Agentic AI Doesn’t Need Perfect Data

Modern AI hasn’t been mainstream for long, yet we’ve all been led to believe that before you can get value from AI, you need “AI-ready” data. Unified pipelines. Clean schemas. Endless prep. But Gartner’s latest research on agentic AI highlights what we’ve seen firsthand: that isn’t true. Gartner recently recognized Tuskira among the providers proving that agentic AI works with the messy, imperfect data enterprises already have. That validation matters because it signals this shift isn’t theory, it’s happening now.

The Old Assumption: AI Requires Clean Data

For more than a decade, enterprises have been told that before you can get value out of advanced analytics (and now AI), you need to clean and centralize your data. That’s why so much time and money have been invested in data lakes, warehouses, and normalization projects.

The logic seems straightforward. Structured, standardized data should make automation and intelligence more reliable. Right?

Well, let’s look at it this way: while teams sink budget and years of effort into endless ETL pipelines and governance frameworks, attackers aren’t sitting around twiddling their thumbs. The SOC is still triaging alerts, vulnerability backlogs continue to grow, and most organizations don’t see tangible results from these “data readiness” efforts for years, if ever.

What was meant to unlock useful business analytics and the possibilities of AI optimization ends up creating costs, delays, and frustration. Which brings us to the shift happening now with agentic AI. Instead of waiting years for clean data pipelines, these systems work the way humans already do, pulling information where it lives, filling in gaps, and acting in real time.

The Shift to Agentic AI:

Agentic AI mirrors how human analysts already work. I can’t imagine any analyst waits for a perfectly normalized database before making a call. They pull logs from SIEM, context from IAM, threat intel from feeds, and tribal knowledge from Slack or email. Then they stitch it together, fill in the blanks, and act.

Gartner refers to this as using data “in situ,” which means working with information where it lives, in its current state.

So there’s the change. The value isn’t in cleaning and centralizing data first. It’s in building agents that can reason across inconsistencies, query the right source at the right time, and still deliver a defensible decision. Instead of demanding perfect inputs, agentic systems focus on reliable outputs: validated risks, prioritized actions, and automated defenses.

What This Means for Security Operations:

Most SOCs probably don’t have “AI-ready” data. What they do have are SIEM alerts, vuln scans, IAM logs, EDR detections, WAF policies, and context scattered across tickets and spreadsheets. 

That’s exactly where agentic AI shines. It doesn’t wait for another massive data overhaul because it works with what’s already there. By unifying signals, correlating across tools, and reasoning through gaps, it delivers validated findings and clear actions.

This is the foundation of Tuskira’s Security Mesh and AI Analysts. The mesh ingests telemetry as-is, without demanding perfect schemas. The analysts simulate attack paths, validate context, and execute defense changes in real time. The result is faster triage, fewer false positives, and controls that adapt as threats evolve.

The Business Impact:

The biggest win is time. With agentic AI, value starts showing up in days, without the years of data prep. SOCs can deploy without waiting for another data warehouse project or normalization effort.

Adoption barriers drop. Instead of re-architecting, security teams use what they already have, with agents reasoning through the messy, imperfect data in place.

And the outcomes are measurable:

• Fewer false positives because analysts spend time on what’s real.
• Faster triage as validated alerts are resolved in minutes.
• Validated risk reduction with attack paths closed before escalation.

Isn’t this what boards want to see? Proof of defense?

Agree with them or not, Gartner’s recognition matters because they often validate new truths in cybersecurity operations. This time, it’s that winning platforms won’t be the ones demanding another multi-year data pipeline project. They’ll be the ones that make messy data actionable today.  Fortunately, that’s where Tuskira fits, and why enterprises are using it to stay ahead of attackers without having to wait for “AI-ready” anything.

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