Why GraphnAI
The identity security industry solved visibility a decade ago. It still hasn't solved action.
Your team has tools that find identity risks. Thousands of findings. Detailed reports. The problem isn't finding them. It's the gap between knowing what's wrong and having the operational confidence to fix it without breaking production.
The Fear of Consequences
Every security team we've talked to has the same story. A service account in AD with full control over a GPO that configures every domain controller. It's been that way since 2019. The account owner left the company two years ago. Nobody knows what depends on it. So it stays.
A group with 47 members that bridges your HR OU to your Domain Controllers OU. Removing it would fix a critical junction finding. It might also break the payroll batch job that runs every Friday at midnight. Nobody can prove it won't. So the finding stays open for another quarter.
This isn't a skill problem. Your team knows what to fix. They're stuck because no tool answers the question that actually matters: what breaks if I do this? That's a simulation problem, not a scanning problem. And scanners don't simulate.
What the Current Tools Get Wrong
Attack path tools show you graphs. Impressive visualizations of every theoretical path from any identity to Domain Admin. The problem: most of those paths have never been used. Some are blocked by conditional access policies. Others require Kerberos delegation chains that haven't fired in years. When everything looks exploitable, nothing gets prioritized. Your team stares at the graph and doesn't know where to start.
Posture tools generate findings lists sorted by severity. High. Critical. Critical. Critical. Hundreds of critical findings, and no context for which one to fix first. Is that over-permissioned service account more dangerous than the stale computer object with unconstrained delegation? The tool can't tell you, because it scores permissions in isolation without considering who holds them, what they can reach, or whether anyone is actually using the path.
Detection tools alert on threats after they happen. Kerberoasting detected. Password spray in progress. But by the time the alert fires, the posture weakness that enabled the attack has been sitting in your environment for months. Detection without posture management is a fire alarm with no sprinkler system.
How GraphnAI Is Different
Tiered Fidelity™ separates real exposure from noise
Every access relationship carries an evidence tier. Theoretical means the permission exists in configuration. Constrained means policy or network controls narrow it. Validated means authentication telemetry proves the path is live. Your team stops chasing thousands of theoretical paths and focuses on the ones that are actually being used. As telemetry flows in, relationships promote automatically. No manual tuning. No threshold spreadsheets.
Simulation answers "what breaks?" before you touch anything
The Differential State Engine™ runs every proposed change against a delta overlay of the identity graph. Remove a group membership in the overlay. Instantly see every identity that loses access, every downstream dependency, and the specific systems affected. The Operational Safety Metric™ turns gut feelings into a number you can take to change management. Run three scenarios side by side if you want. They're lightweight and read-only. Nothing hits production until you say so.
Select Fire™ gives you graduated remediation control
Not every finding needs the same treatment. Safe mode generates the remediation script with full rollback instructions. You read every line before anything executes. Semi-Auto stages the fix and waits for one-click approval. Full-Auto handles the obvious wins: stale accounts inactive for 400+ days, expired group memberships, orphaned service principals. You decide per finding, and you graduate from Safe to Semi-Auto as you build trust. Backlog burn-down becomes a reportable metric, not a quarterly aspiration.
One platform covers posture, attack surface, and detection
Most vendors sell you one piece and expect you to integrate the rest. A posture scanner that can't show you the attack paths. An attack path tool that can't detect active threats. A detection tool that can't remediate. GraphnAI covers posture management, attack surface analysis, and threat detection in one graph, one platform, one pane of glass. The detection engine feeds validated fidelity data back into the posture model. The posture findings flow directly into remediation workflows. No integration tax.
Four Patent-Pending Inventions
Tiered Fidelity™, Identity Folding™ edge consolidation, Criticality-Asymmetric Risk Scoring, and Delegation Posture Analysis. Four provisional patent applications covering novel approaches to problems the industry has been working around for years. These aren't repackaged open-source techniques with a UI bolted on. They're architecturally distinct methods for evidence grading, graph simplification, risk prioritization, and delegation analysis that don't exist in any competing product.
Built by Someone Who Lived the Problem
GraphnAI was founded by Josh Bryant, a 27-year cybersecurity veteran who spent years at Microsoft flying to customer sites worldwide to lead Active Directory breach recoveries. He watched the same pattern at every organization: the security team had findings. They had tools. They had dashboards. What they didn't have was the ability to safely act on what they found. At Tanium, he co-created the Impact module and built identity security products deployed across millions of endpoints in the Global 2000. He is a named inventor on US Patent 11,831,670.
The gap between finding identity risks and fixing them was the same gap at every organization, every year, for two decades. GraphnAI was built to close it.
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