AffectLog’s platform solves a core tension in regulated AI: organisations need the insight of data‑driven models without breaching privacy, compromising trust or violating complex laws. Across sectors—from healthcare and finance to public agencies and education—the platform’s modular services provide secure orchestration, automated compliance, self‑sovereign governance, dynamic risk management, actionable intelligence and privacy‑preserving foundations. Each capability is delivered as a feature page on the /platform site, replacing generic white‑paper links with deep technical explanations. The sections below mirror those feature pages and outline how they work in practice.
Federated Orchestration Engine — Compute Where Data Lives
Schedule cross‑site training and inference with encrypted aggregation, client health checks and quota controls—no raw data transfer. Works on‑prem and sovereign clouds; integrates with your MLOps.
Motivation and design
Sensitive data (health records, payment transactions, student assessments, government statistics) cannot legally be copied to a central cloud. Instead, the Federated Orchestration Engine takes the computation to the data. A central scheduler distributes model weights and training plans to each organisation’s node, where computation occurs locally inside an ephemeral sandbox. The node trains the model on its own data and returns only encrypted parameter updates, never raw records. This approach is the essence of federated learning: the training process remains on the client device or server; a central server aggregates the updates, refines the model and redistributes it[1]. Secure aggregation protocols (e.g., multiparty computation or homomorphic encryption) further obscure individual updates[1]. Each client performs health checks (to ensure adequate data quality and compute resources) and enforces quota controls to prevent any single site from overwhelming the federation.
Technical flow
- Job scheduling: An MLOps integration submits a training or inference job. The orchestrator checks available sites, quotas and consent policies.
- Ephemeral sandbox spin‑up: At each selected site, the Ephemeral Sandbox Orchestration Protocol spins up a container or secure enclave inside the organisation’s own infrastructure. The model and code are loaded; the site’s data never leaves this environment. After the job, the enclave is torn down, leaving no data residue[2].
- Local training/inference: The node trains or evaluates the model using local records (e.g., a hospital’s electronic health records, a bank’s transaction ledger, a school’s assessment data) and computes a parameter update. Privacy‑enhancing technologies such as differential privacy and secure multiparty computation can be applied locally[3].
- Secure aggregation: The encrypted updates are sent to the central aggregator. Secure aggregation protocols ensure that the server can combine updates without learning individual contributions. If a client’s health check fails (e.g., data is too small or unstable), it is skipped to preserve model quality.
- Global model update: The aggregated update is applied to the global model and redistributed. This cycle repeats until convergence.
Real‑world applicability
- Healthcare: Hospitals in France and Germany train a shared triage model without exposing patient data. The engine orchestrates cross‑site training on on‑premise servers and sovereign clouds, ensuring that only aggregated gradients leave each hospital.
- Financial services: A consortium of banks trains and validates fraud‑detection and credit‑risk models. Each bank’s updates are encrypted; the central aggregator produces an improved model while preserving client privacy.
- Government and research: Ministries of health, environment and education across the EU and Africa analyse cross‑border datasets (e.g., pandemic early warning, climate models, skills analytics) without exporting raw data.
- Education: School districts deploy adaptive learning models using on‑premise servers; only anonymised feature updates are shared. This respects parental consent requirements under GDPR Article 8 and UNICEF ethics[4].
Automated Compliance Layer — EU AI Act, GDPR, ISO 42001
Lead: Map model behaviour to law and standards continuously. Pre‑built checks, risk scoring and evidence packs ready for auditors.
Legal obligations and standards
High‑risk AI systems in the EU must comply with a host of requirements. The EU AI Act (Articles 9–15) demands a risk‑management system, data governance, technical documentation, record‑keeping, human oversight, and accuracy, robustness and cybersecurity[5]. GDPR Article 8 adds special rules for minors, requiring verified parental consent for data processing[4]. ISO/IEC 42001 formalises AI management systems and emphasises ethical considerations, transparency and risk management[6]. ISO 42001 guidance explains that an AI management system must include leadership commitment, planning, support, operation, performance evaluation and continual improvement[7].
How AffectLog automates compliance
AffectLog’s RegLogic Compliance DSL encodes ≈400 clauses from the EU AI Act, GDPR, ISO/IEC 42001, OWASP AI security guidelines and sectoral regulations. When a model is trained or deployed, RegLogic automatically maps the activity to relevant legal obligations and runs pre‑built checks. Examples include:
- Consent and lawful basis: Verifies that each record used in training has a valid legal basis (e.g., parental consent for minors)[4].
- Risk classification and bias: Categorises the AI system (e.g., high‑risk) and ensures bias testing and mitigation are enabled[5].
- Data governance: Enforces data‑minimisation, purpose limitation and data‑quality rules.
- Human oversight: Ensures that AI decisions can be reviewed and overridden by authorised personnel.
- Documentation & evidence packs: Auto‑generates the Annex IV technical file required by the EU AI Act (including model design, training data description, risk‑management steps and post‑market monitoring plans) and ISO 42001 performance‑evaluation reports.
All checks and outcomes are logged in an immutable audit ledger (see Self‑Sovereign Governance) and are accessible via dashboards or export. This dramatically reduces the manual burden of compiling evidence for internal auditors, regulators or ethics boards. Because RegLogic is updated as laws evolve, the platform keeps models “evergreen compliant” across regions and sectors.
Applicability
- Finance: The layer maps fraud and credit models to Basel, PSD2 and GDPR requirements; automatically produces conformity documentation for the European Banking Authority.
- Healthcare: It guides medical device AI through MDR and AI‑Act high‑risk obligations; generates risk‑management files; verifies data subject consent.
- Public sector: It enforces Data Governance Act conditions for public sector data reuse and checks cross‑border data sharing rules.
- Education: It ensures minors’ data processing follows GDPR Article 8 and UNICEF/UNESCO ethics guidelines[8].
Self‑Sovereign Governance — Immutable, Verifiable Evidence
Lead: Anchor policies, consent and audit logs to an append‑only ledger. Zero‑knowledge proofs verify compliance without revealing secrets.
Immutable audit ledger
Data‑governance rules must be enforced and provable. AffectLog’s platform integrates an append‑only ledger that stores every policy decision, consent check, model update and inference. Similar to the Microsoft Azure confidential ledger, this ledger provides immutability, tamper‑proofing and append‑only operations, combining cryptographic techniques and blockchain to protect metadata[9]. Every ledger entry is hashed and linked to the previous entry, so any tampering is evident. The ledger is ideal when critical records must have their integrity protected—for example, regulatory compliance and audit trails[10]. The ledger runs inside hardware‑backed secure enclaves and uses consensus across multiple instances to guarantee tamper resistance[11].
Zero‑knowledge proofs for privacy
To provide verifiability without exposing sensitive information, the platform employs zero‑knowledge proofs (ZKPs). A ZKP allows a “prover” to convince a “verifier” that a statement is true without revealing any other information[12]. In blockchain contexts, ZKPs let nodes validate transactions or compliance rules without revealing underlying data[13]. Examples include proving that a company meets KYC/AML requirements without disclosing customer identities or that a model meets fairness thresholds without revealing protected attributes. Combining the audit ledger with ZKPs allows regulators or ethics boards to verify compliance claims without gaining access to the underlying data or code—a key requirement for public accountability.
Examples
- Government & research: Cross‑agency consortia anchor consent and data‑access policies in the ledger; auditors verify compliance via ZKPs. When sharing environmental or health data across borders, ministries can prove that no unauthorised access occurred.
- Finance: Banks record every model‑validation run, bias test and risk‑management decision; regulators verify that each requirement was satisfied using the ledger and ZKPs.
- Education: Schools log parental consent and AI‑decision explanations; parents or oversight bodies can audit decisions without revealing student data.
Dynamic Policy Enforcement — Detect, Mitigate, Prove
Lead: When risk flips red (drift, bias, out‑of‑scope use), trigger automatic mitigation: access throttles, retraining, rollback or human review—with traceable outcomes.
Continuous monitoring
AI risks evolve as data distributions shift or models are repurposed. The platform’s dynamic policy enforcement engine combines design‑time preventive controls and run‑time detective and response controls. According to AI‑governance experts, effective risk management requires preventive measures during development and monitoring during operation; detective controls (such as drift detection algorithms, performance monitoring and anomaly detection) identify emerging risks, while response controls (e.g., model rollbacks, human override procedures) activate when risks materialise[14]. The enforcement engine continuously monitors model inputs and outputs for:
- Data drift: Statistical divergence from the training distribution indicates the model may be misaligned.
- Concept drift: Changes in the relationship between inputs and outputs (e.g., new fraud patterns).
- Bias or fairness anomalies: Disparities across demographic or geographic groups.
- Out‑of‑scope usage: Models invoked for purposes outside their approved consent or risk category (e.g., a credit‑risk model used for employment screening).
Automated mitigation
When any monitor crosses a red threshold, pre‑defined policies trigger automatic actions:
- Access throttles: Temporarily restrict queries or rate‑limit sensitive operations.
- Retraining or update: Launch a federated re‑training cycle with fresh data.
- Rollback: Restore a previous model version if the current one performs poorly.
- Human review: Escalate decisions to a human overseer; log the outcome and rationale.
Every mitigation is logged in the ledger, creating a traceable history of risk events and responses. This approach transforms governance from passive compliance into active risk management.
Compliance Intelligence — From Signal to Decision
Lead: Cross‑model dashboards, fairness/explainability overlays and sector packs. Turn governance from “checkbox” to operational advantage.
Turning data into insight
Regulators and executives need more than raw logs; they require summarised insights to make decisions. AffectLog’s compliance‑intelligence layer aggregates signals from the federated orchestration engine, the compliance layer, the ledger and the XAI pipeline. It provides:
- Risk heatmaps and dashboards: Visualisations of model performance, drift, bias and compliance status across all deployed models and sites. NIST’s AI risk management guidance recommends using risk heatmaps to prioritise high‑risk models and to automate compliance tracking through AI risk dashboards[15].
- Fairness and explainability overlays: Models’ feature importance (e.g., SHAP values) and counterfactual analyses are summarised and flagged when disparities appear. Explainability tools such as SHAP, LIME and counterfactuals are recommended for risk measurement and monitoring[16].
- Sector packs: Preconfigured views and metrics tailored to healthcare, finance, public sector and education. For example, a finance pack highlights credit‑risk metrics (probability of default, adverse impact ratios); a healthcare pack shows model sensitivity and specificity across demographics; an education pack monitors student engagement and fairness.
- Actionable alerts: Integration with dynamic policy enforcement means that signals can trigger mitigation actions or require approvals.
Operational advantage
By turning compliance into intelligence, organisations transform governance from a cost centre into a strategic asset. Executives can make informed decisions (e.g., launching new services or approving research collaborations) based on up‑to‑date risk and fairness metrics. Regulators receive concise evidence packs instead of raw data dumps, speeding up approvals. Partners trust the shared models because they can see the governance status without accessing sensitive data.
Ephemeral Privacy Foundations — PETs by Default
Lead: Differential privacy, secure multiparty computation, homomorphic encryption, trusted execution environments and transient containers ensure zero residue. Hard security meets soft‑data contexts.
Privacy‑enhancing technologies
The platform’s privacy foundations combine multiple privacy‑enhancing technologies (PETs) to protect data at rest, in transit and during computation:
- Differential privacy (DP): NIST explains that DP answers queries by adding random noise to the output; queries with higher sensitivity require more noise, and DP is compositional across multiple analyses[17]. DP prevents an attacker from learning whether any individual’s data was included in the dataset[17] and provides mathematical guarantees that results hardly change when a single individual joins or leaves[18]. AffectLog can apply DP to model updates before aggregation, protecting users even if updates are intercepted.
- Secure multiparty computation (SMPC): SMPC allows multiple parties to compute a function jointly without revealing their inputs. GeeksforGeeks summarises that SMPC enables parties to compute functions while preserving data privacy; data is encrypted and distributed across participants, and no individual can see others’ inputs[19]. This is used in AffectLog’s secure aggregation and in joint analytics across competing organisations.
- Homomorphic encryption (HE): HE allows computations to be performed directly on encrypted data. Although fully homomorphic encryption is computationally heavy, it can be combined with other methods for specific tasks.
- Trusted execution environments (TEEs) / confidential computing: Hardware‑based secure enclaves encrypt data in memory and verify workload integrity through remote attestation. Edgeless Systems notes that TEEs keep data encrypted during processing and allow practical confidential computing compared to purely cryptographic approaches[20]. TEEs enable on‑prem or sovereign cloud deployments with attested security.
- Ephemeral containers: Containers and enclaves are spun up for each task and destroyed immediately. This ensures zero residue and reduces the attack surface[2].
Unified privacy architecture
These PETs are orchestrated by the platform according to context. For instance, federated training may combine DP‑protected model updates with SMPC‑based aggregation; cross‑agency computations might run inside TEEs; dynamic data clean rooms for research use TEEs plus SMPC. The unified architecture allows organisations to choose the right tool for each risk profile while maintaining performance and usability.
Conclusion
AffectLog’s platform provides a comprehensive framework for regulated AI. By orchestrating computation where data lives, encoding laws into machine‑enforceable rules, anchoring evidence on immutable ledgers, dynamically enforcing risk policies, transforming governance signals into insight and employing cutting‑edge privacy technology, the platform turns compliance into competitive advantage. Whether training medical triage models across hospitals, validating credit models across banks, analysing cross‑border health and environmental data, or personalising learning for minors, the same foundation applies. The modular pages described here allow prospective customers—especially in the public sector and regulated industries—to explore each capability in depth and understand how AffectLog enables sovereign, compliant AI at scale.
[1] How does federated learning ensure data remains on the client device?
[2] [3] Federated learning for children’s data | Innocenti Global Office of Research and Foresight
[4] Are there any specific safeguards for data about children? – European Commission
[5] High-level summary of the AI Act | EU Artificial Intelligence Act
[6] ISO/IEC 42001:2023 – AI management systems
[7] Understanding ISO 42001
[8] Policy guidance on AI for children | Innocenti Global Office of Research and Foresight
[9] [10] [11] Microsoft Azure confidential ledger overview | Microsoft Learn
[12] [13] How Zero-Knowledge Proofs Secure Blockchain Privacy
[14] Choosing the right controls for AI risks
[15] [16] Understanding NIST’s AI Risk Management Framework: A Practical Guide
[17] Differential Privacy for Privacy-Preserving Data Analysis: An Introduction to our Blog Series | NIST
[18] Differential Privacy | Privacy-Enhancing Technologies PETs
[19] What is Secure Multiparty Computation? – GeeksforGeeks
[20] Confidential computing and multi-party computation (MPC)