Open Arsenal

Risk-aware decisions by default.

Human Intent Governance Standard (HIGS) is a structured governance framework designed to ensure that AI systems operate under explicit human intent, enforceable policy constraints, and auditable decision processes. It is built around the principle that AI should not act autonomously in high-impact contexts, but instead function as a guided system that clarifies intent, evaluates constraints, and presents structured options for human approval. The result is a decision environment where accountability remains clearly human, while AI handles analysis, verification, and orchestration.

At its core, HIGS introduces an Intent Engine that continuously refines user intent through guided questioning, ambiguity detection, and iterative clarification. This intent is then passed through a set of parallel governance layers, including legal compliance checks, organizational policy validation, risk assessment, and AI safety evaluation. Each layer operates independently but contributes to a unified governance outcome, ensuring that no single dimension of oversight is overlooked. The system is designed to surface missing information early, reduce uncertainty, and prevent execution based on incomplete or contradictory inputs.

A key feature of the framework is its Human-in-the-Loop governance model, which enforces explicit approval checkpoints before any action is executed. These checkpoints can be configured for single or multi-party approval, with escalation paths for higher-risk decisions. The system maintains full decision traceability through an immutable audit trail that records intent evolution, system recommendations, rule evaluations, and final approvals. This creates a complete historical record of how and why any decision was made.

HIGS also emphasizes explainability and risk awareness as first-class system outputs. Every recommendation includes structured reasoning, confidence scoring, applicable rule citations, and explicit risk analysis across legal, financial, operational, ethical, and reputational dimensions. Instead of producing opaque outputs, the system is designed to present transparent trade-offs and alternatives, allowing humans to make informed final decisions.

Finally, the architecture is modular and extensible, allowing organizations to integrate domain-specific compliance modules, jurisdictional rule sets, and custom policy engines. It supports self-hosted deployments, strong security controls, and privacy-by-design principles, making it suitable for enterprise, regulated industries, and open-source governance ecosystems. The overall system ensures that AI remains a structured decision-support layer rather than an uncontrolled decision-maker, aligning automation with accountability at every step.

This specification is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+) and may be used freely with required attribution under Section 7. A Specification Branding License is available for attribution-free deployments, with fees based on usage, scope, and deployment size.