Standards & Certifications
How HCL AION Protects
Your Data and Services
Security
HCL AION applies layered security controls across AI pipelines, model lifecycle management and infrastructure operations.
Data encryption
Data and ML assets are protected through encryption and secure communication protocols across the AI lifecycle management platform.
Data protection
Customer data, model artifacts and configuration assets are protected using access controls, credential protection, and encrypted storage mechanisms.
Identity and access management
Federated authentication, SSO integration and RBAC enforce secure and least-privilege access to the platform.
Logging and monitoring
Container, application, model and access logs provide visibility into system activity and support operational monitoring.
Secure architecture and infrastructure
Containerized deployment with Kubernetes isolation supports secure and reproducible runtime environments.
Incident response and monitoring
Security incidents and vulnerabilities are handled through the HCLSoftware PSIRT process and coordinated remediation workflows.
Compliance
HCL AION follows HCLSoftware’s governance, risk, and compliance framework, with security controls continuously mapped through the GRC program and Secure SDLC practices aligned with industry standards such as NIST SSDF.
Privacy & Data Handling
HCL AION supports strong data governance while allowing customers to retain full control over their AI infrastructure.
Privacy by design and default
Privacy and data protection assessments are integrated into platform design and development processes.
Data roles and responsibilities
Customers retain full ownership and control of their data and infrastructure within their deployment environment.
Data subject rights and transparency
Data governance practices ensure transparency and traceability for how data is processed and used across AI workflows.
Responsible AI
HCL AION embeds governance and safeguards across the AI model lifecycle.
Ethical principles and governance
AI governance frameworks guide the responsible development and deployment of models.
Data handling and privacy in AI
Training datasets undergo validation, lineage tracking, and consent verification before use.
Fairness, bias and model quality monitoring
Models are monitored for bias, fairness, and performance drift across the lifecycle.
Transparency and explainability
Model outputs include traceability, audit logs, and explainability capabilities for accountability.
Support
To report a potential security vulnerability or raise a
security concern, please contact our security team at
hcl-aion@hcl-software.com
