Practical models for governed systems

Architecture,
accountability
and evidence.

Start with the problem you need to solve: designing governed AI, separating architecture from tools, assigning blockchain responsibility, or turning policy into controls that produce evidence.

Governed AI

Harness Architecture v1.1.0: Least Sufficient Governance

Trajectory control, obligation-first H0–H3 depth, relevance-scoped tools and independently verified deployed effects.

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Governed AI

Skills Replace Prompts

Move recurring AI work from improvised instructions to versioned capability contracts with explicit tools, authority, evidence and lifecycle.

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Governed AI

Harness Architecture v1.0.0: What Changed

A weekly architecture release covering decision authority, governed memory, runtime trust, evidence, qualification and continuous assurance.

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Blockchain governance

Decentralization Does Not Remove Responsibility

A practical responsibility model for decentralized systems: factual control, activity, decision authority, economic benefit and assurance evidence.

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AI security

AI Guardrails Are More Than an Output Filter

A layered control architecture across governance, identity, retrieval, generation, tools, outputs and operations.

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AI assurance

AI Can Draft the Assessment. It Cannot Own the Assurance Decision.

A practical architecture for AI-assisted GRC: evidence first, governed analysis, independent challenge and explicit assurance authority.

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Governed AI

Toward a Reference Taxonomy for Governed AI Systems

A vendor-neutral map connecting capabilities, architecture, decisions, evidence, risks, controls, responsibilities and lifecycle.

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Governed AI

Frameworks Are Not Architecture

Separate durable capabilities, trust boundaries and governance contracts from replaceable frameworks, protocols, models and vendors.

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Blockchain governance

The Blockchain Responsibility Model

Assign capable, accountable owners across protocols, infrastructure, applications, governance, data, and users.

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GRC architecture

From Policy Hierarchies to Controls

Design a machine-readable control system that produces usable evidence instead of a document graveyard.

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Responsible AI

The Agentic GRC Operating Model

Use specialised agents to collect evidence and route decisions while human owners retain authority.

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Agentic GRC

From Paper Policies to Agent-Driven Controls

Turn machine-readable requirements into governed gates, evidence contracts and accountable escalation.

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