As organizations lean into cloud-first strategies, they face growing complexity in managing data access and governance across data silos whether in data lakes, enterprise SaaS systems, or structured data sources such as SAP. While cloud provides scale and flexibility, it also introduces fragmentation where data lives in more places, under more policies, managed by more teams.

Organizations are actively prioritizing consistency in data access and policy enforcement across distributed environments. Yet the reality is:

Policy drift across systems remains common.

Manual governance processes don’t scale.

Confidence in data accuracy, compliance, and auditability remains too low.

With rising AI adoption, the risks associated with ungoverned data pipelines are only increasing. Organizations need a single governance fabric that can span across data, people, and systems wherever they are without the friction of centralizing it all first. Some of the challenges are:

Multi-cloud complexity. Organizations operating across AWS, Azure, and Google Cloud face inconsistent governance models that require separate configurations for each environment.

Siloed governance tools. Different data platforms often require different governance approaches, creating administrative overhead and security gaps.

Cross-platform data movement. As data flows between systems, governance policies often fail to follow, creating blind spots in compliance and security.

With rising AI adoption, the risks associated with ungoverned data pipelines are only increasing. This creates new urgency for unified governance:

AI amplifies governance risks. Models trained on ungoverned data can perpetuate biases, violate privacy regulations, or produce unreliable outputs.

Model drift compounds data drift. When underlying data shifts without governance controls, AI model assumptions become outdated, requiring more robust oversight.

Regulatory scrutiny is intensifying. As AI regulation evolves globally, organizations need comprehensive governance to demonstrate responsible AI practices.

Organizations need a single governance fabric with:

Consistent policy enforcement across clouds, platforms, and data types.

Automated governance workflows that scale with growing data volumes.

End-to-end visibility into data lineage, usage, and compliance.

Reduced administrative overhead through centralized management.

Enhanced collaboration through secure, governed data sharing.