Key Takeaways
- Centralized governance with decentralized consumption across BI and AI
- Consistent metrics, definitions and business logic everywhere
- Fine-grained access control, row- and column-level security
- Auditability, lineage tracking and explainability for data and AI
Data & AI Governance Gaps in Enterprise Analytics
As BI and AI scale across teams and tools, governance fragments—forcing enterprises to choose between speed, trust and control
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Metrics and KPIs diverge across dashboards, teams and AI models
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Security rules are re-created separately in each BI tool
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Access control is inconsistent and hard to audit
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AI models consume data without guardrails, clear lineage or explainability
One definition of data. Every question. Every tool.
Centrally governed metric definitions and business logic
Maintain consistent governance across dashboards, AI models and workflows
Expose governed data across teams, tools and use cases without rework
Freedom to explore—without losing control
Enable thousands of users to explore data safely within defined guardrails
Prevent ad-hoc logic and metric sprawl
Balance speed, autonomy and trust as adoption scales
Fine-grained security, enforced everywhere
Access policies defined once and applied consistently
Centrally enforced row, column and metric-level security
No duplicated governance across BI tools
Explainable logic behind every insight
End-to-end lineage from source data to BI and AI outputs
Transparent business logic users can inspect, validate and defend
Complete audit trails of who accessed what, when and how
How Kyvos Fits in Your
Analytics Stack
How Kyvos Delivers Data & AI Governance
Proven controls, certified security and centralized governance for trusted BI and AI at scale
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Central governance layer
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User and role management
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Automatic lineage tracking
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Secure data recovery
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Row- and column-level security
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Single sign-on
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Comprehensive audit logging
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Column data masking
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Multi-tier encryption
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Enterprise-grade certifications