A Dual-Layer Architecture for Enterprise AI

Transcript

1. ARIA. A Dual-Layer Architecture for Enterprise AI Learning from Failure. Containing Every Attack. AEZISAI, Inc.
2. THE PROBLEM // 01 LLMs Repeat the Same Mistakes ▪ LLMs are stateless ̶ every session starts from zero • Project-specific knowledge, team know-how, and past lessons are never carried over ▲ A foundation that forgets everything cannot deliver enterprise-grade reliability ↻ 02
3. THE PROBLEM // 03 The Attack Surface Keeps Expanding ▪ AI agents now make decisions, connect to services, and execute autonomously ▲ New attack vectors are emerging that never existed in traditional applications ✖ Conventional perimeter-based defense is not enough 🤖 🗄 ☁ 🌐 ⚡ ⚡ ⚡ 03
4. OUR APPROACH // 04 Two Structural Answers to Two Structural Problems These two layers work as a complementary dual-layer structure ❖ LAYER 1: SYNAPTIC INHERITANCE FLOW Accumulates experience across sessions and compensates for inherent LLM weaknesses through retained context. ◻ LAYER 2: EPHEMERAL SANDBOX Disposable infrastructure that operates on inherited intelligence, minimizing blast radius and leaving no foothold. SYNAPTIC INHERITANCE FLOW ◉ ◆ ☰ EPHEMERAL SANDBOX ⬚ ◨ ☒ ▲ EXPERIENCE ▼ INTELLIGENCE 04
5. LAYER 1 // 05 Accumulating Experience to Compensate for LLM Weaknesses ❖ Inspired by synaptic plasticity in neuroscience, replicating how biological systems learn. ⇈ Success patterns are reinforced dynamically across operations (long-term potentiation). ⛨ Failure patterns automatically become strict avoidance rules (long-term depression). ⎇ Project-specific context and domain knowledge is inherited consistently across sessions. 05
6. AEM // 06 Not Replacing LLMs ̶ Complementing Them ▪ LLMs handle general-purpose reasoning ▲ AEM handles organization-specific judgment, failure avoidance, and business rule consistency ↗ The more you use it, the more accurate it gets ̶ and the less it costs ↘ Inverted cost structure: accuracy up, cost down LLM AEM ＋ ACCURACY COST START GROWTH MATURITY TIME / USAGE → 06
7. LAYER 2 // 07 "Infrastructure Is Disposable; Intelligence Is Inherited." ▪ Execution environment is created per task and destroyed on completion ◆ Flips the security premise: minimize blast radius, leave no foothold ▲ Unknown vulnerabilities are contained securely to the task in progress • No adjacent environments to pivot to ̶ lateral movement doesn't apply EPHEMERAL SANDBOX BOUNDARY ＋ 1. CREATE Task-specific environment initialized ▶ ▶ 2. USE AI executes autonomously within strict bounds ▶ ✖ 3. DESTROY Infrastructure erased, leaving zero footprint 07
8. DUAL-LAYER STRUCTURE // 08 Inherit the Intelligence, Discard the Environment 🧠 Layer 1 accumulates experience across sessions ✖ Layer 2 destroys the environment after every task 🔒 Only encrypted experience deltas survive 🛡 Together they form a complementary security + intelligence model LAYER 1: SYNAPTIC INHERITANCE FLOW 🌐 🧠 🗄 ↑ 🔒 🔒 ENCRYPTED DELTAS INHERITED UPWARD LAYER 2: EPHEMERAL SANDBOX ▪ ⏬ ⏬ DESTROYED 08
9. 1 Requirements ⬢ 2 Design ⬢ 3 Implementation⬢ 4 Test ⬢ 5 Deploy ⬢ QUALITY GATES // 09 25 Checkpoints. Zero Exceptions. 25 CHECKPOINTS Applied across every stage of the development lifecycle, ensuring consistent governance from requirements to deployment. Incomplete code, workarounds, or skipped tests ̶ any one blocks release immediately with zero exceptions. Quality is guaranteed structurally by the platform, regardless of who or what (AI or Human) produced the code. AEZISAI, Inc. | ARIA Architecture 09 / 16
10. USE CASE: FINANCE // 10 Autonomous Real-Time Audit Agent 🛡 ⏱ 24/7 transaction auditing with autonomous anomaly detection 📈 Fraud pattern experience accumulates ̶ accuracy improves over time 📦 Processing runs in an isolated environment ̶ breach impact is contained 🔒 High-risk operations gated by multi-factor authentication 10
11. USE CASE: LEGAL // 11 Autonomous Contract Review & Negotiation Agent ⚖ ✎ Contract analysis and automated corrections within policy parameters ⇄ Negotiation patterns inherited ̶ reviews align with organizational standards 🛡 Confidential documents processed in isolation, destroyed on completion 🔒 Information leakage eliminated at a structural level 11
12. USE CASE: MANUFACTURING // 12 Autonomous Plant Control Agent 🏭 🔌 Operates in air-gapped (network-disconnected) environments 🧠 Veteran engineers' tacit knowledge structuralized and inherited 🛡 Control system attack paths blocked by isolation structure 🔒 Emergency operations require multi-layer authentication 12
13. USE CASE: HEALTHCARE // 13 Confidential Data Analysis Agent 🗄 📄 Raw patient data accessed for statistical analysis 🛡 Only anonymized insights extracted 🗑 Analysis completes in isolation ̶ environment erased with data 🧩 Resolves the "need raw data but can't expose it" dilemma structurally 13
14. COMMON PATTERN // 14 Three Pillars of Enterprise AI Autonomy 🧠 Experience Accumulation Accuracy improves the more it's used, reinforcing success patterns autonomously. 📦 Disposable Environments Confidentiality and containment guaranteed structurally through isolated tasks. 🔒 Authority Gates Clear ceilings on AI autonomy, preventing dangerous unverified operations. Autonomy and containment ̶ achieved simultaneously. 14
15. MULTI-PLATFORM & PHYSICAL AI // 15 Web. Desktop. Mobile. And Beyond. ❖ ARIA CORE 🖥 DESKTOP 📱 MOBILE 🤖 PHYSICAL AI 🔗 Shared core engine across all platforms ↻ AEM and Synaptic Inheritance operate identically on every device ⌨ Desktop: AI-integrated development environment 📲 Mobile: workflow continuity on the go ⚙ Vision: extending into Physical AI ̶ industrial robotics, drone swarms, smart offices 15
16. SUMMARY & VISION // 16 Solving Complex Enterprise Infrastructure ̶ AI ALL IN 🧠 AEM Experience accumulation compensates for LLM weaknesses 📦 Ephemeral Sandbox Disposable environments contain attacks structurally 🛡 Zero Tolerance 25 quality gates eliminate incomplete deliverables 🔄 Ecocycle Full SDLC automation from requirements to deployment The future is within reach. AEZISAI, INC. | AEZISAI.CO.JP 16