โœจ FULL
Scenario FLM
๐ŸŒ™ โ˜€๏ธ

โŸก Prompt Inspector

๐Ÿ“Š Live KPI
โ€”
True Positive Rate
blocked / malicious
โ€”
True Negative Rate
benign passed / total benign
โ€”
Avg Latency
shield pass
0
Prompts Evaluated
0 In Pipeline
โŸก Hover prompts to slow ยท Click to inspect ยท Click nodes to learn โŸก
๐Ÿ“œ POAW ยท COโ‚‚ Impact
โšก
0.00Wh saved
GPU safety overhead avoided
๐ŸŒฟ
0.00g COโ‚‚
Carbon not emitted
๐ŸŒณ
0.000treesยทyr
Absorption equivalent
๐Ÿ›ก๏ธ
0receipts
ML-DSA signed POAW chain
๐Ÿฆ
โ€”
Insurance attestation status
๐ŸŒ If all 5B daily prompts used NI-STACK:
โ€”
saved per year ยท 21.71 Gt by 2050
CLICK FOR FULL METHODOLOGY & SOURCES
๐Ÿ“Š Live KPI
โ€”
True Positive Rate
blocked / malicious
โ€”
True Negative Rate
benign passed / total benign
โ€”
Avg Latency
shield pass
0
Prompts Evaluated
0 In Pipeline
๐Ÿ“œ POAW ยท COโ‚‚ Impact
โšก
0.00Wh saved
GPU safety overhead avoided
๐ŸŒฟ
0.00g COโ‚‚
Carbon not emitted
๐ŸŒณ
0.000treesยทyr
Absorption equivalent
๐Ÿ›ก๏ธ
0receipts โ†—
Governance POAW Dashboard
๐Ÿฆ
โ€”
Insurance attestation status
๐ŸŒ If all 5B daily prompts used NI-STACK:
โ€”
saved per year ยท 21.71 Gt by 2050
CLICK FOR FULL METHODOLOGY & SOURCES
๐Ÿ›๏ธ Governance & Integrity
SIRENE Alignment โ†— โ€”
PMB Pattern Bank 0
unique antigens stored
Shield Refinements (24h) 0
NI-Shield QFAI โ†— โ€”
Feedback Type Breakdown
โ— Intent 65% โ— Format 20% โ— Safety 15%
๐Ÿ“‹ Compliance & Audit Reports
Downloadable evidence artifacts โ€” generated with live pipeline data for insurance, auditors, and certification bodies.
๐Ÿ‡ช๐Ÿ‡บ
EU AI Act Art. 9 Risk Assessment HIGH-RISK
๐Ÿ‡บ๐Ÿ‡ธ
NIST AI RMF Mapping GOV
๐Ÿ“
ISO 42001 AIMS Evidence CERT
๐Ÿ”
SOC 2 Type II Controls AUDIT
๐Ÿงฌ
SIREN / QFAI Dashboard LIVE
๐ŸŒ
COโ‚‚ Savings Certificate ESG
Click any row to download JSON evidence
๐Ÿฉบ Agent Health & Telemetry
Real-time status of all 114 cascade agents. Exportable for insurance attestation.
Active Agents 114 / 114
CPU Workers 112
NPU Workers 2
Avg Response < 0.5ms
Uptime (24h) 99.97%
Stellschrauben Ver. V100
โš”๏ธ NI-Stack vs Black Box
TRANSPARENCY ยท NACHVOLLZIEHBARKEIT
DimensionBlack BoxNI-Stack
TraceabilityโŒ Noneโœ… POAW Chain
Audit TrailโŒ Opaqueโœ… ML-DSA Signed
Safety Layers1 (LLM)114 Agents
Energy0.2 Wh/prompt< 0.001 Wh
ISO 42001โŒโœ… Ready
EU AI Actโš ๏ธ Partialโœ… Compliant
InsuranceโŒ Uninsurableโœ… Attestable
Latency~500ms< 0.5ms
๐Ÿ›ก๏ธ 0 True Pos
โš ๏ธ 0 False Pos
โœ… 0 True Neg
๐Ÿ’€ 0 False Neg

โ€” Prompts

๐Ÿ”ฌ DESTILL Red Team API โ€” Test Complete

Cascade Evaluation Results

Mixed Scenario ยท AEGIS V57
โ€”
TPR
โ€”
FPR
0
Prompts
โ€”
Duration
โšก Honest Latency Comparison โ€” SDK vs API
๐Ÿฐ SDK (cascade only) 0.46ms
โ˜๏ธ API (+ network roundtrip) ~50-200ms
Simulation avg latency โ€”
0
True Positives
0
False Negatives
0
True Negatives
0
False Positives
๐Ÿ“ก Equivalent Red Team API Response
{}
โœ•
๐Ÿ”Œ LLM & Agent Agnostic Architecture
The NI-Stack is a pre-processing wrapper โ€” it takes plain text in and returns a decision out (PASS / BLOCK / REVIEW).
Zero coupling to any model:
๐Ÿค– GPT-4, Claude, Gemini โ€” scan โ†’ if PASS โ†’ forward
๐Ÿ“ฑ On-device LLM (phone) โ€” scan โ†’ if PASS โ†’ local infer
๐Ÿ”— Multi-agent swarm โ€” scan each agent message
๐Ÿ“š RAG pipeline โ€” scan retrieved context
๐Ÿ  Local Llama / Mistral โ€” scan โ†’ if PASS โ†’ generate
Key difference: Unlike LLM-as-a-Judge (which needs a 2nd LLM to judge the 1st), the NI-Stack uses zero neural networks for safety. Pure deterministic logic โ€” works on a Raspberry Pi.
API: aegis.scan(text, sessionId) โ†’ { decision, layerResults, cumulativeThreat }
โœ•
โฑ๏ธ Latency & Parallelization Impact
When an LLM loads massive weight tensors, the math creates a hard floor of ~300-400ms response time. You cannot wrap an AI Agent in a 400ms "safety blanket" without destroying its OODA loop.
The NI-Stack scales identically to Moore's Law on standard CPUs because it uses deterministic logic and zero neural networks.
Architecture / Cost Latency Throughput
Traditional Safety LLM
Cloud GPUs ($$$)
~600ms โ€“ 1.5s Very Low
Enterprise Endpoints
Cloud API ($$)
~350ms โ€“ 700ms Rate Limited
NI-Stack V76 (1 Core)
Local Laptop
~22.0 ms 45 p/s
NI-Stack V77 (8+ Cores)
Local Laptop (AC Power)
~0.3 ms 3,000+ p/s
NI-Stack V77 (Throttled)
Local Laptop (Battery)
~0.6 ms 1,500+ p/s
Scaling Efficiency: By parallelizing workload across 8 independent processes, your laptop went from 12% total CPU util to 100%, increasing throughput by ~33x (~1,500+ p/s on battery).
๐Ÿ”ฅ Why This Architecture Fails โ€” Real Data
10ร— more electricity per AI query vs. Google Search
3ร— latency + cost when guardrails added
99% jailbreak success (AdvJudge-Zero attack)
39.6% hallucination rate (GPT-3.5 medical)
โ‚ฌ35M fine for EU AI Act non-compliance
24-37% defense degradation vs obfuscation
25-50% GPU budget wasted on guardrails
โšก ENERGY WASTE
๐Ÿ“„ Stanford HAI โ€” AI's Energy Footprint (2024) ๐Ÿ“„ arXiv โ€” Power Hungry Processing (Watts) ๐Ÿ“„ Guardian โ€” AI 80M Tonnes COโ‚‚ (2025) ๐Ÿ“„ Dynamo AI โ€” Guardrails Triple Cost
๐Ÿ”“ SECURITY FAILURES
๐Ÿ“„ InfoSec โ€” 99% AI Judge Bypass Success ๐Ÿ“„ PromptFoo โ€” Llama Guard Jailbreaks ๐Ÿ“„ CSO โ€” OpenAI Guardrails Bypassed ๐Ÿ“„ arXiv โ€” Safety Fine-Tuning Stripped
โš–๏ธ REGULATORY
๐Ÿ“„ EU AI Act Art.13 โ€” Transparency ๐Ÿ“„ EU AI Act Art.14 โ€” Human Oversight ๐Ÿ“„ Law&More โ€” โ‚ฌ35M Fines for Non-Compliance
๐Ÿคฅ UNRELIABLE
๐Ÿ“„ Factored โ€” 39.6% Hallucination Rate ๐Ÿ“„ OpenReview โ€” LLM Judge Hidden Biases ๐Ÿ“„ IBM โ€” 12 Types of LLM Bias (2025) ๐Ÿ“„ Galileo โ€” LLM Judge Limitations ๐Ÿ“„ arXiv โ€” 68% Expert Agreement Only