How FaburAI compares
A different layer, not a better version of the same one.
Most of what is compared against FaburAI is solving an adjacent problem well. The useful question is not which tool is stronger — it is which layer the control sits at, and what that placement makes possible or impossible.
Five approaches, and what each one is shaped for.
Every row below is a real category with real strengths. The limitation beside it is architectural rather than a matter of execution — it follows from where the approach sits, and it would apply to us too if we sat there.
| Approach | What it is shaped for | What its position does not allow |
|---|---|---|
| In-path gateways | One place every AI call passes through, which makes interception straightforward and gives a single point to change behavior. | Carrying the call means being in the availability path for it, and payloads pass through the intermediary. When the intermediary is vendor-operated, that vendor is in the path of your traffic and your data. |
| Documentation-first governance | Control frameworks, attestations, policy registers and audit workflow. The organizing record for a compliance program. | It records intent — the control as described and the policy as written. It does not observe a call, so it cannot show what a given agent actually did. |
| Data catalogs and metadata management | A rich picture of the data estate: lineage, ownership, glossary, stewardship. Built for people to find and understand data. | It describes rather than decides, and it models data rather than callers. AI actors, and the paths from those actors to a column, are not in the model. |
| Identity and API gateways | Authentication, coarse authorization and traffic management for services and users. Mature, and already deployed nearly everywhere. | They authorize the caller they can see. When an agent acts for a person, that is the agent or a shared credential — so the person behind the call is not part of the decision. |
| Model-layer safety and evaluation | Output quality, harmful-content handling, prompt-level attacks, regression testing of model behavior. | It works on what the model produces. Access to systems and data is a different layer — a well-behaved model with an over-scoped credential is still an over-scoped credential. |
The decision travels to the call. The call does not travel to us.
This is the comparison that matters most, because it is the one that changes what happens on a bad day. An intermediary that carries the call is a dependency of the call. A decision point that already holds the policy is not.
Your agents do not depend on us
Decisions are evaluated locally against policy already retrieved. If FaburAI is unreachable, enforcement continues on the last known good policy — agents neither stop nor quietly open up.
Your data does not reach us
The agent, the decision point and the tool are all inside your environment. Request and response bodies are never captured or transmitted; what leaves is a payload-free record of the decision.
Rollout does not require a cutover
Because nothing is being placed in front of your traffic, adopting governance is not a migration of the request path — and a rule can record what it would have denied before it denies anything.
Scale is your own
Call volume is served by your environment rather than by a shared tier somebody else is also using. Governance does not become a throughput conversation.
The trade is real and worth stating: an out-of-path design means we never see your payloads, so we cannot inspect content. That is a deliberate exchange — we give up content inspection to stay out of your availability path and away from your data.
Two different artifacts, and an audit usually wants both.
Documentation-first governance produces the record of what an organization intends: the control as described, the policy as written, the attestation as signed. That record is necessary, and FaburAI does not produce it.
What a control framework holds
The obligation, the control that addresses it, the owner, the review cycle and the sign-off. Organized around the framework being satisfied.
What the runtime record holds
Every governed call: the AI actor, the user role behind it, what it reached, allowed or denied, and the specific rule that decided — alongside who approved that rule and when.
The second exists because enforcement happened, not because a report was scheduled. That is what makes it evidence rather than description — and it is generated by the same act that protected the system in the first place.
Authorizing an agent is not the same as authorizing a person.
Identity providers and API gateways authorize the caller they can see. When an agent acts on someone's behalf, that caller is the agent — or a service credential several agents share — so the person's entitlements are not part of the decision.
Both parties, one decision
Access is decided on the user's role together with the AI actor acting for them. An agent on its own is not an accountable party.
Roles federate in
Sign-in runs through your existing identity provider and roles come from it. We do not become a second source of truth for who someone is.
Unregistered actors still appear
Agents nobody declared are recorded as observed actors when they call, rather than being absent from the picture because no one enrolled them.
Between the AI actor and what it is allowed to reach.
Not in front of the call, and not after the fact in a document. At the moment of the call, inside your environment, deciding on the identity behind it and the path it is taking — and leaving a record because it decided.
That position is what lets the same mechanism govern access, data sensitivity and spend. A token budget is a policy condition alongside a rule about a column, because both are properties of the same call.
The tools that own the rest of this.
Governing AI access is one layer of a larger program. These are the categories that own the others, and none of them is a gap we are working around.
- Identity providers own who someone is.Roles federate in from yours. We consume identity; we do not administer it.
- GRC platforms own the control framework.Obligations, control mapping, attestations and audit workflow live there. We supply the runtime evidence those controls are asserted about.
- SIEM and detection and response own alerting and response.Decision records are exportable into them. Correlation across your whole estate, on-call routing and incident handling belong to that tooling.
- Model safety and evaluation tooling owns model behavior.Output quality, harmful content and prompt-level attacks are that layer's work. We govern what the agent may reach, not what the model says.
- Data catalogs own stewardship at large.Lineage, glossary and ownership across the data estate are theirs. We model the subset that policy is written against, and the AI actors reaching it.
- Finance and FinOps tooling owns the invoice.We attribute consumption to the agent that caused it; reconciling against a bill is theirs.
Every line above is a reason we integrate rather than replace. The record is exportable and the estate model is queryable specifically so it can feed the tooling you already run — we are glad to be one layer in a program rather than ask anyone to rebuild the rest of it around us.
Bring what you are already evaluating.
The most useful version of this conversation starts from what you have in place and what it does not cover, rather than from a feature list.