Here's a problem most enterprises haven't fully grappled with yet: when an AI agent acts on your behalf, what should it actually be allowed to see?
The easy answer is to give the agent the same access you have. Angad Narang, VP of Product at Everpure, doesn't think that's the right answer. "It's dangerous as hell," he said during a conversation at Pure Accelerate 2026.
The problem is straightforward once you say it out loud. A person in HR shouldn't have access to finance data. But an AI agent trying to surface useful insights often needs to look across HR, finance, and CRM data simultaneously to find patterns a human working through each system manually would never catch. If you just hand that agent the same role-based permissions the HR person has, you've either locked the agent out of the data it needs to be useful, or — more dangerously — you've quietly expanded what that HR person can effectively see, because the agent is acting with their identity.
Conventional role-based access control wasn't built for this. It assumes a person operating within one system at a time. Agentic AI breaks that assumption by design.
A Multi-Layered Answer
Narang's answer isn't a single fix — it's layered. Role-based access control still has a place at the system and application level. But on top of that, Everpure Data Intelligence (formerly 1touch.io) can tag and selectively obfuscate specific data, hiding personally identifiable information while still allowing an agent to surface trends and patterns from that same underlying dataset.
In practice, that means an agent can analyze finance and HR data together to identify a meaningful business trend, without ever exposing the actual PII to the person who requested the analysis — and without that person needing standing access to either system. The agent gets enough context to be useful. The human gets the insight without the liability of direct access they were never supposed to have.
It's a genuinely difficult problem, and Narang was candid that Everpure doesn't have it fully solved. "We're going to develop as we go along, as we integrate. Very, very early days," he said. "Next year this time, we'll have a much better and integrated offering."
That timeline lines up with data from IDC's June 2026 survey of more than 1,300 organizations, commissioned by Everpure: 41% of respondents cite security as their top challenge to agentic AI deployment, and 39% cite a lack of real-time data access as the second biggest. Those numbers can look contradictory — more access means more risk, but agents need broad access to be useful. Narang's view is that the resolution isn't choosing one over the other. It's building a system precise enough to grant useful context without granting blanket access.
Early Days, Literally
That "early days" framing isn't just cautionary talk. Everpure completed its acquisition of 1touch.io on May 8, 2026 — barely five weeks before this conference. The product that's now central to Everpure's entire data-primacy pitch is still in its first quarter as part of the company.
That context matters for how to read everything else Narang said. The fleet telemetry data that Everpure has collected from its storage systems for years, and the semantic intelligence layer that 1touch (now Data Intelligence) provides, remain separate from these systems. Narang confirmed Prakash Darji's comment from a separate interview that the two are expected to converge within the next year — but as Narang put it, "that hasn't yet completely been integrated into where we are as a company."
The two systems come from different engineering teams with different histories. Getting them to operate on a unified roadmap, Narang said, will take deliberate, sustained coordination — not a quick technical merge.
Building for Now and for Three Years Out
Asked how Everpure decides what to build given how early most customers are in their AI readiness journey, Narang described a split timeline: product vision is set 24 to 36 months out, while near-term engineering work focuses on what customers need today.
That's a fairly standard product leadership answer, but it's grounded in something Narang was unusually direct about: nobody — including Everpure — actually knows what enterprise AI infrastructure needs will look like in three years, because the pace of change makes any confident prediction a guess. The discipline isn't in correctly predicting the future. It's in building toward a long-term direction while staying flexible enough to adjust as that direction becomes clearer.
That tension shows up concretely in the adoption gap Darji flagged in a separate conversation: 80-90% of customers still don't fully understand semantic data management. Narang agreed and was specific about why. Before you can explain why a shared semantic layer matters, you have to first explain what semantics even means in this context — that data carries inherent meaning, and that meaning needs to be understood consistently across applications for AI to use it well. Most enterprise IT conversations are still happening at that first, foundational layer.
The connection to a concept familiar to anyone who uses AI coding tools daily is direct: this is context engineering, applied at the enterprise data layer rather than the prompt layer. The same discipline that separates effective AI tool usage from frustrated AI tool usage — understanding and structuring context rather than just writing better prompts — is what Everpure is trying to build into the infrastructure itself.
The Hardest Engineering Problem
Asked which stage of the data pipeline — ingest, curate, classify, embed, index, retrieve, generate — is hardest to get right, Narang pointed to building the knowledge graph itself: figuring out how different pieces of data, sitting in entirely different systems, actually relate to one another. Everpure has been doing a version of this for years, at both the storage and application levels. The acquisition of 1touch is meant to extend that capability significantly further, building the connective layer across applications rather than within Everpure's own platform alone.
Why Working with Data Where It Lives Matters
Ashish Gupta, GM of Data Management at Everpure, made the case earlier in the week that competing vendors generally want customers to copy their data into a new system. Narang's response to that framing, from a product perspective, was almost dismissive of the difficulty: Everpure has been building toward a unified data plane for years, so working with data wherever it already lives isn't a new challenge to solve — it's the architecture the company has been building toward all along.
His broader point connects directly to something every engineer who has worked with Databricks, Snowflake, or SAP integrations will recognize. Those platforms are powerful, but customers consistently describe them as hard to work with. Narang's argument is that simplicity isn't just a nicer customer experience — it's a security advantage. Complexity creates friction, and friction creates workarounds. Workarounds create the exact security gaps that well-architected, simple systems are designed to avoid.
What's Actually Contested Internally
Asked what product decision is likely to generate the most internal disagreement over the next year, Narang pointed to resource allocation among three priorities: continuing to build faster, more capable storage hardware; investing further in Data Stream's AI pipeline capabilities; or pushing harder to make enterprise data AI-ready through Data Intelligence. There's no obviously correct split, and reasonable people on his team will weigh those trade-offs differently.
It's a candid acknowledgment that Everpure's rebrand and broader platform ambitions create real internal tension over where engineering investment should go — not just a messaging challenge to manage externally.
What Actually Matters to Developers
Asked what he thought developers, engineers, and architects most want to know that hadn't already come up, Narang's answer was simple: they want to know how this makes their lives easier.
That's consistent with what Everpure has been arguing all week, just stated more plainly than the product slides do. Narang described the kind of task that used to take him months — pulling information from Salesforce and connecting it to order management data to understand a full year of business activity — and said that with the context already established by tools like Data Intelligence, the same task could realistically take an hour.
The bigger shift he pointed to isn't really about engineers at all. It's about extending that same capability to business users with no technical background by building a semantic context that lets AI surface insights they'd otherwise need a data team to produce. Engineers are already living this shift directly — many are now writing the majority of their code with AI assistance rather than from scratch, a point Narang didn't dispute. The same underlying capability, applied to enterprise data, is what Everpure is betting will extend that kind of leverage to everyone else in the business.
Whether Everpure executes on that vision faster than the access control, governance, and integration challenges it just openly admitted to still working through remains the real story to watch over the next year.
Everpure (NYSE: P) provided press access to Pure Accelerate 2026. The IDC research cited in this article was commissioned by Everpure.