Security & control
G‑14 does not claim probabilistic models become certain or intrinsically safe. It constrains machine action with explicit authority, bounded runtime, operator command, consequence-aware release, and durable evidence. Teams that need a direct security conversation can contact security@g14.ai.
Generic AI vs. G‑14
What generic AI security leaves unresolved
What G‑14 makes explicit
Four visible control commitments
A serious platform should make four things obvious right away: who is authorized, how unsafe paths stop, how release stays bounded, and how every consequential run remains attributable afterward.
Visible in G‑14
G‑14 treats prompt injection, excessive agency, and ambiguous intent as authorization problems before it lets them become runtime problems.
Visible in G‑14
A serious system has to hold, deny, quarantine, or escalate when the path is unqualified. Security cannot depend on the happy path alone.
Visible in G‑14
The question is not only whether a model can complete a task. It is what else that action can touch, trigger, or expose if release is premature.
Visible in G‑14
Durable evidence, outcome records, and replayable truth are part of the security story because a system that cannot explain itself after action cannot be trusted under pressure.
Threat classes
This is where generic agent software usually becomes hard to trust in real systems.
Hostile or ambiguous inputs
Prompt injection, corrupted context, and adversarial operator inputs should narrow authority, slow the path, or stop the run instead of relying on the model to self-correct.
Excessive agency
The dangerous failure is not only a wrong answer. It is a system taking more action than the present authority, state, or release conditions justify.
Data disclosure and boundary escape
Sensitive context, environment secrets, and protected runtime data should not leak simply because a model was given tools, memory, or a broad workflow.
Context or tenant spoofing
The system has to preserve who the request belongs to, what environment it belongs to, and which policy bundle actually governs it. Weak binding makes every later control suspect.
Opaque incidents
When something goes wrong, buyers need attributable records, replayable truth, and a defensible account of what happened instead of screenshots and memory.
Control response
Each layer closes a different part of the failure surface so machine action stays governable under pressure.
Authority before action
Castor gives the stack a real allow, deny, or escalate decision before action advances. That is how G‑14 treats hostile inputs and excessive agency as control failures instead of only model-quality problems.
Bounded runtime and negative path
Atlas keeps work inside named mission packets, hold points, and terminal outcomes. Missing conditions stop the path, quarantine the work, or force escalation instead of letting ambiguity drift into action.
Operator command under pressure
Apollo and the readiness layer keep humans inside the same governed record as machine work. Review, intervention, and readiness remain visible while the path is live.
Consequence-aware release
Covalence and release discipline help the system reason about blast radius, dependency impact, and whether the next step is fit to propagate. Preparation and release are not treated as the same thing.
Evidence, attestation, and replay
Lamina and the broader evidence model preserve what the system knew, what it did, who intervened, and what survived afterward. That is the difference between a demo log and a defensible post-run record.
Review boundary
The right security posture makes the operating boundary clear and explains when the next step becomes a direct private review.
What is public
The published material explains threat classes, control categories, module responsibilities, deployment boundary, and the security questions a buyer can settle without special access.
What moves into private review
Validation packets, adversarial-runtime results, attestation detail, environment-specific policy thresholds, and sensitive deployment mechanics belong in a private technical review, not on the open web.
What the deployment claim actually means
Company-owned infrastructure with a private, operator-controlled execution boundary matters because it keeps authority, runtime, evidence, and release inside one controlled operating boundary. It is not a slogan about owning the building.
What a serious next step looks like
A real security conversation should end in one narrow question: product security review, deployment-boundary review, high-assurance review, or a decision not to continue.
Read in docs
Use the docs when the conversation needs threat-model detail, runtime containment, evidence posture, and a deeper explanation of the deployment boundary.
Recommended reading
Start here for the published security position: which threat classes G‑14 treats as first-class and how the control stack answers them.
Read in docsRecommended reading
Use this when the question shifts from generic security to hostile inputs, excessive agency, data disclosure, and tenant or context integrity.
Read in docsRecommended reading
Read this when you need to inspect how negative paths stop, hold, or escalate instead of degrading into silent action.
Read in docsRecommended reading
Use this when you need to understand what survives after a run and what a serious after-action review can inspect.
Read in docsReview path
Once the security story is clear, the follow-up should be direct and narrow: security review, high-assurance review, or one technical diligence thread around a bounded concern.
Before this
Trust establishes the control contract. Security shows how that contract behaves under hostile inputs, consequence, and post-run scrutiny.
Open TrustCurrent step
Threat classes, control layers, deployment boundary, and the boundary to private review are explicit.
You are here
Next step
If the deployment or mission context raises the burden of proof further, the next move is the stricter high-assurance path.
Open High assurance
