Services
Training & Support
AI Governance
Be Confident in Every Decision Your AI Makes.
Structure governance so it can be operated, evidenced, and improved. QualiWare provides the platform foundation to connect AI assets to processes, roles, risks, controls, and documentation.
Protect Your AI UsageGovern Generative AI Use
- Define usage tiers and approved tools
- Control how internal data is used with AI
- Maintain approvals for higher-risk prompts and automations
Control Vendor and Embedded AI
- Register vendor AI features in SaaS platforms
- Apply procurement requirements and review schedules
- Track vendor changes and internal risk decisions
High-Impact Decision Support
- Define who approves and who is accountable
- Document contestability and escalation mechanisms
- Retain evidence for audits and investigations
Operational Monitoring and Assurance
- Set review cadence and recertification triggers
- Track incidents, corrective actions, and outcomes
- Report governance status to leadership
What you Get
- Central AI register for systems, use cases, owners, and data inputs
- Risk tiering to prioritize high-impact AI first
- Approvals with decision rights and traceability
- Lifecycle checkpoints from intake to monitoring and retirement
- Vendor controls for procurement and ongoing reviews
- Reporting for audits, executives, and governance bodies
Ensure Privacy
Embed data protection and ethical safeguards into every stage of the AI lifecycle.
Prevent unauthorized use or data leaks by mapping how personal information is collected, used, and stored.
Innovate responsibly without compromising trust or security.
Stop Misuse
Make sure systems are used only for approved purposes with clear guardrails around how AI is developed, deployed, and managed.
Reinforce accountability through documented approvals and automated checks that prevent unauthorized models or data use.
Maintain control and avoid costly legal issues and reputational damage while improving the reliability and integrity of AI-driven decisions.
Mitigate Bias
Embed fairness, transparency, and accountability into how AI systems are built and used.
Ensures decisions made by algorithms are explainable, consistent, and based on accurate, representative data.
Have more reliable and equitable outcomes by Reducing legal and regulatory risks, improving data quality,