If you’re a CISO, you’ve seen this movie before.
You commit to Zero Trust. The board likes the direction. The roadmap looks crisp. Then you hit the “Discovery” phase, which, quietly turns from a foundation into a bottleneck. Suddenly, your security team is stuck asking the same four questions over and over:
Discovery inevitably becomes a quarterly spreadsheet ritual - a cycle of exports, manual meetings, and Slack pings to a CMDB that is always one re-org behind reality.
The NSA’s recently published Zero Trust Implementation Guideline - Discovery Phase (January 2026) validates this pain. Its core premise is blunt: If you cannot describe your environment, enforcement becomes guesswork. The guideline also exposes the hard truth CISOs feel every day: manual discovery does not scale. For modern enterprises, it becomes highly expensive and inefficient.
This is where agentic systems change Discovery from a documentation project into a continuous operating model.
Most Zero Trust programs don’t fail because controls are weak. They stall because the system of record is wrong.
The NSA framework emphasizes building confidence across your environment (data, applications, assets, services). Most CISOs aren’t blocked by a lack of controls; they are blocked by a lack of truth.
You likely have scanners, cloud inventories, and governance docs. But when you try to move toward target-level Zero Trust - granular segmentation, access tightening, data policy enforcement - you hit the same wall:
To solve this, we have to stop treating Discovery like a “cleanup” project and start treating it as a loop.
One enterprise customer came to us mid-Zero Trust initiative with a familiar blocker: “We’re not stuck on policy. We’re stuck on ownership and dependencies.”
In a multi-cloud production environment with thousands of services, a new cloud data store appeared and was immediately flagged by security tooling. The CMDB had no owner. The data platform team said, “not ours.” The app team said, “we just consume it.” Meanwhile, the security team couldn’t decide whether to restrict access without breaking production.
With Agentic Discovery, the system pulled the surrounding evidence: a recent change ticket referencing the store, an architecture doc describing the producing service, and an on-call rotation tied to the owning team. The agent proposed the owner and dependency chain with citations, routed a one-click confirmation to the right team lead and then pushed the resolved context back into the system of record.
What changed wasn’t “more visibility.” What changed was that discovery stopped being a series of endless meetings - and became a mechanism.
Traditional Discovery tells you what is missing. Agentic Discovery closes the gap. Instead of just flagging a missing “Owner” field, agentic systems connect to the messy sources where ownership and intent actually live - tickets, runbooks, architecture docs, collaboration threads - and retrieve the missing context with evidence. Humans don’t spend their time hunting; they spend their time confirming and acting.
And the difference is not just automation. Traditional automation follows predefined rules. Agentic Discovery reasons across evidence, weighs confidence, and knows when to ask for human confirmation versus when to act.
That judgment layer is what turns discovery from a reporting function into an operating model.
The goal isn’t just a list; it’s a master inventory that understands dependencies.
The NSA explicitly emphasizes the need for continuously updated metadata.
Zero Trust isn’t a once-and-done state; your environment moves every day.
The NSA’s Discovery Phase is a brilliant roadmap. But without an agentic operating layer, it becomes a labor trap.
Traditional Discovery tells you the map is broken. Agentic Discovery fixes the map while you drive.
That’s how Zero Trust moves from a guideline to an enforceable architecture - continuously, credibly, and at enterprise scale.
