Platform
Five capabilities on one record, with no export, no re-keying, and no gap between them.
Every capability feeds the next. Discovery informs identity mapping, which informs risk scoring, which drives real-time enforcement and continuous governance.
Your people are already using AI tools that nobody approved, and most companies cannot name even half of them. You cannot protect what you cannot see. AIBound finds every AI tool, agent, MCP server, extension, and API running across your browsers, endpoints, network, and cloud. You get the full list in under 24 hours, and there is nothing new to install.
Knowing an AI tool exists is only half the story. The real question is who is using it, and what it can reach on their behalf. AIBound maps every AI to the people and the machine accounts behind it, then traces exactly what data, systems, and agents each one can touch. Now you can see which AI sits one step away from your most sensitive information.
Once you have a long list of AI tools, everything looks urgent and nothing gets fixed. Teams argue about what matters instead of acting. AIBound grades every AI from A to F using how severe the technical risk is, how the business uses it, how sensitive the data is, and how far the damage could spread. One clear grade tells you what to deal with first.
Finding a risky AI tool does not help much if it takes weeks to act on it. Most teams end up filing tickets while the risk stays live. AIBound turns a risk grade into action straight away, blocking, approving, or fixing high-risk AI in real time. It works through the endpoint, network, and identity tools you already own, so there is nothing new to roll out.
When an auditor or a board member asks what AI you are running and whether it is safe, most teams spend weeks building the answer by hand. By the time the report is finished, it is already out of date. AIBound builds that evidence for you continuously, straight from your live AI inventory. Audit trails and framework-mapped reporting stay current on their own, so you are always ready to answer.
A point tool is not worse than a platform at its own job. It is worse at the four moments when its output has to become somebody else's input. Those moments are the product.
What crosses: the resource record.
What the receiver does with it: resolves the record to the principal it actually runs as, human account or service account, and traces every system, dataset, and agent that principal can reach.
Without the join: discovery hands over a list of names, and somebody looks each one up by hand in a separate identity system, which is the moment an export enters the process. An exported list is accurate on the day it is exported and never again, and the fleet it describes changes daily.
What crosses: blast radius, the reach a resource has once it is tied to an identity.
What the receiver does with it: carries that reach onto the resource's scored record, presented next to the grade as the business context for how far the resource can go if something goes wrong.
Without the join: the grade shows technical severity with no reach printed beside it. A model that only summarizes public documents and an agent holding a production database credential can carry the same finding, and a queue sorted on that score alone sends the analyst to the wrong one first.
What crosses: the grade.
What the receiver does with it: uses the grade together with the resource's context to choose a response by rule, alerting, routing to a governed review queue, opening a ticket in the system that already owns remediation, or blocking.
Without the join: the grade lands on a dashboard and a person decides case by case. An inconsistent record is exactly what an auditor pulls on, because the question is never whether a decision was reasonable, it is whether the same facts produced the same decision every time.
What crosses: the action log.
What the receiver does with it: turns each action into evidence carrying its own timestamp, its own trigger, and the control it maps to, generated from live state rather than assembled for a meeting.
Without the join: evidence gets reconstructed after the fact from screenshots and exports. That survives a first audit, because everyone knows the estate is new. It rarely survives a fifth.
Worth stating plainly, because it is the most common misreading of this page: the block in join 03 and the log in join 04 do not happen inside AIBound. Enforcement executes through the endpoint, network, and identity controls already in place, which is why there is nothing new to install and nothing to roll out to a device. What the platform owns is the decision and the record of it, not the mechanism.
Discovery without identity is noise. Risk scores without enforcement are dashboards. AIBound is the only platform where signal flows end-to-end, from the moment an AI is discovered to the moment it's blocked or governed.
An AI security control plane is a single system that discovers AI resources, resolves each one to the identity it runs as, grades its risk, applies policy, and produces the evidence, all against one shared record rather than five separate ones. The distinction from a collection of tools is not feature count. It is whether the output of each stage is the direct input of the next without an export, a re-key, or a person in between.
Because the value sits in the four boundaries between them, and a boundary belongs to neither tool on either side of it. Discovery that cannot hand a record to identity resolution produces a list. Identity data that cannot reach the scoring model produces a grade built on technical severity alone. A grade that cannot reach enforcement is a dashboard. An action that cannot reach reporting has to be reconstructed from screenshots later.
Yes, and the order matters when you do. Discovery is the only stage with no upstream dependency, so it is the only one that produces a complete result on its own. Every later stage consumes a field that an earlier stage wrote, so adopting scoring before identity means scoring on technical severity alone, and adopting reporting first means reporting on an inventory that was compiled by hand for that report.
Nothing is installed on endpoints. Deployment is agentless and read-only, which is the stated basis for a complete inventory in under 24 hours rather than the quarter an endpoint rollout takes to negotiate. Enforcement actions execute through the endpoint, network, and identity controls you already own, so blocking does not introduce a new agent either.
The platform is not a one time scan. Discovery, identity resolution, scoring, enforcement, and reporting all read from and write to the same live record, so a newly appearing AI tool or a newly granted identity connection shows up in that one place rather than waiting for the next manual pass. Each downstream capability sees the update the moment the upstream one records it, which is the same property that lets reporting generate evidence continuously instead of compiling it for a specific date.
The inventory comes first, in under 24 hours, because every later artefact is generated from it. Grades follow once resources are discovered and resolved to identities. Enforcement records begin accumulating from the first policy action, and framework-mapped reporting draws on all of the above. Stating the sequence matters: an evidence artefact is only as current as the inventory underneath it.
A complete AI inventory in under 24 hours, agentless and read-only, with every downstream capability reading from it rather than from an export.