Kyoto AI RMF NIST AI 100-1 Compliance Engine
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Live Demo Mode
πŸ›‘οΈ Executive Compliance Showcase Live Kyoto Cloud NIST AI 100-1 β€’ ISO 42001 β€’ EU AI Act

Automated AI Governance, Safety & Audit Engine

Enterprise AI brings transformative capabilities, but regulators and boardrooms demand proof of safety. This interactive walkthrough demonstrates how our engine discovers AI assets, scans code for safety vulnerabilities, deploys automated guardrails, records cryptographic executive sign-offs, and generates audit deliverables in 5 intuitive steps.

Target System
demo-rag-app
FastAPI + LangChain Core
AI Backbone
Google Gemini 1.5 Flash
Cloud LLM via Vertex AI
Risk Classification
Moderate Impact Tier
Customer-Facing with PII Access
Current Readiness Score
11.1% Met (8 of 72)
⚠️ Baseline (Action Required)
1 πŸ” 1. Discovery (MAP)
2 βš–οΈ 2. Risk Audit (MEASURE)
3 πŸ›‘οΈ 3. Attack & Fix (MANAGE)
4 ✍️ 4. Executive Sign-Off (GOVERN)
5 πŸ“¦ 5. Audit Deliverables (REPORTS)
Step 1 of 5 β€’ NIST MAP Function

1. Understand & Catalog Your AI Architecture

βœ… 4 Components Identified
The Plain-English Concept: You cannot govern what you cannot see. Our automated scanner analyzes the target code repository, container dependencies, and runtime environment to produce an exact software & model inventory.
πŸ€–
Backbone AI Model
Google Gemini 1.5 Flash
Cloud LLM endpoint accessed via Google Vertex AI API.
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Orchestration Framework
FastAPI + LangChain Core
Retrieval-Augmented Generation (RAG) assistant pipeline.
πŸ—„οΈ
Knowledge Base Store
FAISS Vector Index
Embeddings database storing enterprise product manuals & FAQs.
πŸ›‘οΈ
Regulatory Profile
Moderate Impact Tier
Customer-facing service with access to order history and PII.
πŸ“Œ Mapped to: NIST MAP 1.1 NIST MAP 1.5 EU AI Act Art. 11 ISO 42001 A.6
Step 2 of 5 β€’ NIST MEASURE Function

2. Automated Code & Vulnerability Audit

⚠️ 2 Critical Safety Gaps Detected
The Plain-English Concept: Traditional web firewalls cannot stop AI attacks. Our automated auditor performs static code scanning, architecture analysis, and simulated stress tests across all 72 NIST subcategories to uncover vulnerabilities.
CRITICAL GAP Prompt Injection Vulnerability (NIST MEASURE 2.6 / MANAGE 2.4)
User input flows directly into model prompts without validation. Attackers can override instructions to extract sensitive database credentials.
Status: OPEN / FAILING
CRITICAL GAP Context PII & Data Leakage Risk (NIST MANAGE 2.4 / MEASURE 2.5)
Chat memory passes raw customer contact info and credit numbers to external cloud LLM APIs without regex redaction.
Status: OPEN / FAILING
MODERATE GAP Missing Machine-Readable ML-BOM (NIST MAP 1.5 / EU AI Act Art. 11)
Model weights and supply-chain packages lack a cryptographically signed CycloneDX 1.6 Bill of Materials.
Status: UNVERIFIED
PASSING Transport Encryption & TLS (NIST MANAGE 4.1)
All incoming and outgoing traffic uses TLS 1.3 encryption.
Status: MET
Step 3 of 5 β€’ NIST MANAGE Function

3. Test Adversarial Attacks & Deploy 1-Click Guardrails

⚠️ System Unprotected
The Plain-English Concept: Test how an attacker tricks the AI assistant. Click one of the sample prompt attacks below, test it against the application, and then click Deploy Drop-In Safety Guardrails to watch our safety bouncer intercept malicious prompts in real time.
🎯 Adversarial Prompt Sandbox (Choose a pre-filled query or write your own)
Simulates live execution against FastAPI endpoint with Gemini 1.5

πŸ› οΈ Fix the Vulnerability: 1-Click Guardrail Injection

In traditional security audits, engineering teams spend weeks configuring custom interceptors. Our engine automatically injects drop-in NeMo input validation guardrails and PII tokenizers directly into the application pipeline.

Step 4 of 5 β€’ NIST GOVERN Function

4. Executive Governance Sign-Off & Attestation

⏳ Pending Executive Sign-Off
The Plain-English Concept: AI cannot be legally accountableβ€”humans must be. Regulators (and the EU AI Act) mandate that designated leadership officially attest to the risk posture and safety controls. Our engine anchors every signature into an immutable SHA-256 ledger.
Anchored to tamper-proof SHA-256 evidence chain
Step 5 of 5 β€’ Regulatory Proof

5. Export Audit Deliverables for Regulators & Board

✨ 5 Standard Deliverables Ready
The Plain-English Concept: Compliance is only as good as the proof you provide to external auditors, regulatory agencies (FTC, EU AI Office), and enterprise customers. The engine instantly packages all findings into industry-standard deliverables.
C-Suite & Board Executive Summary

Visual briefing with maturity charts, residual risk analysis, and signed executive attestation seal.

πŸ‘οΈ Preview HTML
Public Disclosure AI Nutrition Label

Consumer-facing transparency card detailing data sources, safety guardrails, and known limitations.

πŸ‘οΈ Preview HTML
Supply Chain CycloneDX ML-BOM

Machine-readable Software & Model Bill of Materials cataloging packages, model weights, and licenses.

πŸ“„ View JSON
Federal / FedRAMP NIST OSCAL Assessment

Automated security assessment results adhering to the official NIST Open Security Controls format.

πŸ“„ View JSON
Cryptographic Proof Evidence Bundle ZIP

Complete verifiable zip archive containing the immutable ledger, audit traces, and signed policies.

πŸ“₯ Download ZIP

Ready to Inspect the Full 72-Control Auditor Console?

You have experienced the 5-step executive workflow. Now switch to the deep technical view to inspect individual NIST AI RMF subcategories, edit policy documents, or run custom archetype evaluations.

Interactive Compliance Demo Live Kyoto Cloud

NIST AI 100-1 Compliance Audit & Evidence Engine

Continuous AI governance, automated code-level scanning, and machine-readable regulatory compliance. Auditing an enterprise RAG assistant against all 72 NIST AI RMF subcategories.

Audited Target: demo-rag-app (FastAPI + LangChain)
AI Backbone: gemini-1.5-flash (Google Vertex AI)
Risk Profile: MODERATE TIER
Baseline Score: 11.1% Met (8 of 72 Subcategories)
πŸ’‘
Why is the baseline score 11.1%? (Fail-Closed Architecture)
Automated technical scans (pip-audit dependency vulnerability analysis, Fairlearn demographic bias probes, and prompt-injection defenses) have already verified 8 technical controls. Under NIST AI RMF 1.0 guidelines, organizational governance, policy sign-offs, and human accountability cannot be assumed by code alone. Follow the 4-step interactive journey below to experience how human attestations and evidence verification work!

Recommended Demo Journey: 4 Quick Steps

Experience how developers, compliance officers, and auditors collaborate on AI governance
Interactive Walkthrough
Step 1 β€’ Technical Scans
🧭 Inspect 72 Controls
Filter across GOVERN, MAP, MEASURE, and MANAGE. View automated technical scans like MAP 4.1 with pip-audit SHA-256 evidence.
Step 2 β€’ Governance Docs
πŸ“œ Review AI Policies
Read the 9 auto-generated governance policies tailored to this RAG system: System Card, AI Use Policy, and Incident Response Playbook.
Step 3 β€’ Live Simulation
✍️ Sign GOVERN 1.1
Simulate human compliance sign-off. Enter your name, commit with SHA-256, and watch the readiness dials increment live!
Step 4 β€’ Compliance Exports
πŸ“¦ Audit Deliverables
Preview the standalone Executive HTML brief, FDA-style AI Nutrition Label, CycloneDX 1.6 ML-BOM, and NIST OSCAL 1.1 JSON.
0%
Overall
0 / 72 Met
0%
Govern
0 / 19 Met
0%
Map
0 / 18 Met
0%
Measure
0 / 22 Met
0%
Manage
0 / 13 Met
Subcategories Breakdown
--
MET / PARTIAL / GAP
Evidence Records
--
Cryptographically Hashed (SHA-256)
Target Architecture
--
Provider / Model Profile
Ledger Integrity
VALIDATED
Last Snapshot: Now
πŸ›οΈ Auditor Deliverables & Compliance Hub 5 Regulatory Standards
Instant 1-click preview and download of regulatory audit deliverables generated from this snapshot
Executive HTML Executive Summary

Self-contained styled report with maturity charts and executive attestation sign-offs.

πŸ‘οΈ Preview HTML
Transparency AI Nutrition Label

FDA-style consumer transparency card detailing datasets, bias audits, and guardrails.

πŸ‘οΈ Preview Label
CycloneDX 1.6 ML-BOM (JSON)

Machine-readable ML Bill of Materials: model components, FAISS vector index, SHA-256 hashes.

πŸ“₯ Download JSON
NIST OSCAL 1.1 OSCAL Profiles

Open Security Controls Assessment Language Assessment Results & Component Definitions.

πŸ“₯ Assessment Results
Ed25519 Signed Evidence Bundle

Complete cryptographic .ZIP archive with digital signature manifest and all audit logs.

πŸ“¦ Download ZIP
Priority Next Compliance Actions
Ranked by risk tier weight, category impact, and missing evidence
Subcategory Function Statement & Priority Rationale Est. Effort Action
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Compliance Readiness History
Historical trajectory from workspace/ledger_history.jsonl
Timestamp (UTC) Action Actor Readiness Subcategory Counts (Met / Partial / Gap)
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Project Setup & Archetype Interview
Configure system scope, risk tiers, and tailor canonical AI RMF subcategories
Unique system identifier for the compliance ledger and attestation certificates.
Tailors mandatory subcategories and risk thresholds.
Loading NIST AI RMF subcategories...
Workspace Docs
Loading documents...
Select a document
Document Review Attestation: Sign compliance attestation citing this document as formal evidence.
Comprehensive Compliance Artifacts & Export Engine
Generate machine-readable standards (OSCAL, CycloneDX) and executive audit deliverables
Executive Deliverable
Executive Compliance Summary
Stand-alone styled HTML report with functional maturity breakdowns, leadership attestation sign-offs, and risk matrices.
πŸ‘οΈ View HTML Report
NIST OSCAL 1.1
Machine-Readable Profiles
Complete NIST Open Security Controls Assessment Language exports: Component Definitions, Assessment Results, and POA&M.
πŸ“₯ Component Definition (JSON) πŸ“₯ Assessment Results (JSON) πŸ“₯ Plan of Action & Milestones (JSON)
CycloneDX 1.6
Machine Learning BOM
Standardized Software/ML Bill of Materials containing model architecture, datasets, dependencies, and SHA-256 hashes.
πŸ“₯ Download ML-BOM (JSON)
Consumer Transparency
AI Nutrition Label
FDA-style model facts card disclosing training datasets, bias audits, autonomy boundaries, and safety guardrails.
πŸ‘οΈ View Nutrition Label
Cryptographic Bundle
Signed Evidence Bundle
Complete cryptographic archive containing all evidence artifacts, compliance ledger, manifest, and SHA-256 signatures.
πŸ“¦ Download Bundle (.ZIP)