| --- |
| title: ARF AI – Governance for Every AI Decision |
| emoji: 🧠 |
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| colorTo: indigo |
| sdk: static |
| pinned: false |
| license: other |
| --- |
| |
| # ARF AI |
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| **Trusted decision infrastructure for AI‑driven operations.** |
|
|
| > 🔒 **The core ARF engine is access‑controlled and not open source.** |
| > Public specification and demo UI are Apache 2.0. |
| > Pilots: outcome‑based pricing – pay for *verified risk reduction*, not API calls. |
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| --- |
|
|
| ## Why ARF AI? Why now, not tomorrow? |
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| Every AI‑assisted decision you *don’t* govern is a liability you *already own*. |
| Unreviewed, unaudited AI actions in production are like signing blank checks. |
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| - **Loss aversion** – The pain of a regulatory fine or operational outage is twice as powerful as the pleasure of moving fast. Avoiding a $500k incident is more valuable than gaining $200k in speed. |
| - **Ambiguity aversion** – Teams hate unclear approval processes. ARF replaces tribal knowledge with deterministic, explainable outcomes. |
| - **Urgency of compounding risk** – Each ungoverned AI decision increases your exposure. The longer you wait, the harder to retroactively audit. |
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| **ARF AI gives you control today.** Not after the breach. Not next quarter. **Now.** |
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| --- |
|
|
| ## What ARF AI does |
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| ARF sits between AI intent and execution. It evaluates every request in real time and returns one of three outcomes: |
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| - ✅ **Approve** – action is safe, within policy, and uncertainty is low. |
| - ⚠️ **Escalate** – insufficient confidence or high impact → human reviewer. |
| - ❌ **Deny** – violates policy, exceeds risk tolerance, or data incomplete. |
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| Every decision is: |
| - **Logged** with a full audit trail (who, what, when, inputs, output). |
| - **Explained** in plain language – no black boxes. |
| - **Signed** (Ed25519) for non‑repudiation. |
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| --- |
|
|
| ## For whom? |
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|
| | Role | Value | |
| |------|-------| |
| | **VP of Engineering** | Reduce operational risk from AI‑generated actions. | |
| | **CTO / CIO** | Maintain speed while adding governance. No full redesign required. | |
| | **Head of Compliance** | Complete, tamper‑evident audit trail for regulators. | |
| | **Security Officer** | Deterministic policy gates that cannot be silently overridden. | |
| | **AI/ML teams** | Get your models into production *with* trust and oversight. | |
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| --- |
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| ## Core engine (protected – no public access) |
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| ARF’s core is a Bayesian governance engine. Key technical components: |
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| ### Bayesian risk fusion |
| Combines online conjugate priors, offline HMC logistic regression, and optional hierarchical hyperpriors. |
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|
| $$ |
| \text{risk} = w_{\text{conj}}\cdot\frac{\alpha}{\alpha+\beta} + w_{\text{hmc}}\cdot p_{\text{hmc}} + w_{\text{hyper}}\cdot \mu_{\text{hyper}} |
| $$ |
| |
| Weights adapt with data volume. Posterior → 90% HDI for uncertainty. |
| |
| ### Expected loss minimisation |
| Chooses the action that minimises expected cost: |
| |
| $$ |
| \begin{aligned} |
| L_{\text{approve}} &= \text{COST\_FP}\cdot R + \text{COST\_IMPACT}\cdot b_{\text{mean}} \\ |
| L_{\text{deny}} &= \text{COST\_FN}\cdot(1-R) + \text{COST\_OPP}\cdot v_{\text{mean}} \\ |
| L_{\text{escalate}} &= \text{COST\_REVIEW} + \text{COST\_UNCERTAINTY}\cdot\psi |
| \end{aligned} |
| $$ |
|
|
| ### Execution ladder (Rust) |
| Mechanical gates: `license` → `confidence` → `risk` → `rollback` → `causal`. Deterministic, auditable. |
|
|
| ### Lyapunov stability |
| Quadratic candidate $V(x,r) = \alpha r^2 + \beta\|x - x_{\text{des}}\|^2$ guarantees healing actions converge. |
| |
| ### Cryptographic signing |
| Ed25519 signatures for `HealingIntent` – non‑repudiable governance. |
| |
| --- |
| |
| ## Public specification (Apache 2.0) |
| |
| - [Data models & API contracts](https://github.com/arf-foundation/arf-spec) – `InfrastructureIntent`, `HealingIntent`, `RiskScore`, `GovernanceLoop` |
| - [Mathematics](https://arf-foundation.github.io/arf-spec/mathematics/) – Full derivations, Lyapunov proof |
| - [Governance loop](https://arf-foundation.github.io/arf-spec/governance/) – Constants (`COST_FP`, `COST_FN`, `EPISTEMIC_ESCALATION_THRESHOLD`) |
| |
| --- |
| |
| ## Live demos (mock data only) |
| |
| | Demo | Description | Link | |
| |------|-------------|------| |
| | **Risk Dashboard** | Adjust priors, see HMC simulation, semantic memory retrieval | [Launch](https://huggingface.co/spaces/A-R-F/Agentic-Reliability-Framework-v4) | |
| | **Sandbox API** | Mock FastAPI endpoint, interactive `/docs` | [Try API](https://huggingface.co/spaces/A-R-F/Agentic-Reliability-Framework-API/docs) | |
| |
| > All demos use simulated responses. Real enforcement requires pilot access. |
| |
| --- |
| |
| ## Public repositories (Apache 2.0) |
| |
| | Repository | Description | |
| |------------|-------------| |
| | [`pitch-deck`](https://github.com/arf-foundation/pitch-deck) | Public overview and investor materials | |
| |
| Private repositories (Core Governance Engine, API Control Plane, Enterprise Extension) are **pilot / enterprise only**. |
| |
| --- |
| |
| ## Access model |
| |
| | Layer | Availability | Purpose | |
| |-------|--------------|---------| |
| | **Public sandbox** | Mock responses only | Demonstration and evaluation | |
| | **Pilot program** | Invitation‑only, time‑limited | Validate use case with controlled access | |
| | **Enterprise core** | Protected production engine | Commercial deployment, full enforcement | |
| |
| --- |
| |
| ## Pilot access – outcome‑based pricing |
| |
| You don’t pay per API call. You pay for **verified risk reduction**. |
| |
| 👉 **[Apply for pilot access →](https://www.arf-ai.com/signup)** |
| |
| When applying, include: |
| - Organization name |
| - Use case (e.g., infrastructure change review, AI‑assisted operations) |
| - Expected volume (approx. evaluations per month) |
| - Cloud environment (AWS, Azure, GCP, on‑prem) |
| |
| Pilot is **time‑limited and free** for qualified organizations. No commitment – just validation. |
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| --- |
| |
| ## Why enterprises choose ARF AI |
| |
| - **Deterministic** – identical inputs → identical decisions. |
| - **Explainable** – every decision includes a human‑readable justification. |
| - **Auditable** – complete, tamper‑evident audit trail. |
| - **Cloud‑agnostic** – runs anywhere (AWS, Azure, GCP, on‑prem). |
| - **Secure** – SSO, RBAC, SOC2‑ready design. |
| - **Preserves speed** – adds governance without forcing a full system redesign. |
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| --- |
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| ## Trust & compliance |
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| ARF is architected for regulated environments: |
| |
| - Tamper‑evident audit trails (regulatory review ready). |
| - Mechanical enforcement – policy gates cannot be bypassed. |
| - Explainable reasoning – suitable for third‑party audits. |
| - Supports GDPR, SOC2, ISO27001 alignment. |
| |
| --- |
| |
| ## Product principles |
| |
| 1. Governance should be deterministic where possible. |
| 2. High‑impact decisions must be explainable. |
| 3. Human review remains available for uncertainty. |
| 4. Auditability is built in, not retrofitted. |
| 5. Enterprise deployment must not require abandoning existing infrastructure. |
| 6. Commercial terms reflect actual value delivered (risk reduction). |
| |
| --- |
| |
| ## Short version |
| |
| ARF AI is the decision layer between AI intent and production execution. |
| It evaluates, decides, and logs – so you can move fast without losing control. |
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| **Don’t wait for the incident that forces governance. Govern every AI decision today.** |
| |
| --- |
| |
| ## Legal & license |
| |
| - **Core engine** – proprietary, trade secret. No public access. |
| - **Public specification and demo UI** – Apache 2.0. |
| - **All materials** – may not be copied, redistributed, reverse engineered, or used for AI training without written permission from ARF Foundation. |
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| --- |
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| *© ARF Foundation. All rights reserved.* |
