AI Compliance Engine with execution tracking, policy control and incident analysis (CIOS system) 

Onto-Compliance Engine - github

https://github.com/roman-shaban Building control layers for AI systems | Execution, Compliance, Safety

GitHub - roman-shaban/onto-compliance-engine: AI Compliance Engine with execution tracking, policy control and incident analysis (CIOS system)

AI Compliance Engine with execution tracking, policy control and incident analysis (CIOS system) - roman-shaban/onto-compliance-engine

Onto Compliance Engine (CIOS)

AI-powered execution control system for structured, safe and auditable task processing. The Universal Standard for Autonomous Agent Safety, Semantic Validation, and Sovereign Control.

🎯 Who This Is For

This system is designed for:

• AI startups building autonomous agents

• companies implementing AI workflows with control requirements

• developers working on AI governance and safety layers

• product teams needing structured AI execution

• researchers exploring sovereign machine logic and semantic protocols

⚡ Problem It Solves

Modern AI systems:

• are unpredictable

• lack control

• are not auditable

This engine introduces:

• execution control

• policy enforcement

• traceability

• incident handling

• deterministic legal and semantic boundaries

🚀 What This Is

Onto Compliance Engine is a prototype of a next-generation AI execution layer.

It transforms AI from “text generator” into:

• controlled system

• auditable process

• structured execution engine

• cryptographically verifiable semantic actor

🧠 Core Idea

Every AI action must be:

• defined (intent)

• controlled (policy)

• validated (compliance)

• traceable (execution graph)

• reviewable (human layer)

⚙️ Features

• Task execution engine

• Delegation between agents

• Policy enforcement layer

• Risk scoring system (Risk, Trust, and Proof scores)

• Incident detection & reporting

• Full execution trace

• Replay & recovery mechanisms

• Semantic verification against Onto-Protocol standards

🏗 System Components

• runner.py — execution orchestrator

• compliance_engine.py — validation core

• policy.py — rules & restrictions

• incident_engine.py — failure analysis

• execution_graph.py — trace structure

• scoring_engine.py — risk & trust scores

• onto_models.py — semantic definitions (OntoTask, Intent, Permissions)

📊 Example Flow

1. Task is created (intent)

2. Policy is applied

3. Execution is delegated

4. Results are validated

5. Trace is recorded

6. Incidents (if any) are logged

🎯 Goal

To build a foundation for:

• AI compliance systems

• enterprise AI control layers

• autonomous agent governance

• a monetizable "Logic Economy" via standardized semantic validation

📌 Status

Working prototype (local execution). Currently preparing for a formal architecture transition.

🔮 Next Steps

• API layer

• UI dashboard

• integration with external systems

• enterprise-ready version

• Formalization of the Onto-Protocol via Zenodo White Paper

• Migration of core logic to Rust for enterprise-grade memory safety

🧩 System Architecture

Core modules:

• execution_graph.py — task flow tracking

• policy.py — control rules

• approval.py — human-in-the-loop layer

• incident_engine.py — failure detection

• state_machine.py — execution lifecycle

👤 Author

Roman Shaban AI Systems Architect (in progress) / Architect of Sovereign Intelligence Systems

⚖️ Intellectual Property & License

This project is a sovereign intellectual asset. All architectural designs, including the Onto-Protocol and CIOS Framework, are the property of the author.

Licensed under the Apache License 2.0. See the LICENSE file for details.

🧪 How to Run

python runner.py

Apache License

                           Version 2.0, January 2004

                        http://www.apache.org/licenses/

   Copyright 2026 Roman Shaban

   Licensed under the Apache License, Version 2.0 (the "License");

   you may not use this file except in compliance with the License.

   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software

   distributed under the License is distributed on an "AS IS" BASIS,

   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

   See the License for the specific language governing permissions and

   limitations under the License.

The Onto-Protocol and CIOS Framework: A Deterministic Compliance Standard for Sovereign AI Agents

As autonomous artificial intelligence (AI) agents increasingly interact across decentralized networks, traditional communication protocols (e.g., HTTP, MCP) fail to provide intrinsic execution control, semantic validation, and liability boundaries. This paper introduces the Catalyst Intelligence Operating System (CIOS) and its core communication layer, the Onto-Protocol. Unlike transport-focused standards, the Onto-Protocol is a semantic contract framework designed to define agent intent, strictly enforce execution boundaries, and generate cryptographically verifiable audit trails. Furthermore, we present the CIOS Probabilistic Trust Model—a dynamic scoring engine that calculates operational risk and agent trust in real-time. By separating semantic meaning from execution and infrastructure, this framework establishes a foundational layer for sovereign, legally compliant, and monetizable machine logic.

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