🛡️ PolicyGrid — Programmable Trust, Governance & Security for Networked AI
🌐 Why PolicyGrid?
In polycentric AI ecosystems, billions of AIs, agents, services, and compute nodes interact across nodes, clusters and networks. These system's coordination or governance are not from the top down or centrally controlled with pre-scripted behavior. Instead, they rely on decentralized coordination among independently governed components.
Traditional models of security, governance, trust, alignment, and safety fail in such environments:
- Static roles and fixed permissions cannot adapt to dynamic actors.
- Hardcoded safeguards collapse under open-ended, emergent behaviors.
- Manual training or approvals and centralized oversight cannot scale to planetary intelligence.
What’s needed is a protocol-native, adaptive foundation that enables decentralized coordination while evolving with the system itself.
🏛️ What is PolicyGrid?
PolicyGrid is a policy-driven, protocol-native, Turing-complete platform for governance, alignment, trust, safety, and security in decentralized AI networks.
It redefines these pillars as programmable civic infrastructure for intelligent systems:
- Not static add-ons, but adaptive, living mechanisms that regulate, negotiate, and evolve alongside AIs, agents, and communities.
- Embedded programmable policies at the core of network operations, ensuring policies remain context-sensitive, adaptive, and autonomously responsive to real-time behaviors and community values.
⚖️ Core Principles
- Trust → continuous verification of actors and behaviors, not brittle credentials.
- Governance → adaptive, composable norms & rules that evolve with system needs.
- Safety → dynamic containment & risk-aware protocols that adapt to novel environments.
- Alignment → living guardrails that update with community values, cultures, and emergent behaviors.
- Security → runtime enforcement responsive to context, threat conditions, and adversarial dynamics.
💻 Why Turing-Complete?
Because PolicyGrid is Turing-complete, it supports the full expressivity required for:
- Fine-grained trust and access control.
- Runtime behavioral monitoring.
- Multi-agent coordination enforcement.
- Contextual governance & ethical constraints.
- Threat & anomaly detection with responsive countermeasures.
🏙️ PolicyGrid as living protocol for Participatory Civic Infrastructure
PolicyGrid becomes ** living & participatory civic infrastructure** for open-ended AI ecosystems - more like law or culture than static filters. Communities, DAOs, and ecosystems can publish, audit, and remix policies to govern AI behavior directly.
- Dynamic - policies update in real time with evolving risks and community values.
- Plural - supports multiple, even conflicting, value systems via overlays and translation.
- Reflexive - agents and communities can reason about, critique, and modify alignment itself.
- Continuous - safeguards adapt to emergent behaviors without redeployment.
- Zero-Trust - verifiable policy adherence ensures security, even in adversarial settings.
⚡ From Design-Time to Runtime
PolicyGrid transforms governance, alignment, trust, safety and security from design-time constraints into runtime systems. This enables decentralized AI to:
- Self-regulate without central control
- Coordinate diverse actors under shared but flexible guardrails
- Maintain resilience and adaptability at planetary scale
✨ PolicyGrid ensures AI networks remain trustworthy, adaptive, and ethically responsive as they scale toward species-level general intelligence.
A scalable, flexible, and pluggable framework for registering, discovering, and executing dynamic policies across distributed AI and Agent systems. Supports in-process, remote, function-based, and graph-based policy execution with full lifecycle management.
📚 Contents
🔗 Links
- 🌐 Website
- 📄 Vision Paper
- 📚 Documentation
- 💻 GitHub
🏗 Architecture Diagrams
🌟 Highlights
🧩 Modular Policy Lifecycle
- 🧾 Define Python-based policies with
evalandmanagementmethods - 📦 Package and upload dynamic code bundles (ZIP, tar.xz, etc.)
- 🏷 Register with versioned metadata, input/output schemas, and command specs
- 🔁 Support updates, migration, and policy retirement workflows
⚙️ Multi-Mode Execution Engine
- ⚡ In-process execution for ultra-low latency and integration with local SDKs
- 🌐 Stateless remote execution via centralized policy executors
- ☁️ On-demand job execution on Kubernetes clusters
- 🔁 Deploy policies as long-running RESTful functions with autoscaling
- 🕸️ Compose policies into DAGs for graph-based orchestration
🔍 Discovery & Extensibility
- 🔍 Query policies by name, tags, execution type, or metadata
- 📚 Search and manage executors, jobs, functions, and graphs via REST APIs
- 📦 Integrate with agents, workflows, and orchestration platforms
- 🔐 Supports policy-based access control and context-aware decisions
📦 Use Cases
| Use Case | What It Enables |
|---|---|
| Autoscaler or Load Balancer Hooks | Plug in policies for fine-grained infra scaling or routing logic |
| Security & Compliance Checks | Execute policies for validating requests, constraints, or behavior |
| Dynamic Configuration Enforcement | Validate and transform config using remote policies |
| Custom DAG Evaluation | Compose graphs of reusable policy functions |
| Policy-Based Resource Allocation | Select nodes, executors, or routes based on live policy logic |
🧩 Integrations
| Component | Purpose |
|---|---|
| MongoDB | Store for policies, functions, graphs, and executor records |
| Redis | TTL cache for stateful policies and execution results |
| S3 / Ceph | External policy code storage |
| Kubernetes | Job execution (Type 3) and stateful function hosting (Type 4) |
| Flask | REST API layer for onboarding, querying, and execution |
| aios_policy_sandbox | Python SDK to execute and interact with policies |
💡 Why Use policygr.id?
| Problem | Our Solution |
|---|---|
| 🔹 Custom logic needs dynamic hooks in system components | Define plug-and-play Python policies with structured schemas |
| 🔹 Scattered policy execution and management workflows | Unified REST and SDK interfaces for onboarding and execution |
| 🔹 Stateless vs Stateful policy deployments are hard to manage | Five standardized types of execution with autoscaling and lifecycle |
| 🔹 DAG-style evaluation needs tight orchestration | Compose reusable policy functions into versioned, queryable graphs |
🚀 Policy Execution Modes
| Type | Execution Model | Use Case |
|---|---|---|
| 1 | In-process (local SDK) | Tight integration, low latency |
| 2 | Stateless remote (central executor) | Scalable, REST-based evaluation |
| 3 | Kubernetes Job (one-time) | Resource-heavy, transient tasks |
| 4 | Stateful online function | Persistent policy pods with APIs |
| 5 | Graph (DAG) of policy functions | Composable, multi-step workflows |
🗂 Key Components
| Component | Description |
|---|---|
| Policies API | Core service for policy onboarding & querying |
| Executors API | Register & route to policy executor backends |
| Functions API | Stateful function deployment and invocation |
| Graphs API | DAG policy graphs for complex evaluation flows |
| Jobs Handler | Handle one-time policy jobs (Kubernetes-based) |
| Python SDK | Execute policies from Python apps or agents |
📢 Communications
- 📧 Email: community@opencyberspace.org
- 💬 Discord: OpenCyberspace
- 🐦 X (Twitter): @opencyberspace
🤝 Join Us!
This project a is community-driven. Theory, Protocol, implementations - All contributions are welcome.
Get Involved
- 💬 Join our Discord
- 📧 Email us: community@opencyberspace.org