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If you can’t map your data, can you trust your AI?
As AI becomes more deeply embedded into business operations, organizations are discovering that trustworthy AI depends on trustworthy information. Governance, data lineage, explainability, and human oversight are becoming central to how AI systems are scaled, managed, and defended over time.
As organizations expand their AI use, questions around trust, accountability, and governance are becoming harder to separate from the data behind the outputs AI systems produce.
This was the focus of our April Iron Mountain Education Series webinar, where I was joined by Jarrett Garcia, Director of Enterprise Platform Architecture at Iron Mountain, and Steve Wright, Founder & Chairman of PICCASO. Together, we explored how organizations are rethinking governance, data lineage, explainability, and human oversight as AI becomes more deeply connected to day to day processes.
AI is raising the stakes for data trust
Most conversations about AI start with the technology: the application, model, interface, use case, or productivity gains. But as AI usage becomes the norm, there's a fundamental question to answer: can we trust the data behind it?
AI agents, machine learning models, and generative AI tools don't operate in isolation—they rely on enterprise information to support responses and operational decisions. When that data is incomplete, outdated, poorly classified, or missing context, even a polished answer can be unreliable.
Rubbish in, rubbish out.
Trustworthy AI depends on whether users can validate the answers they receive and understand how those answers were produced. If a tool generates outputs that can't be explained or verified, confidence erodes, adoption slows, and risks to a brand's reputation increases. AI doesn't remove the need for governance, judgment, or accountability—it raises the bar for all three. As AI adoption expands, visibility into the underlying data becomes increasingly important.
Bringing information governance upstream
Information governance, records management, and data privacy teams have spent years building the foundations organizations now need for AI. That work was once viewed primarily through a compliance lens, centered on retention, classification, and lifecycle oversight. But as AI usage becomes the norm, organizations are paying much more attention to overarching governance. Before information can support AI-driven decisions, teams need a clearer understanding of what information exists, where it lives, and whether it can be trusted.
The demand for a good data steward program has just gone up.
AI is also bringing governance and privacy requirements closer together. Questions around accountability, lawful use, and explainability are becoming harder to separate from day-to-day operations as AI moves closer to core business processes. As reliance on AI grows, governance becomes less about downstream oversight and more about determining whether information is fit for purpose before it enters AI systems.
The rising importance of data lineage
The challenge isn't just managing data—it's understanding where information came from and how it's being used. AI systems rely on data that often moves across multiple platforms, teams, and external environments before it ever reaches a model or agent. Without clear lineage and provenance, it becomes difficult to validate outputs or explain how decisions were made.
Treat your data supply chain as critical infrastructure. If you can't map it, you can't trust it.
That pressure is also reshaping how organizations think about explainability. Citations, reasoning cards, and output validation are becoming more important as teams look for clear ways to trace how AI-generated responses were produced.
In response, many organizations are formalizing certification processes around the data used by AI systems. Rather than treating certification as a separate governance exercise, it's emerging as the result of stronger data supply chain management—built on clear traceability, defined ownership, and greater confidence in the data's fit for purpose.
Accountability remains human
Automation is changing how governance work gets done, but it isn't changing where accountability sits. AI now supports work that once depended heavily on manual review, particularly in areas that depend on reviewing information against policy and compliance requirements. That changes the role of governance teams from processing information to interpreting outcomes and deciding where human escalation is required.
You can't take the human out of everything.
This level of oversight becomes more important once AI is involved in decisions that carry lasting consequences. As Jarrett put it during the discussion: "You can't undestroy a box." AI may support operational decision-making, but responsibility still depends on human oversight when decisions need to withstand regulatory scrutiny or be defended over time. Responsible AI isn't about removing humans from the process—it's about applying human judgment where accountability cannot be delegated.
Trusted AI starts with trusted information
The discussion made one point clear: AI is increasing the operational importance of governance that spans AI, data, and information. Oversight of the data behind AI-driven outputs is becoming central to whether organizations can scale AI responsibly and defend the decisions it supports over time.
To hear the full discussion, visit Iron Mountain's 2026 Education Series to watch the on-demand recording of Risk-proofing the AI supply chain: Building trust and integrity.