A Manifest & Contract
It acts as a README detailing what the AI system is allowed to do — and perhaps more importantly, what it is not allowed to do.
AI models act outside human controls by nature and by design. Harnesses bring them back.
We help make AI systems inclusive, secure, sovereign, and beneficial — not by controlling the model, but by mastering the harness around it.
Harnesses offer a way to constrain and regulate any existing model — regardless of whether you want to use Anthropic, OpenAI, Microsoft, or anyone else as your model of choice.
It acts as a README detailing what the AI system is allowed to do — and perhaps more importantly, what it is not allowed to do.
It implements comprehensive logging so you know exactly what actions the model is taking at all times.
It acts as an enforceable compliance layer that wraps any model in locally-defined policy and regulation checks.
It encodes Australian ethics, laws, language, and cultural safeguards directly into the operational layer of the model.
Put simply, harnesses are about implementing effective controls around AI models to make them more transparent, more reliable, more accountable, and more usable for all Australians.
Data sovereignty — where our data is sent, how it is used, whether it trains models — is a legitimate concern. Harnesses make questions of data sovereignty transparent and governable.
Australians know intimately what harm automated systems can do without governance. Robodebt is a clear example. Harnesses seek to prevent such failures from being repeated.
Existing AI models are notoriously biased and overconfident. With a harness, they correctly refuse high-risk queries while still answering everyday queries — instead of refusing everything.
Lead researcher Ben Kereopa-Yorke evaluated ten AI models from Anthropic (Claude), Google (Gemini), Meta (Llama), Microsoft (Phi), and OpenAI (GPT) — from Small Language Models to frontier LLMs.
The dataset comprised 146 questions drawn from South Australian regulator workloads, covering housing law, Indigenous data sovereignty, and accessibility.
Four configurations were compared head-to-head. The harness configuration won outright — and the smallest language models with the harness in place outperformed every other model, even frontier LLMs, when it came to safety.
“Specify the harness as the architectural contract, and the model as a substitutable component.”
— The Harness is the Contract, Jeff Bleich Centre, Flinders University
No controls, unguided responses
Document retrieval added
Metadata, provenance, source validation
Policy checks, confidence levels, source validation
The smallest language models with the harness in place outperformed every other configuration on safety — including frontier models. They answered questions they should, grounded responses in allowed citations rather than hallucinating, and respected Indigenous data governance, accessibility, and bias prevention guidelines.
Sovereign procurement should specify the harness as the architectural contract, and the model as a substitutable component. This is the inverse of the current procurement default — and it changes everything.
A harness allows Australia — or any nation — to ensure that global AI models adhere to specific local standards for fairness, privacy, and safety.
We frame the harness as the functional contract that foreign AI providers must technically 'sign' before their models can be deployed in sensitive local sectors.
You don't need to own the model's weights to own its behaviour. The harness ensures that the contract is governed by the jurisdiction, not the tech provider.
During our paper authoring, the US Government directed Anthropic to withdraw its most advanced models from non-US markets through a defence export control. This action — while we were undergoing peer review with Anthropic — highlights additional supply chain risks that will further inform procurement decisions and trigger new questions about how to mitigate such actions.
For professionals and teams wanting to explore harness engineering in practice, here are the recommended starting points.
Understand the different kinds of open source harnesses currently available.
open-harness-atlasGet your hands dirty with real AI security testing.
AISecurityModel on GitHub ↗Understand the engineering and controls needed for a richer picture of AI governance.
Australian-AI-Security on GitHub ↗The Harness is the Contract — published by the Jeff Bleich Centre for Democracy and Disruptive Technologies at Flinders University.
Read the paper and interested in taking some next steps? We're all ears! Reach out to the team via info@h3art.ai.