This document offers an in-depth exploration of the technical underpinnings of the AL360° platform — a next-generation solution developed by AffectLog to solve the pressing challenge of accessing sensitive private data in a trustworthy and regulation-aligned manner. AL360° is conceived as a full-stack, modular, and future-proof infrastructure layer for the privacy-first AI ecosystem. The platform is designed to be deployed across various industries — healthcare, education, finance, government — where privacy, explainability, consent, and compliance are non-negotiable.
Unlike conventional approaches that rely on centralized data repositories or blind anonymization, AL360° fundamentally restructures the flow of computation: models are sent to the data, not the other way around. Computation happens locally, respecting data sovereignty, with the outcome protected by formal guarantees like secure aggregation and programmable consent enforcement.
The AL360° system allows multiple roles — data owners, use-case leaders, auditors, and regulators — to interact within a trustable, auditable, and reward-based ecosystem. This document introduces the building blocks of this system: the architecture layers, their responsibilities, orchestration flows, security guarantees, and compliance design. It provides a blueprint for deploying AL360° in real-world settings at enterprise or sovereign scale.
Core Platform Objectives
- Privacy-Preserving Computation
AL360° ensures that sensitive data never leaves its origin. Computation is pushed to the data source where tasks execute inside controlled, ephemeral environments. Sensitive attributes are never transmitted — only sanitized, anonymized or differentially private outcomes. - Machine-Enforced, Programmable Consent
Data owners define policy contracts through intuitive interfaces that are transformed into machine-executable rules. These rules are enforced before any federated task is accepted. The result is a system where data access is conditional, transparent, and always logged. - Decentralized Federated Execution
Instead of aggregating data, AL360° federates AI model training and inference. Distributed execution ensures data minimization, local control, and mitigates single points of failure. Encryption and policy enforcement ensure that even federated aggregators cannot reverse engineer the source data. - Economic Participation and Incentivization
Data contributors — whether individuals or institutions — receive real-time feedback and monetary or tokenized rewards for participating in federated jobs. Contributions are scored algorithmically and transparently. Fairness and value sharing are baked into the incentive engine. - Regulatory Alignment by Design
The platform is pre-integrated with compliance dashboards, policy audits, explainability modules, and immutable logs to satisfy requirements like GDPR, the EU AI Act, and future sovereignty mandates. It’s a regulation-first technical framework.
High-Level System Architecture
AL360° is built as a horizontally layered system. Each layer adds functionality while maintaining architectural separation to enable maintainability, security, and scalability. The five principal layers are:
- Interface Layer
This layer includes user-facing interfaces like web portals, dashboards, mobile apps, and API documentation. It allows stakeholders to configure policies, initiate federated jobs, visualize logs, and monitor compliance status. - Orchestration Layer
The brain of the system — responsible for evaluating job submissions, matching them against available nodes, distributing encrypted jobs, managing compute sessions, and handling results aggregation. - Execution Layer
At the data edge, nodes run isolated containers or virtual enclaves where tasks execute securely. The environment enforces constraints from the policy engine and returns only output derivatives, never raw data. - Consent & Audit Layer
This layer includes the policy evaluator, immutable ledger, and provenance engine. It tracks what happened, where, when, and under what policy. It supports internal audits and external regulatory review. - Incentive Layer
A contribution graph scores every node based on activity, accuracy, and reliability. Reward tokens or fiat equivalents are calculated and distributed. Historical reputation helps prevent abuse and rewards consistent, fair participation.
Component-Level Breakdown
Frontend & Interface Layer
- Dashboard UI: Allows users to submit, track, and review FL tasks, consent logs, and node performance.
- Policy Builder: Visual and code-based editor to define data use boundaries (who can access what, how, and when).
- Participation Ledger: Offers data owners a timeline of contributions and visibility into impact and earnings.
- FL Campaign Marketplace: A matching layer that showcases open FL jobs and allows data owners to opt-in with custom constraints.
Backend & Orchestration Layer
- Task Engine: Accepts incoming tasks, validates schema, estimates node cost, and prioritizes execution.
- Policy Validator: Matches submitted tasks against registered policy contracts before job dispatch.
- Node Discovery Service: Maintains heartbeat and availability maps for all federated execution nodes.
- Session Manager: Handles compute lifecycle, retries, and anomaly detection during task execution.
Federated Compute Layer
- Isolated Execution Nodes: Sandboxed environments that execute compute jobs locally using confidential computing.
- Data Mapper: Translates heterogeneous datasets (e.g., EHR, IoT, CSV) into task-ready formats without leaking metadata.
- Secure Aggregator: Combines local outputs using privacy-preserving techniques such as secure multi-party computation.
- Task Loader: Dynamically injects policies, model weights, and feature constraints into the enclave.
Consent & Compliance Layer
- Policy Engine: Translates natural language consent into deterministic policy objects.
- Audit Ledger: Logs every transaction and decision as a hash-based, non-repudiable event.
- Explainability Exporter: Tags every model update with interpretability metadata (e.g., feature attribution).
- Compliance Dashboard: Visual summaries of bias detection, privacy preservation, and AI Act readiness.
Reward and Tokenization Layer
- Contribution Tracker: Measures the relative weight of each node’s output in model performance.
- Reward Calculator: Allocates payouts proportionate to validated contributions.
- Payout Gateway: Supports fiat, crypto, or on-platform credit systems configurable by the organization.
- Reputation Graph: A trust graph that adjusts node privileges and matching priority based on performance.
Data Lifecycle and Execution Flow
- Task Definition
A project owner defines an FL or analytics task, including model parameters, expected data features, and intended use. - Policy Matching
Before deployment, the orchestrator matches available nodes that satisfy the task’s declared policy needs. - Job Dispatch
The encrypted task, containing weights or inference requests, is dispatched only to eligible nodes. - Local Execution
Nodes run the task in a secure enclave, referencing local data and producing output metrics. - Secure Aggregation
Results are processed using privacy-preserving methods (e.g., noise addition, aggregation masking) and transmitted. - Explainability & Metadata
Alongside the result, the node attaches interpretability metadata (saliency maps, confidence intervals, bias flags). - Immutable Logging
All task events, from execution to return, are hashed and appended to the audit trail ledger. - Reward Distribution
Based on contribution value, nodes receive fair payouts, strengthening the trust loop and incentivizing future participation.
Deployment & Scalability
AL360° is designed to be highly portable and horizontally scalable. Its components are containerized and can run on-premises, in public/private clouds, or sovereign digital infrastructures. The orchestrator can scale linearly, adding parallelism as job volume increases. The architecture supports multi-tenancy, which allows enterprises and governments to host dedicated instances while participating in shared marketplaces.
Risk, Privacy, and Security Controls
- End-to-End Encryption using secure key exchange and session-based encryption across all communications.
- Ephemeral Execution Environments that auto-destruct post-execution, leaving no traces.
- Differential Privacy Layer for quantifiable privacy guarantees in aggregated insights.
- Zero Data Leakage Assurance by enforcing data locality and blocking all raw data export channels.
- Role-based Access Control to segregate access across internal users, developers, auditors, and external agents.
Regulatory and Ethical Compliance
AL360° is architected with regulatory foresight:
- GDPR: Built-in data minimization, purpose limitation, and auditability.
- EU AI Act: Risk-tiered model with documentation hooks for conformity assessment.
- Digital Sovereignty: Designed to comply with national AI and data strategies across the EU and other jurisdictions.
- Ethical AI: Integrates fairness scoring, transparency toggles, and redress logging into all decisions made on the platform.
Conclusion
AL360° is a breakthrough in privacy-respecting AI infrastructure. It unifies consent, compliance, compute, and compensation into a system that’s not only technically rigorous but aligned with the economic and ethical realities of working with private data. By replacing data extraction with consented, programmable access, AL360° turns liability into a platform advantage — giving organisations the tools to innovate responsibly, scale ethically, and lead confidently.