Governed Intelligence Architecture Specification (GIAS)

Boulder, CO

An open architecture for deterministic AI governance that separates policy enforcement from model behavior, enabling trustworthy deployment across providers.

Project Details

AI is becoming increasingly capable, but the mechanisms for governing its behavior remain fragmented, model-specific and largly opaque. As organizations adopt AI across workflows, they need infrastructure that makes AI behavior predictable, auditable, and accountable - without depending upon the behavior of any single model.

This projects develops the Governed Intelligence Architecture Specification (GIAS), and open architectural framework for deterministic AI governance. Rather than attempting to change how foundational models reason internally, GIAS defines how identity, policy enforcement, execution, observation, persistence, and forensic replay should operate outside the model, allowing governance to remain consistent regardless of which AI provider is used.

Funding would support continued development of the reference architecture, implementation guidance, and the working reference implementations that demonstrate the spec in practice. This includes the continues evolution of Palladium, a governed execution platform, and Origina, which demonstrates governed identity and memory operating above multiple foundational model -- independent of the provider.

The primary work will be led my me, building on several years of applied research and implementation. The concrete outputs include a publically available architectural spec, reference implementation patterns, technical documents, and open demonstrations showing how deterministic governance can reduce operational AI risk.

Theory of Impact

As AI becomes more capable, the primary challenge is not just model behavior. It is ensuring that AI can be deployed safely, consistently, and can be held accountable in real world environments.

Today, governance is often tightly coupled to individual model providers or implemented as application-specific logic. That makes it difficult to maintain consistent oversight as organizations adopt new models and increasingly autonomous workflows.

GIAS reduces this operational risk by defining an architectural separation between AI reasoning and AI governance. Governance of models cannot happen inside the model. GIAS places deterministic policy enforcement, identity, execution, auditability, and forensic replay outside the model. This allows organizations to apply consistent governance regardless of which foundational model is used.

GIAS governs AI systems without requiring changes to the models themselves.

GIAS enabled organizations to retain meaningful human accountability while benefiting from continued advances in AI capability. I believe reducing systemic AI risk requires a trustworthy architecture, not just increasingly capable models.

People

Rob River

Team Member

Grants Received– no grants recorded

Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$50,000

Ideal

$50,000

Theory of impact

As AI becomes more capable, the primary challenge is not just model behavior. It is ensuring that AI can be deployed safely, consistently, and can be held accountable in real world environments.

How the money will be spent

It will be used to accelerate the completion of the Governed Intelligence Architecture Specification (GIAS), expand reference implementation, and improve the technical materials required for adoption and independent evaluation.

The requested funding will directly increase the quality, maturity and accessibility of GIAS as open architectural infrastructure for governed AI deployment.

Discussion

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