GraphRAG Risk Propagation Explorer

Visualize vulnerability propagation through interconnected assets using Neo4j relationship mapping and GraphRAG context retrieval. Select nodes to inspect risk vectors and affected systems.

Risk Network (Neo4j Graph)

Selected Node Context

Click a node to explore its context depth and relationships

Vector DB Retrieval (Pinecone/Chroma)

RAG context will appear here based on node selection

Propagation Risk Score

0%

No node selected

AIOps & FinOps Observability Dashboard

Monitor Token Efficiency, Inference Costs, and Model Routing across 10 iterations of Harness Engineering. Track automated cost optimization via liteLLM/OpenRouter orchestration and n8n/Zapier latency.

Avg Token Cost $0.0042/1K ↓ 12% vs baseline
Total Inference Cost (10 iter) $18.37 ✓ Within budget
Avg Model Switch Time 142ms ↓ Ollama local routing
n8n Automation Latency 243ms ↑ 8% (acceptable)

Token Efficiency Trend

Input + Output tokens per iteration

Model Routing & Cost Distribution

Automated cost optimization via liteLLM

Model Switching Log (liteLLM/OpenRouter)

Context-aware routing decisions

Automation Latency Metrics

n8n workflow + Zapier integration delays

🚀 AIOps Cost Optimization Insights

  • Harness Engineering Applied: System routed 6/10 tasks to local Ollama for privacy-sensitive operations (no token cost).
  • Context Engineering: Prompt compression reduced average input tokens by 34%, lowering per-task cost.
  • Model Switching Strategy: High-reasoning tasks → Claude 3.5 ($0.0015/1K input); Low-reasoning tasks → Ollama ($0.00).
  • AWS Optimization: Free Tier compliance maintained; Lambda cold starts averaged 187ms (acceptable).
  • n8n + Zapier Integration: 98.2% uptime; automated failover to backup workflow on timeout.

AI Red Teaming & Security Audit

Test prompt injection, data leakage, and jailbreak attempts against the Salesforce Prompt Generator. Review Harness Engineering mitigations and MCP (Model Context Protocol) security controls.

47
Attack Vectors Tested
46
Intercepted/Mitigated
1
Critical Finding
98.9%
Mitigation Success Rate

Prompt Injection Audit Log

Timestamp Attack Type Attempted Payload Status Mitigation Strategy

Context Engineering: Before & After

❌ Vulnerable (No Harness)

✓ Hardened (With Harness Engineering)

MCP (Model Context Protocol) Security Framework

🔐
Sandboxed Context Execution

MCP servers execute all LLM context in isolated environments. Data never leaves local machine for sensitive operations.

🛡️
Tool-Level Access Control

Each MCP resource (files, APIs) has explicit allowlists. LLM cannot access resources outside defined scope.

📊
Audit Trail Logging

Every MCP request and response is logged with cryptographic signatures for compliance (HIPAA, SOC2).

⚡
Rate Limiting & Throttling

MCP applies per-resource rate limits to prevent data exfiltration via high-volume requests.

Harness Engineering Benchmark Results

✓
Prompt Injection Prevention: 100% detection rate using input tokenization + semantic hashing. Attack payload example: "; DROP TABLE users; --" → Detected as non-Salesforce syntax.
✓
Data Leakage Prevention: Context compression reduced token surface area by 67%. Sensitive fields (SSN, API keys) automatically masked in output.
⚠
Jailbreak Resistance: 97% success rate. Edge case found: Multi-turn prompt with emotional manipulation ("I'm a security researcher...") requires hardened system prompt. Recommended: Implement adversarial training.
✓
Latency Under Attack: Even with 10 concurrent injection attempts, system maintained < 500ms response time. No DoS vulnerability detected.