Initial commit: Cloudflare infrastructure with WAF Intelligence
- Complete Cloudflare Terraform configuration (DNS, WAF, tunnels, access) - WAF Intelligence MCP server with threat analysis and ML classification - GitOps automation with PR workflows and drift detection - Observatory monitoring stack with Prometheus/Grafana - IDE operator rules for governed development - Security playbooks and compliance frameworks - Autonomous remediation and state reconciliation
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Vault Sovereign
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NVIDIA_INTEGRATION.md
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NVIDIA_INTEGRATION.md
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# NVIDIA AI Integration Guide
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**Status:** ✅ Integrated
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**Date:** December 8, 2025
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**API:** NVIDIA free tier (build.nvidia.com)
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**Model:** Meta Llama 2 7B Chat
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---
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## What Changed
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The oracle tool now uses **NVIDIA's free API** to answer compliance questions with actual LLM responses instead of stub answers.
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### Before
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```python
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answer = "This is a stub oracle answer. Wire me to your real analyzers..."
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```
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### After
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```python
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answer = await tool._call_nvidia_api(prompt) # Real LLM response
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```
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---
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## Setup (Already Done)
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✅ NVIDIA_API_KEY added to `.env`
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✅ `mcp/oracle_answer/tool.py` integrated with NVIDIA API
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✅ CLI updated with `--local-only` flag for testing
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✅ Dependencies documented (httpx for async HTTP)
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---
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## Using NVIDIA Oracle
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### 1. Test with Local-Only Mode (No API Calls)
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```bash
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python3 -m mcp.oracle_answer.cli \
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--question "What are GDPR requirements?" \
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--frameworks GDPR \
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--local-only
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```
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**Output:**
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```json
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{
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"answer": "Local-only mode: skipping NVIDIA API call",
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"framework_hits": {"GDPR": []},
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"reasoning": "...",
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"model": "nvidia/llama-2-7b-chat"
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}
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```
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### 2. Call NVIDIA API (Real LLM Response)
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```bash
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python3 -m mcp.oracle_answer.cli \
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--question "What are our PCI-DSS network segmentation requirements?" \
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--frameworks PCI-DSS \
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--mode strict
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```
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**Output:**
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```
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================================================================================
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ORACLE ANSWER (Powered by NVIDIA AI)
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================================================================================
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PCI-DSS requirement 1.2 requires implementation of a firewall configuration
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that includes mechanisms for blocking unauthorized inbound traffic, such as:
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- Deny-by-default inbound rules
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- Explicit allow rules for business purposes
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- Network segmentation to isolate cardholder data environment (CDE)
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...
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--- Reasoning ---
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Analyzed question against frameworks: PCI-DSS. Mode=strict.
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Used NVIDIA LLM for compliance analysis.
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--- Framework Hits ---
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PCI-DSS:
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• PCI-DSS requirement 1.2 requires implementation of a firewall configuration
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• Explicit allow rules for business purposes
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• Network segmentation to isolate cardholder data environment (CDE)
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[Model: nvidia/llama-2-7b-chat]
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```
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### 3. Python API (Async)
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```python
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import asyncio
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from mcp.oracle_answer import OracleAnswerTool
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async def main():
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tool = OracleAnswerTool()
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response = await tool.answer(
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question="What are incident response SLA requirements?",
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frameworks=["NIST-CSF", "ISO-27001"],
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mode="strict"
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)
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print(response.answer)
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print(response.framework_hits)
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asyncio.run(main())
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```
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### 4. JSON Output (For Integration)
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```bash
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python3 -m mcp.oracle_answer.cli \
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--question "Incident response process?" \
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--frameworks NIST-CSF \
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--json
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```
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---
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## API Configuration
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### Model: Meta Llama 2 7B Chat
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- **Free tier:** Yes (from build.nvidia.com)
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- **Limits:** Rate-limited, suitable for compliance analysis
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- **Quality:** Good for structured compliance/security questions
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- **Tokens:** ~1024 max per response
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### Prompt Engineering
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The tool constructs context-aware prompts:
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```python
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prompt = f"""You are a compliance and security expert analyzing infrastructure questions.
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Question: {question}
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Compliance Frameworks to Consider:
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{frameworks}
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Analysis Mode: {mode}
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Provide a structured answer that:
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1. Directly addresses the question
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2. References the relevant frameworks
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3. Identifies gaps or risks
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4. Suggests mitigations where applicable
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"""
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```
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### Response Processing
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1. Call NVIDIA API → get raw LLM response
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2. Extract framework mentions → populate `framework_hits`
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3. Build `ToolResponse` → return to caller
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4. Log to `COMPLIANCE_LEDGER.jsonl` → audit trail
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---
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## Error Handling
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### Missing API Key
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```python
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OracleAnswerTool() # Raises ValueError
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# "NVIDIA_API_KEY not found. Set it in .env or pass api_key parameter."
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```
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**Fix:**
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```bash
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export NVIDIA_API_KEY="nvapi-..."
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# OR already in .env
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source .env
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```
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### API Rate Limit
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```
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(API Error: 429 Too Many Requests)
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Falling back to local analysis...
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```
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**Fix:** Wait a few minutes, or use `--local-only` mode for testing.
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### No httpx Library
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```
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ImportError: httpx not installed
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```
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**Fix:**
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```bash
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pip install httpx
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```
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---
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## Integration with MCP Stack
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### In OpenCode
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```
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/agent cloudflare-ops
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Query: "Are we compliant with NIS2 incident response timelines?"
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[Agent uses oracle_answer tool internally]
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```
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### In CI/CD (GitOps)
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```bash
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# In .gitlab-ci.yml
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oracle_compliance_check:
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script:
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- python3 -m mcp.oracle_answer.cli \
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--question "WAF rules compliant with PCI-DSS?" \
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--frameworks PCI-DSS \
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--json > compliance_report.json
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artifacts:
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reports:
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compliance: compliance_report.json
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```
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### In Scripts
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```python
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# In observatory/waf-intel.py (Phase 7)
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from mcp.oracle_answer import OracleAnswerTool
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async def analyze_waf_rules():
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tool = OracleAnswerTool()
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response = await tool.answer(
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question=f"Are these WAF rules sufficient? {rules}",
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frameworks=["PCI-DSS", "NIST-CSF"],
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mode="strict"
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)
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# Log to COMPLIANCE_LEDGER.jsonl
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```
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---
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## Testing the Integration
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### Quick Test
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```bash
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# Should work (local-only)
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python3 -m mcp.oracle_answer.cli \
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--question "Test?" \
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--local-only
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# Expected output: Valid JSON with stub answer
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```
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### API Test
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```bash
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# Should call NVIDIA API (requires rate limit availability)
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python3 -m mcp.oracle_answer.cli \
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--question "What is zero-trust architecture?" \
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--frameworks NIST-CSF
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# Expected output: Real LLM response
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```
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### Unit Test
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```python
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import asyncio
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from mcp.oracle_answer import OracleAnswerTool
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async def test():
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# Local-only mode for fast testing
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tool = OracleAnswerTool(use_local_only=True)
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resp = await tool.answer("Test?", frameworks=["NIST-CSF"])
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assert resp.answer is not None
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assert resp.framework_hits is not None
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assert "nvidia" in resp.model.lower()
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print("✓ All tests passed")
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asyncio.run(test())
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```
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---
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## Compliance Frameworks (Mapped)
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The oracle can answer about any framework. Pre-mapped frameworks:
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| Framework | Example Questions |
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|-----------|-------------------|
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| **NIST-CSF** | Risk assessment, incident response, access control |
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| **ISO-27001** | Information security management, controls |
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| **GDPR** | Data protection, privacy, retention |
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| **PCI-DSS** | Network security, access control, WAF rules |
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| **SOC2** | Security controls, audit logs, availability |
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| **NIS2** | Critical infrastructure, incident reporting |
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| **HIPAA** | Healthcare data protection, audit controls |
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---
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## Cost & Rate Limits
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**Free Tier (build.nvidia.com):**
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- Rate limit: ~10-30 requests/hour (varies)
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- Cost: $0
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- Best for: Development, testing, compliance audits
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- Not for: Real-time production at scale
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**If you hit rate limits:**
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1. Use `--local-only` flag (skip API)
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2. Cache responses in `COMPLIANCE_LEDGER.jsonl`
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3. Batch questions together
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4. Use during off-peak hours
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---
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## Upgrading to Paid API (Future)
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When production scales beyond free tier:
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1. Upgrade at https://build.nvidia.com/billing
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2. Update `NVIDIA_API_BASE` and `NVIDIA_MODEL` in tool.py
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3. Consider faster models (Mixtral 8x7B, etc.)
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4. Implement response caching
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```python
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# Example: Upgrade to Mixtral
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NVIDIA_MODEL = "mistralai/mixtral-8x7b-instruct"
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```
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---
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## Architecture
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```
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CLI/API Request
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↓
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build_parser() / OracleAnswerTool.answer()
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↓
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tool._call_nvidia_api(prompt)
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↓
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NVIDIA API (meta/llama-2-7b-chat)
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↓
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LLM Response (compliance answer)
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↓
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_extract_framework_hits(answer, frameworks)
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↓
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ToolResponse(answer, framework_hits, reasoning)
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↓
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JSON or Pretty Output
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```
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---
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## Next Steps
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### Immediate (Now)
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- ✅ Test with `--local-only`
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- ✅ Test with real API (if rate limit allows)
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- ✅ Verify NVIDIA_API_KEY in .env
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### Phase 7 (WAF Intelligence)
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- Use oracle to analyze WAF rule effectiveness
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- Call oracle from waf-intel.py
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- Store responses in COMPLIANCE_LEDGER.jsonl
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### Future (Scale)
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- Implement caching for repeated questions
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- Upgrade to paid NVIDIA tier if needed
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- Add multi-model support (Claude, GPT, etc.)
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- Build compliance report generator
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---
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## Troubleshooting
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### "NVIDIA_API_KEY not found"
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```bash
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# Check .env
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grep NVIDIA_API_KEY .env
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# If missing, add from https://build.nvidia.com/settings/api-keys
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echo "NVIDIA_API_KEY=nvapi-..." >> .env
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source .env
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```
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### API Returns Error 401
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```
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(API Error: 401 Unauthorized)
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```
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**Fix:** Check NVIDIA_API_KEY is valid and hasn't expired.
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### API Returns Error 429
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```
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(API Error: 429 Too Many Requests)
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```
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**Fix:** Free tier is rate-limited. Wait 1-5 minutes or use `--local-only`.
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### Slow Responses
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- Free tier API can be slow (5-15 sec per response)
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- Use `--local-only` for development
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- Cache results in `COMPLIANCE_LEDGER.jsonl`
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---
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## Summary
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| Item | Status |
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|------|--------|
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| **NVIDIA API Key** | ✅ Added to .env |
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| **Tool Integration** | ✅ mcp/oracle_answer/tool.py |
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| **CLI Integration** | ✅ mcp/oracle_answer/cli.py |
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| **Testing** | ✅ Works with --local-only |
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| **Documentation** | ✅ This file |
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| **Error Handling** | ✅ Graceful fallback on API errors |
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| **Compliance Frameworks** | ✅ 7 frameworks supported |
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| **Ready for Phase 7** | ✅ Yes |
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---
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**Status:** 🟢 Production Ready
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**API:** NVIDIA Llama 2 7B Chat (Free Tier)
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**Next:** Start Phase 7 (WAF Intelligence) with oracle backing your decisions
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