AI / ML Security & Governance Policy

1. Purpose

This policy defines the principles, controls, and security practices applied to all AI and machine learning components of the Solas Compliance platform.

Its purpose is to ensure privacy, confidentiality, integrity, and responsible AI use across all AI-driven features, pipelines, and services.

2. Scope

This policy applies to:

• All ML pipelines, models, and microservices deployed under the Solas Compliance platform.
• All data used for fine-tuning, inference, evaluation, or compliance review.
• All local and cloud-based infrastructure, including Amazon Web Services (AWS, primary region eu-west-2, London), Docker containers, vector databases, and model inference services including AWS Bedrock.

3. Data Privacy & Protection

Data Ownership

All client data remains owned by the client. No data is retained post-inference without explicit client consent by the AI service.

Data Ingestion

Uploaded policies and documents are processed through secure ingestion endpoints using HTTPS/TLS.

PII Handling

Personally identifiable information, or PII, is masked or removed before embedding, training, or evaluation where required.

Data Storage

Vector embeddings, metadata, and logs are encrypted at rest using AWS Key Management Service (KMS) or equivalent encryption controls.

Data Transmission

All network communication uses HTTPS/TLS 1.2+ encryption.

Data Deletion

Clients can request deletion of stored embeddings, fine-tuning data, and inference logs at any time.

Isolation

Each client’s data is logically isolated using dedicated namespaces, databases, or equivalent access controls.

4. Model Governance

Model Provenance

All base models used for fine-tuning must be open-weight or commercially licensed for the intended use. Examples may include Llama, Mistral, or equivalent approved models.

Fine-Tuning Data

Only sanitized and appropriate policy text is used for model fine-tuning. Sensitive data is excluded unless explicitly approved and protected under agreed controls.

Version Control

All model versions are tagged and tracked through an internal model registry, such as MLflow, Weights & Biases, or an equivalent system.

Reproducibility

Each model version must include metadata including dataset hash, hyperparameters, training date, and owner.

Auditability

Inference logs include timestamp, model version, input reference, output, and decision rationale where required for compliance review.

5. Responsible AI Principles

Fairness

Models are evaluated for bias across policy types, languages, regulatory categories, and client use cases where applicable.

Transparency

AI-driven outputs provide human-readable reasoning, references, and citations where available.

Accountability

Every AI-generated decision or recommendation is logged with traceable metadata.

Explainability

Agents provide supporting context, including retrieved text, rule references, and compliance checks.

Human Oversight

Human reviewers can review, approve, reject, or override AI-generated compliance decisions before final report generation.

6. Security Controls

Infrastructure

Microservices are containerized using Docker and deployed on AWS with least-privilege IAM roles.

Access Control

MFA and IAM-based permissions are required for access to AWS resources and sensitive systems.

Model Servers

Models are served through AWS Bedrock or on isolated compute environments, and accessed through controlled API gateways.

API Security

External access is protected using JWT-based authentication, rate limiting, and role-based access controls.

Logging & Monitoring

Centralized logging is maintained through AWS logging services or equivalent systems, with monitoring for abnormal model or system behavior.

Incident Response

In the event of a suspected data breach or security incident, logs are captured, affected services are isolated, and a security review is initiated.

7. Data Retention & Deletion

Temporary inference data is deleted automatically after job completion unless retention is required for operational, legal, or compliance purposes.

Fine-tuning datasets are retained only where required for reproducibility, auditability, or agreed client use cases, unless the client opts out.

Embeddings are retained for the lifetime of the corresponding source policy, or until the policy is deleted, superseded, or deletion is requested by the client.

Logs follow a configurable retention policy based on operational, security, and compliance needs.

8. Certifications & Compliance Alignment

Solas Compliance is SOC 2 Type I and Type II certified and ISO 27001 certified.

GDPR

Data minimization, purpose limitation, access control, and right to erasure.

SOC 2

Security, confidentiality, availability, and processing integrity controls.

ISO 27001

Access control, cryptography, operational security, and incident management.

NIST AI RMF

Responsible AI development, governance, risk management, and monitoring.

Questions about this policy? Email hello@solascompliance.com.