What “extending data governance to AI” means:

It is the practice of applying your enterprise data governance controls to the full AI data path. That path includes what users put into models (prompts), the derived artifacts models create or use (embeddings), the responses models return (outputs), and the services and people involved. In practical terms, you make lineage, access rights, policy enforcement, vendor oversight, and auditability work not only on databases and files, but also on prompts, embeddings, outputs, and AI services. 

Regulatory Implications

Regulators and auditors are moving from “policies on paper” to evidence. NIST’s AI Risk Management Framework stresses governance functions that map, measure, and manage risk with procedures and records, not promises. The EU AI Act adds data governance and transparency obligations for high-risk systems and model providers. Audit groups call for auditable logs and traceability for AI behavior. If prompts, embeddings, and outputs sit outside governance, you cannot prove compliance or defend decisions.

What it includes in practice:

• Inventory and lineage for AI data use. Know which datasets feed prompts, where embeddings are created, which models and apps reference them, and what outputs were produced.

• Automated policy controls at the point of use. Mask or redact sensitive fields before they enter prompts or embeddings. Block responses that violate policy. Apply identity-aware rules so only authorized people and agents use sensitive data with AI.

• Continuous risk evaluation and vendor oversight. Monitor how external AI services handle data, retention, and jurisdiction. Prioritize risky providers and restrict their use when needed.

• Defensible audit. Keep audit-grade logs of inputs, outputs, dataset usage, and enforcement actions so you can answer regulators, customers, and courts. Legal guidance now calls out preserving prompts and outputs as records.

How Acuvity supports this

• Full-spectrum visibility shows prompts, attachments, and AI outputs across users and agents, with audit trails for PCI, HIPAA, and GDPR. This gives you the “what happened” record for lineage and reviews.

• Dynamic policy enforcement integrates with identity and applies rules in real time. It can anonymize prompts, redact sensitive content, and block unsafe outputs in production.

• Adaptive risk engine scores usage and providers continuously. It looks at prompt content, user and agent context, provider practices, and policy breaches, then acts automatically when risk exceeds thresholds.

• Contextual intelligence adds environment awareness around agents, APIs, data sources, and tools, so governance decisions consider how AI is actually being used. 

In short, extending data governance to AI means governing the data where AI actually uses it. With Acuvity, that covers lineage for prompts, embeddings, and outputs, automated policy at the point of use, continuous provider oversight, and a defensible audit trail your compliance team can rely on.

Acuvity provides visibility into how enterprise data is used once it enters AI systems. It detects when sensitive information is entered into prompts, converted into embeddings, or returned in outputs, and links those events to users and policy actions. This extends lineage and oversight beyond databases into the full AI workflow.

Acuvity enforces governance policies in real time. Sensitive values can be masked or redacted before reaching a model, permissions can be tied to identity systems so only authorized roles interact with certain datasets, and outputs that violate compliance rules can be blocked automatically.

A defensible audit is verifiable evidence of how data was used inside AI systems and what controls were applied. Acuvity generates audit-grade logs of prompts, outputs, and enforcement actions, tied to users and context. These logs can be presented during regulatory reviews, customer assessments, or contractual audits to prove that governance policies were followed.

Acuvity continuously evaluates external AI services and model providers for risk factors such as retention policies, jurisdiction, and data handling practices. High-risk providers are flagged by the risk engine, and their use can be restricted or blocked before exposure occurs. This oversight extends governance to the vendor layer.

Yes. Acuvity combines visibility, real-time policy enforcement, and audit-ready logs to support compliance with regulations and frameworks such as GDPR, HIPAA, PCI, NIST AI RMF, and the EU AI Act. By tracking lineage, enforcing safe-usage policies, and producing audit trails, Acuvity gives enterprises the evidence regulators and auditors expect.