Responsible and Ethical Practices in the Use of Generative AI
Date: 2025/11/06
Version: 1.1
Scope: All consulting, architecture, and implementation projects involving Generative AI developed by Clouxter using AWS services and foundation models.
Purpose
Clouxter reaffirms its commitment to the responsible and ethical use of Generative Artificial Intelligence, prioritizing accuracy, security, transparency, user empowerment, and sustainability across all its solutions.
The goal is to ensure that every initiative based on Generative AI is aligned with human values, respects privacy, and promotes verifiable, reliable, and sustainable outcomes.
1. AI Governance and Fundamental Ethical Principles
Clouxter integrates responsible AI principles throughout the entire lifecycle of Generative AI solution development and operations—from data preparation to production monitoring—ensuring control, traceability, and ethical use.
1.1 Accuracy and Reliability
We focus on generating accurate, verifiable, and contextually relevant results:
- Use of techniques such as Retrieval-Augmented Generation (RAG) and semantic search to ground responses in reliable sources.
- Systematic validation through quantitative metrics: accuracy, confidence levels, and reduction of hallucinations.
- The solutions published in Clouxter’s Success Stories demonstrate measurable improvements in accuracy and response quality in real-world environments (for example, projects in the FinTech, HealthTech, ISV, and Education sectors).
- Models are evaluated using curated datasets to ensure consistency and stability in their recommendations.
Expected Evidence: Written documentation of AI ethical policies and validation reports.
1.2 Security and Compliance (Confidentiality and Integrity)
Clouxter adopts robust security and privacy practices in the design and operation of its Generative AI solutions:
- Application of the Confidentiality, Integrity, and Availability principles under the AWS Well-Architected Framework.
- Data encryption in transit (TLS 1.2+) and at rest (AWS KMS).
- Network isolation through VPCs, Security Groups, and least-privilege IAM roles.
- Compliance with applicable regulations and best practices (HIPAA, PCI-DSS, GDPR, among others).
- Continuous monitoring through AWS Config, Security Hub, and the controls defined in Clouxter’s Data Encryption SOP.
Expected Evidence: Secure architecture diagrams, encryption policies, and IAM configuration.
2. Bias Mitigation Strategies and Fairness
Identifying and reducing bias is an essential part of our responsible AI practice. Clouxter employs structured mechanisms to evaluate, measure, and mitigate potential biases in models and generated outputs.
| Component | Practice Description |
| Fairness and Consistency Evaluation | Standardized frameworks are applied to assess fairness and performance across different foundation models and LLMs using curated datasets. Evaluation results are reviewed periodically and fully documented. |
| Prompt Management and Quality Control | Clouxter maintains a documented process for prompt lifecycle management. Versions and adjustments are tracked to ensure consistency and response quality. |
| Continuous Improvement | La metodología interna de GenAI incluye métricas de desempeño, evClouxter’s internal GenAI methodology includes performance metrics, post-implementation evaluations, and repositories of lessons learned derived from projects featured in Success Stories. |
3. Transparency, Auditability, and Traceability
Transparency is an essential principle for ensuring trust and accountability in the use of Generative AI.
| Component | Implemented Practice |
| Auditability and Traceability | Every generated inference is fully traceable: model version, data sources, and execution parameters are recorded. Metadata is stored in Amazon DynamoDB or CloudTrail. |
| Operational Monitoring and Logging | Continuous monitoring is enabled with Amazon CloudWatch, generating alerts and dashboards to supervise model behavior and prompt performance. |
| Documentation of Components and Prompts | Prompts are documented along with their role in the architecture and the reasoning chains (Chain-of-Thought) when applicable. |
| Openness and Replicability | The practices demonstrated in our Success Stories show how technological replicability and transparency are promoted across the implemented AI components. |
4. User Empowerment, Safety, and Consent Protocols
Clouxter prioritizes user control over their data and ensures secure access and operations throughout the entire project lifecycle.
| Element | Practice Description |
| Data Privacy and Security | All data is encrypted in transit and at rest using AWS KMS. Privacy-preserving mechanisms are applied (anonymization, tokenization). |
| Access and Consent | Explicit consent is obtained prior to any data use. During audits or access to customer environments, Clouxter uses temporary IAM credentials and identity federation. |
| Customer Acceptance and Delivery | Each delivery includes acceptance criteria defined in the SOW, along with runbooks and playbooks for ongoing operations. |
| Support and Maintenance | Support plans are provided with SLAs, escalation procedures, and post-launch monitoring integrated into CI/CD. |
5. Sustainability and Cost Optimization
Clouxter is committed to environmental and economic sustainability through efficient, scalable architectures aligned with AWS sustainability best practices.
| Initiative | Practice Description |
| Serverless and Scalable Architectures | We prioritize the use of fully serverless architectures (AWS Lambda, Step Functions, DynamoDB) to minimize consumption and costs. |
| Resource Efficiency | Compute and storage resources scale dynamically based on demand, reducing idle time and carbon footprint. |
| Sustainability Monitoring | Tools such as AWS Cost Explorer and the Sustainability Dashboard are used to track environmental and economic metrics. |
| Automation and Operational Excellence | Infrastructure is managed as code (IaC) using Terraform or CloudFormation, ensuring consistency and operational efficiency. |
6. Roles and Responsibilities
| Role | Responsibility |
| Clouxter Board of Directors | Approve and oversee the company’s ethical AI strategy. |
| CTO / Generative AI Practice Lead | Ensure the implementation of this policy and its alignment with AWS standards. |
| Project Manager / Delivery Manager | Ensure that responsible practices are applied in every customer engagement. |
| Model Owner / Ethical Reviewer | Manage model documentation, bias reviews, and audit results. |
| Security Lead | Oversee IAM controls, encryption, and regulatory compliance. |
| Customer Success Team | Manage customer communication, consent, and feedback. |
7. Training and Awareness
All employees and partners involved in AI projects must complete:
- Annual training in Responsible AI, including modules on ethical practices and security in AWS.
- Hands-on workshops on prompt engineering, bias testing, and ethical incident response.
- Simulation exercises on human review (“Human-in-the-Loop”) for high-risk scenarios.
8. Governance and Review
- Annual review of the policy by the Ethical AI Governance Committee.
- Periodic internal audits to verify compliance and supporting documentation.
- Mandatory updates in response to regulatory, technological, or model changes.
- Option for external audits or AWS-led assessments to validate responsible practices.
9. Alignment with AWS and International Standards
This policy is aligned with:
- OECD AI Principles (2019)
- ISO/IEC 42001:2023 – AI Management Systems
- Clouxter’s Data Encryption SOP and CI/CD & Operations Standard
10. Evidence and Documentation Matrix
| Requirement | Expected Evidence |
| AI Ethical Policies | This document |
| Bias Mitigation Strategies | Evaluation reports and prompt management records |
| Consent Protocols | Forms and clauses included in the SOW |
| Transparency Reports | RegistroAudit logs and model metadata |
| Security Measures | IAM configuration, encryption, monitoring, and all other items related to Governance and Security SOPs |
| Sustainability Initiatives | Efficiency and cost metrics and reports |
11. Approval and Change Control
| Version | Date | Description |
| 1.0 | 2025/11/05 | Initial public version |
| 1.1 | 2025/11/06 | Expanded version with references to Success Stories and AWS controls |