Building Your Own AI Assistant for SCBE
Guide to creating a custom AI assistant that understands and operates within the SCBE-AETHERMOORE security framework
Overview
You can build an AI assistant that:
- Understands SCBE concepts - Trained on your documentation
- Operates securely - Uses SCBE’s 14-layer security for all operations
- Has a spectral identity - Unique rainbow fingerprint for verification
- Follows Sacred Tongue governance - Multi-signature consensus for actions
Option 1: Custom GPT (Easiest)
Create a Custom GPT on OpenAI with SCBE knowledge:
Setup Steps
- Go to chat.openai.com/gpts
- Click “Create a GPT”
- Upload these files as knowledge:
docs/ARCHITECTURE.mddocs/API.mddocs/LANGUES_WEIGHTING_SYSTEM.mddocs/SACRED_TONGUE_SPECTRAL_MAP.mdSCBE_CHEATSHEET.md
System Prompt
You are SCBE-Assistant, an AI security expert specializing in the SCBE-AETHERMOORE framework.
Your capabilities:
- Explain the 14-layer security pipeline
- Help users implement SCBE in their projects
- Calculate trust scores using the Langues Weighting System
- Generate spectral identities for entities
- Advise on Sacred Tongue governance configurations
Your identity:
- Spectral Hash: SP-SCBE-ASST
- Primary Color: Sapphire (#0F52BA) - representing Cassisivadan (verification)
- Trust Level: HIGH
Always respond with security-first thinking. When users ask about implementing features,
reference the appropriate SCBE layer and Sacred Tongue.
Key formulas you know:
- Langues Metric: L(x,t) = Σ w_l × exp[β_l × (d_l + sin(ω_l×t + φ_l))]
- Harmonic Scaling: H(d) = R^(d/d₀)
- Trust Classification: HIGH (≤0.3), MEDIUM (0.3-0.5), LOW (0.5-0.7), CRITICAL (>0.7)
Option 2: Local AI with Ollama + SCBE Integration
Run a local AI that uses SCBE for security:
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ YOUR SCBE AI ASSISTANT │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Ollama │───▶│ SCBE │───▶│ Response │ │
│ │ (LLM) │ │ Security │ │ Filter │ │
│ └──────────────┘ │ Gate │ └──────────────┘ │
│ └──────────────┘ │
│ │ │
│ ┌──────▼──────┐ │
│ │ 14-Layer │ │
│ │ Pipeline │ │
│ └──────┬──────┘ │
│ │ │
│ ┌───────────────────┼───────────────────┐ │
│ │ │ │ │
│ ┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐ │
│ │ Trust │ │ Spectral │ │ Audit │ │
│ │ Manager │ │ Identity │ │ Logger │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
Implementation
# scbe_ai_assistant.py
"""
SCBE-Secured AI Assistant
Uses Ollama for local LLM + SCBE for security
"""
import ollama
from src.harmonic.spectral_identity import (
SpectralIdentityGenerator,
generate_spectral_identity
)
from src.scbe.context_encoder import ContextEncoder
from src.crypto.sacred_tongues import SacredTongueManager
class SCBEAIAssistant:
"""AI Assistant secured by SCBE 14-layer pipeline"""
def __init__(self, model: str = "llama3.2"):
self.model = model
self.spectral_gen = SpectralIdentityGenerator()
self.context_encoder = ContextEncoder()
self.tongue_manager = SacredTongueManager()
# Generate assistant's spectral identity
self.identity = self.spectral_gen.generate_identity(
entity_id="scbe-assistant",
trust_vector=[0.9, 0.8, 0.85, 0.95, 0.9, 0.7] # High trust
)
print(f"🌈 Assistant Spectral Identity: {self.identity.spectral_hash}")
print(f" Color: {self.identity.hex_code} ({self.identity.color_name})")
def process_query(self, user_query: str, user_context: dict) -> str:
"""Process user query through SCBE security pipeline"""
# Step 1: Generate user's spectral identity
user_trust = user_context.get('trust_vector', [0.5]*6)
user_identity = self.spectral_gen.generate_identity(
entity_id=user_context.get('user_id', 'anonymous'),
trust_vector=user_trust
)
# Step 2: Encode context (Layer 1-2)
context_hash = self.context_encoder.encode({
'user_id': user_context.get('user_id'),
'query': user_query,
'timestamp': user_context.get('timestamp'),
'spectral_hash': user_identity.spectral_hash
})
# Step 3: Check trust level
if user_identity.confidence == 'LOW':
return "⚠️ Trust level too low. Please verify your identity."
# Step 4: Get Sacred Tongue approval for action type
action_type = self._classify_action(user_query)
required_tongues = self.tongue_manager.get_required_tongues(action_type)
# Step 5: Generate response via LLM
system_prompt = self._build_system_prompt(user_identity)
response = ollama.chat(
model=self.model,
messages=[
{'role': 'system', 'content': system_prompt},
{'role': 'user', 'content': user_query}
]
)
# Step 6: Filter response through SCBE
filtered_response = self._filter_response(
response['message']['content'],
user_identity
)
# Step 7: Audit log
self._audit_log(user_query, filtered_response, user_identity)
return filtered_response
def _build_system_prompt(self, user_identity) -> str:
"""Build system prompt with SCBE context"""
return f"""You are an SCBE-secured AI assistant.
User Spectral Identity:
- Hash: {user_identity.spectral_hash}
- Color: {user_identity.hex_code}
- Confidence: {user_identity.confidence}
Your responses must:
1. Never reveal sensitive system information
2. Respect the user's trust level
3. Follow Sacred Tongue governance rules
4. Be helpful within security constraints
You have knowledge of the SCBE-AETHERMOORE framework including:
- 14-layer security pipeline
- Langues Weighting System
- Sacred Tongue governance
- Spectral Identity System
"""
def _classify_action(self, query: str) -> str:
"""Classify query into action type for governance"""
query_lower = query.lower()
if any(w in query_lower for w in ['delete', 'remove', 'destroy']):
return 'DESTROY'
elif any(w in query_lower for w in ['deploy', 'publish', 'release']):
return 'DEPLOY'
elif any(w in query_lower for w in ['create', 'write', 'generate']):
return 'CREATE'
elif any(w in query_lower for w in ['update', 'modify', 'change']):
return 'UPDATE'
else:
return 'READ'
def _filter_response(self, response: str, user_identity) -> str:
"""Filter response based on user trust level"""
# Add spectral signature to response
return f"{response}\n\n---\n🌈 Verified by: {self.identity.spectral_hash}"
def _audit_log(self, query: str, response: str, user_identity):
"""Log interaction for audit trail"""
print(f"[AUDIT] User: {user_identity.spectral_hash} | Action: Query")
# Usage
if __name__ == "__main__":
assistant = SCBEAIAssistant()
response = assistant.process_query(
user_query="Explain the Langues Weighting System",
user_context={
'user_id': 'developer-123',
'trust_vector': [0.8, 0.7, 0.9, 0.6, 0.8, 0.5],
'timestamp': '2026-01-20T18:00:00Z'
}
)
print(response)
Option 3: Strands Agent SDK (AWS)
Use AWS Strands to build an SCBE-powered agent:
# Using Strands Agent SDK with SCBE
from strands import Agent
from strands.tools import tool
@tool
def compute_trust_score(trust_vector: list[float]) -> dict:
"""Compute SCBE trust score from 6D vector"""
from src.spaceTor.trust_manager import TrustManager
manager = TrustManager()
score = manager.compute_trust_score("agent", trust_vector)
return {
"raw": score.raw,
"normalized": score.normalized,
"level": score.level
}
@tool
def generate_spectral_identity(entity_id: str, trust_vector: list[float]) -> dict:
"""Generate spectral identity for an entity"""
from src.harmonic.spectral_identity import spectral_generator
identity = spectral_generator.generate_identity(entity_id, trust_vector)
return {
"hex_code": identity.hex_code,
"spectral_hash": identity.spectral_hash,
"color_name": identity.color_name,
"confidence": identity.confidence
}
# Create SCBE-powered agent
scbe_agent = Agent(
name="SCBE-Security-Agent",
model="anthropic.claude-sonnet",
tools=[compute_trust_score, generate_spectral_identity],
system_prompt="""You are an SCBE security agent.
Use the trust scoring and spectral identity tools to verify entities.
Always compute trust before allowing sensitive operations."""
)
Option 4: Kiro Custom Agent
Create a custom agent in Kiro that uses SCBE:
Agent Definition (.github/agents/scbe-assistant.agent.md)
# SCBE Security Assistant
## Role
You are an AI assistant specialized in the SCBE-AETHERMOORE security framework.
## Capabilities
- Explain SCBE concepts and architecture
- Help implement security features
- Calculate trust scores
- Generate spectral identities
- Advise on Sacred Tongue governance
## Knowledge Base
- 14-layer security pipeline
- Langues Weighting System (6D trust vectors)
- Sacred Tongue governance (KO, AV, RU, CA, UM, DR)
- Spectral Identity System (rainbow fingerprinting)
- Post-quantum cryptography (ML-KEM, ML-DSA)
## Response Style
- Security-first thinking
- Reference specific SCBE layers when relevant
- Include spectral identity information when discussing entities
- Use Sacred Tongue terminology appropriately
## Tools Available
- File reading/writing
- Code execution
- Web search for security research
Spectral Identity for Your Assistant
Every AI assistant should have its own spectral identity:
// Generate your assistant's identity
const assistantIdentity = spectralGenerator.generateIdentity(
'my-scbe-assistant',
[0.95, 0.85, 0.90, 0.92, 0.88, 0.80] // High trust across all tongues
);
console.log(`
╔══════════════════════════════════════════╗
║ MY SCBE AI ASSISTANT ║
╠══════════════════════════════════════════╣
║ Spectral Hash: ${assistantIdentity.spectralHash} ║
║ Color: ${assistantIdentity.hexCode} (${assistantIdentity.colorName}) ║
║ Confidence: ${assistantIdentity.confidence} ║
╚══════════════════════════════════════════╝
`);
Security Considerations
When building your AI assistant:
- Always verify spectral identity before processing sensitive requests
- Use Sacred Tongue governance for action approval
- Log all interactions through the audit system
- Apply trust decay for inactive sessions
- Implement rate limiting based on trust level
Next Steps
- Choose your platform (Custom GPT, Ollama, Strands, or Kiro)
- Generate a spectral identity for your assistant
- Configure Sacred Tongue governance rules
- Implement the 14-layer security pipeline
- Test with various trust levels
Build secure AI with SCBE-AETHERMOORE