Data Provenance: Governing AI Before It Governs You

As artificial intelligence (AI) permeates enterprise functions, from HR communications to legal contract analysis, its integration is happening quietly, according to a Help Net Security article by Anthony Diaz, CISO at Exterro. This integration is not occurring through sweeping transformation programs, but rather via subtle, tool-based adoption. These tools, often powered by large language models (LLMs), are embedded in SaaS platforms or piloted internally without centralized oversight. 

For cybersecurity leaders, the concern isn’t the use of AI, but rather the false sense of security it can create. Many organizations assume that popular, “enterprise-ready” models are inherently compliant and secure. In reality, they often obscure the origin, transformation, and use of data, creating a dangerous blind spot: the loss of data provenance.

Data provenance is not just a log; it is the backbone of trustworthy data governance. It traces the lifecycle of data: who handled it, under what conditions, and for what purpose. In LLM-driven systems, that lineage is often severed. Prompts containing sensitive information may not be logged, and outputs can travel across systems without traceability. In highly regulated industries, this lack of transparency poses a significant risk.

The decentralization of AI adoption exacerbates this challenge. Enterprises now face “AI sprawl,” where disparate tools, each with distinct data handling practices, operate beyond the scope of traditional security controls. A single employee can complete an entire data cycle, from input to output, without triggering any alerts or audits. Meanwhile, data protection regulations are not behind; they are evolving alongside AI. The issue is not the rules, but our systems’ inability to meet them.

For CISOs, Diaz says modern AI governance starts with infrastructure, not policy. Security teams must drive automated, contextual data mapping, expand records of processing to include model behavior, enforce dynamic consent reconciliation, and log AI prompts with rigor. AI is not just a data challenge; it’s also a test of traceability. In this new era, visibility and data provenance are the pillars of trust.