Predictions 2026 | The AI Reality Check: Why Curated Security Data Wins in 2026

In 2025, enterprises sprinted toward AI. In 2026, they’ll discover that the real differentiator isn’t the model—it’s the quality, normalization, and lineage of the data fueling it. Expect a rollback of AI hype as organizations confront fragmented security tooling, manual reporting, and point-in-time compliance. The leaders who win will shift to continuous controls compliance anchored in curated, clean datasets—then layer agentic AI on top as a “data expertise assistant” to scale decisions, not to shortcut rigor.

These predictions and recommendations come from Tyler Alfriend and Stephanie Whitnable, two of DataBee’s top data experts and practitioners. Drawing on their extensive experience in data engineering and analytics for compliance and cybersecurity, they share what organizations must do to turn AI hype into measurable business outcomes in 2026.

1) From Point-in-Time to Continuous Controls Compliance

Compliance is moving beyond 12-month lookbacks and sample-based testing into full-population monitoring at the data source. This shift makes compliance auditable, repeatable, and defensible—and it’s the only way to support real-time executive reporting and board-level confidence.

“The big intersection point is taking full populations from the data source to perform compliance tests—moving away from point-in-time and sample-based reporting.” — Tyler Alfriend

What to watch: Teams will sunset manual “number-hunting” in spreadsheets and instead, funnel direct, automated feeds from systems of record into compliance analytics.

2) Agentic AI Becomes the Data Expertise Assistant

As compliance gets data-centric, skill gaps surface: policy experts don’t always speak in SQL. In 2026, agentic AI steps in—not to replace domain expertise, but to amplify it. Think of it as a side-by-side assistant that translates policy into queries, navigates complex schemas, and surfaces exceptions—while the human retains judgment, context, and accountability.

“Agentic AI could be the ‘data expertise assistant’ that lets compliance pros operate in a new data-centric world without losing their core skill set.” — Tyler Alfriend

Leadership angle: Executives won’t start their day with dashboards; they’ll start with AI-generated briefings: “What’s in tolerance? Where should I focus?” Visuals still matter—but they become working dashboards for teams to validate, triage, and remediate with embedded automation.

3) Dashboards Don’t Die; They Get Practical

Rather than executive vanity metrics, 2026 rewards operational dashboards connected to automated workflows (tickets, playbooks, remediation actions). AI summarizes; dashboards execute. The cycle becomes: AI briefing → working dashboard → auto-generated remediation → continuous evidence trail.

“Dashboards stay, but as working spaces linked to automation, so teams can verify gaps and trigger remediation actions.” — Stephanie Whitnable

4) The Rise of Curated, Normalized Domain Datasets

AI won’t learn your unique policies or tool quirks out of the box. The key to unlocking AI’s potential is curated, clean domain datasets that abstract local nuance and enable AI to answer many questions—not just the one a single report was designed for. In 2026, data programs invert: build deep datasets first, then let AI generate the metrics and views on demand.

“It’s about building deep datasets around a domain—strip out nuance so AI can thrive.” — Tyler Alfriend

“The first word that comes to mind when I think about ‘AI-ready data’ is clean; normalized, clean data is required for the data sets feeding AI to answer lots of questions.” — Stephanie Whitnable

5) A Unified Security Data Fabric Moves from Idea to Imperative

Security teams live with more tools per capita than any other business unit, which obscures data clarity and creates inconsistent language across consoles. 2026 is the breaking point: the AI wave exposes data fragmentation and accelerates adoption of a security data fabric—a unifying layer that normalizes, enriches, and governs controls evidence across tools, then feeds it to AI and compliance analytics.

“Cybersecurity has way more tools per capita… and paradoxically less awareness of data problems. A security data fabric is the solution behind AI.” — Tyler Alfriend

6) The Great AI Reality Check

Enterprises expecting “push-button AI” will be disappointed. The constraint is data cleanliness and context, not model horsepower. Expect a rollback of hype as home-grown initiatives collide with messy datasets. The best outcome? A refocus on fixing data at the source and investing in people who can bridge business context with analytics and AI.

“It’s not going to work as perfectly as expected. Clean data still hasn’t had its spotlight.” — Stephanie Whitnable

“Don’t treat AI as an easy button. Build a good data environment, and AI can finally do what it’s hyped to do.” — Tyler Alfriend

2026 Playbook: Five Actions to Operationalize These Predictions

1. Embed source-level data into compliance
   Replace sample-based checks with full-population, automated monitoring at the data source.
2. Stand up curated domain datasets
   Normalize controls, events, assets, identities, and policies into clean, governed tables that AI can interrogate broadly.
3. Deploy agentic AI for briefings; keep dashboards for work
   Give leaders morning summaries from AI, then route teams to dashboards with embedded remediation workflows.
4. Invest in people and upskilling
   Pair business context owners with analytics upskilling and AI tools.
5. Adopt a security data fabric
   Reduce “tool-silo sprawl.” Unify telemetry, controls evidence, and policy mappings in one fabric.

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^{The views and opinions expressed by the individuals herein are their own and do not reflect any official policy or position of Comcast. These views and opinions are provided for illustrative purposes only and Comcast makes no warranties, whether express, implied, or statutory, regarding or relating to the accuracy of such statements.}