilst protecting sensitive information. This allows organizations to train high-utility AI models and work with third parties securely, without putting sensitive data at risk.

Capabilities

Differentially private analytics that keep the results even while keeping the sensitive data protected
Privacy-preserving machine learning training using DP-SGD to prevent data leakage through memorization, reconstruction, or extraction
Generate synthetic datasets that maintain statistical fidelity for development, testing, or collaboration
Configurable epsilon controls, balance accuracy, and protection and automated privacy loss accounting for audits
Automatically combine Diffyn with Nexis (governance) and HElios (encrypted computation) for multi-layered privacy protection

Use Cases

Healthcare
Legal
Finance
Government & Public Sector
Pharmaceuticals & Research
Retail & e-commerce

Why Diffyn?

Diffyn brings mathematically engineered differential privacy into your analytics and machine learning workflows, ensuring individuals remain protected without sacrificing accuracy.

Diffyn becomes the dedicated privacy layer for safe data use — enabling teams to analyze sensitive datasets, train models, and share insights with confidence. It injects calibrated noise to prevent re-identification, preserves statistical utility for high-value workloads, supports privacy budgets through epsilon controls, and provides formal, regulator-aligned guarantees that masking or heuristic methods simply cannot offer.

Technical Deep Dive

DP-enabled analytics and reporting

Diffyn allows developers to wrap queries and outputs with differential privacy mechanisms, applying calibrated noise to preserve accuracy while guaranteeing individual-level protection.

Privacy-preserving model training

Through DP-SGD integration, Diffyn adds noise at the gradient level during ML training, preventing models from memorizing or leaking sensitive details from the underlying dataset.

DP synthetic data generation

The platform generates synthetic datasets that retain useful statistical patterns while providing provable ε-privacy guarantees, allowing teams to share and test data safely.

Configurable epsilon and privacy accounting

Developers can choose epsilon values and track cumulative privacy loss across queries or training epochs, making differential privacy measurable, auditable, and regulator aligned.

ML-ready privacy integration

Diffyn supports differential privacy across embeddings, vector databases, and inference outputs — allowing ML pipelines to be privacy-preserved from end to end.

Intelation HElios

Layered Privacy for every machine learning workflow

Value Proposition

HElios brings encrypted computation into your analytics and machine learning workflows by keeping data fully protected even while it's being processed. It enables organizations to run models, analyze information, and collaborate across systems without ever exposing raw data.

HElios becomes the cryptographic security layer for zero-trust AI — ensuring that sensitive information stays encrypted end-to-end while remaining useful for high-value workloads.

Capabilities

Fully encrypted computation across analytics and machine learning
Zero-trust data processing with no plaintext exposure at any stage
Encrypted model inference and feature transformation for sensitive workloads
Multi-party encrypted collaboration for cross-institution projects
Cryptographic protection aligned with strict compliance and data-locality requirements
Seamless interoperability with Nexis and Diffyn for layered PET enforcement

Use Cases

Healthcare
Legal
Finance
Government & Public Sector
Pharmaceuticals & Research
Retail & e-commerce

Why HElios?

Because keeping data encrypted at rest and in transit isn't enough anymore. HElios ensures your data stays protected even during computation — the moment it's traditionally most exposed.

HElios becomes the cryptographic security layer for zero-trust analytics and AI, enabling organizations to run models, process information, and collaborate across untrusted environments without ever revealing raw data. It allows encrypted feature processing, model inference, and multi-party computation, bringing homomorphic encryption into practical workflows while maintaining performance and utility. HElios gives you end-to-end protection for the workloads that matter most.

Technical Deep Dive

Encrypted feature processing and model inference

HElios supports computation directly on encrypted vectors, embeddings, and model features, enabling linear layers, polynomial activations, and full inference pipelines to run without exposing raw data.

HE-compatible machine learning workflows

Developers can convert selected ML layers into homomorphic-encryption-friendly operations, allowing end-to-end encrypted inference for models built in frameworks like PyTorch or TensorFlow.

Secure multi-party model collaboration

HElios enables encrypted gradient or parameter exchange across institutions, supporting collaborative analytics and federated learning without revealing sensitive training data.

Adjustable HE parameters for performance tuning

Developers can configure key cryptographic parameters — such as modulus size, security level, and scaling factors — to balance accuracy, speed, and security depending on workload needs.

Ciphertext-only execution

All intermediate operations, logs, caches, and pipeline stages run strictly on encrypted values, ensuring no plaintext is ever exposed during processing.

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