AI Risk Management Assurance Network (AIRMAN)
Aidan Kierans
Submitted to AI Safety Entrepreneurship Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.
The AI Risk Management Assurance Network (AIRMAN) addresses a critical gap in AI safety: the disconnect between existing AI assurance technologies and standardized safety documentation practices. While the market shows high demand for both quality/conformity tools and observability/monitoring systems, currently used solutions operate in silos, offsetting risks of intellectual property leaks and antitrust action at the expense of risk management robustness and transparency. This fragmentation not only weakens safety practices but also exposes organizations to significant liability risks when operating without clear documentation standards and evidence of reasonable duty of care. Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring. The AIRMAN is unique in three ways: First, it creates a neutral, dedicated platform where competitors can collaborate on safety standards. Second, it provides technical integration layers that enable interoperability between different types of assurance tools. Third, it offers practical implementation support through templates, training programs, and mentorship systems. The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.
Reviews
I think this is a solid well-thought idea. Main reservations: I think it's a bit too abstract, and assumes things like they'll reach a definitive framework by 2027, and that adoption won't be a major blocker.
I really like the project's approach of combining open source with commercialization potential, it's a proven model that works well in tech!
The alignment with existing standards like the EU AI Act and the inclusion of real-world examples like the nuclear industry precedent adds a lot of credibility to the proposal. I particularly appreciate their proactive stance on working with initiatives like CoSAI to ensure complementary approaches rather than competition.
However, I have some concerns about the timeline. The proposed yearly release cycle might be too slow for the fast-paced AI industry...there's a real risk the framework could become outdated before reaching maturity, or competitors might develop similar solutions faster.
While the project shows great promise in terms of governance and regulatory alignment, I would've loved to see more forward-thinking considerations about how it would adapt to future scenarios involving AGI or ASI. That said, the thorough market analysis and industry context demonstrate solid groundwork, and the focus on practical implementation through standardized documentation and verification practices seems well thought out. With some timeline adjustments and more future-proofing considerations, this could be a really impactful project in the AI safety space!
Read full reviewShow less
Cite this project
@misc{kierans2025ai,
title = {{AI Risk Management Assurance Network (AIRMAN)}},
author = {Aidan Kierans},
year = {2025},
month = jan,
note = {Submitted to AI Safety Entrepreneurship Hackathon, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/ai-risk-management-assurance-network-(airman)}},
url = {https://apartresearch.com/sprints/projects/ai-risk-management-assurance-network-(airman)}
}More from AI Safety Entrepreneurship Hackathon
- 1st place by peer reviewView project: AntiMidas: Building Commercially-Viable Agents for Alignment Dataset Generation
AntiMidas: Building Commercially-Viable Agents for Alignment Dataset Generation
the commonwealth
AI alignment lacks high-quality, real-world preference data needed to align agentic superintel- ligent systems. Our technical innovation builds on Pacchiardi et al. (2023)’s breakthrough in detecting AI deception …
- View project: Scoped LLM: Enhancing Adversarial Robustness and Security Through Targeted Model Scoping
Scoped LLM: Enhancing Adversarial Robustness and Security Through Targeted Model Scoping
FocusAI
Even with Reinforcement Learning from Human or AI Feedback (RLHF/RLAIF) to avoid harmful outputs, fine-tuned Large Language Models (LLMs) often present insufficient refusals due to adversarial attacks causing them to …
- View project: Prompt+question Shield
Prompt+question Shield
Seon's team
A protective layer using prompt injections and difficult questions to guard comment sections from AI-driven spam.