The promise of AI to transform business with massive productivity gains and operational efficiencies is real. Yet, for many organizations, AI projects fail to reach production due to a lack of confidence in the accuracy of the output. Inaccurate or misconstrued results put organizations at financial or reputational risk when organizations are flying blind to the data serving the models.
Organizations need a scalable KI-Governance-Plattform to ensure data integrity, reduce risk, and improve trust in AI outcomes.
Inaccurate output from AI prompts and autonomous agent workflows suffer from a common problem. It is well understood that AI failures are directly related to the integrity of the data feeding models. What is less well understood is how dependent AI infrastructure is on the context surrounding the data at the time a model is run.
Why the Enterprise Context Layer Matters
• AI systems are only as reliable as the data context behind them
• Governance requires visibility into data, policies, and access
• Fragmented tools create blind spots and risk
• BigID and Atlan unify data intelligence and context for AI governance
• Organizations can reduce risk and accelerate AI adoption
The Enterprise Context Layer
BigID und Atlan are partnering to bring AI first organizations trusted, compliant data and context. Atlan defines the enterprise context layer as “the governed infrastructure between your data stack and AI systems. It encodes what data means including business definitions, relationships, operational rules, lineage, and policies so AI agents reason correctly at inference time.
Building the enterprise context layer is complex and requires partnerships and collaboration across the Data & AI ecosystem. Sandipan Bhaumik, a Data & AI thought leader from Databricks, coined the phrase context drift in his blog The Governance Layer Enterprise AI Has Been Missing. Bhaumik states that context drift occurs when an AI’s “evaluation infrastructure is decoupled from the governed context that determines what a correct answer means at a given point in time.” In other words, it’s not always the AI model or the data in use that is to blame, but the lack of context surrounding the data at the time the AI run is executed.
Bhaumik defines 4 key components of enterprise context. All 4 context components in the table below must be discovered, indexed, and inferred in order to ensure AI models generate accurate results consistently.
Table 1: Enterprise Context Components
| Component | Definition |
| Datenkontext | The technical metadata describing the structure, location, and lineage of data across structured and unstructured sources . |
| Semantic Context | The business meaning, logic, and standardized definitions (e.g., glossary terms) that provide a consistent interpretation of data for both humans and AI. |
| Policy Context | The regulatory, security, and privacy rules—such as retention, sensitivity classification, and risk alerts—that govern how data can be used. |
| User Context | The identity-aware details including access permissions, ownership, and intended purpose (use case) for the data being accessed . |
BigID and the Context Layer
The accuracy of any data and AI project starts with Entdeckung und Klassifizierung von Daten. In short, you can’t manage what you can’t see. BigID provides the industry’s best data discovery and classification to provide a solid foundation for data context, visibility and control.
Building visibility, trust, and control of data allows organizations to move from AI paralysis to AI production with a scalable KI-Governance-Plattform with trustworthy output that has proper context. Below is a breakdown of how BigID and Atlan deliver value across the 4 context components outlined above.
Datenkontext
BigID enriches the Atlan enterprise context layer with context for the data inside of a file, object, or database field so organizations gain data context across unstructured, structured, and semi-structured data. Atlan’s enterprise data graph consumes BigID classification and sensitivity tags to inform agentic workflows on what data to use (and not use) for AI models.
Semantic Context
BigID’s metadata registry provides a stateful record of technical metadata across an organization’s entire data estate at any point in time. BigID technical metadata enriches Atlan’s catalog, providing business users with added context on the state of business rules at the time AI models are run.
Policy Context
BigID maps an organization’s sensitive data to AI, Security, and privacy policies that govern how data should be used (or not used) including AI risk management workflows. BigID flags when an organization’s data is out of compliance and takes action to protect the data in accordance with regulatory policies. BigID enriches Atlan’s data catalog with this policy context information. Classification tags and policy alerts propagate through Atlan’s elaborate data lineage graphs, providing data owners with context for what actions to take to cleanse data & AI pipelines.
User Context
BigID classification and policy alerts can also be used to provide user context. Using patented graph technology, BigID correlates sensitive data findings across all datasets in an enterprise. The view provides an accurate picture of where the sensitive data is located, who or what is accessing it, and the number of access attempts. BigID invokes actions either directly or through API calls to the enterprise context layer to apply data access governance controls to protect data by user role.
Abschluss
BigID and Atlan help close the AI context gap, allowing organizations to unleash the value of AI with governed and secure data. Figure 1 provides a visual of how BigID and Atlan deliver trustworthy, compliant data through the enterprise context layer. Join the Atlan Activate virtual event on April 29 to learn more about how Atlan, BigID, and the context layer partner ecosystem accelerate trustworthy AI in production.
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