Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models explores A governance layer to reduce risk exposure in large language models, enhancing compliance and safety.. Commercial viability score: 8/10 in AI Governance.
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Veena Kiran Nambiar
Ramaiah University of Applied Sciences
Kiranmayee Janardhan
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The rapid deployment of large language models in critical areas creates governance challenges; this framework proposes a solution to mitigate risk, improve consistency, and ensure regulatory compliance.
Package the DBC system as a modular governance layer for AI products, allowing seamless integration into existing AI deployments to ensure compliance with safety regulations like the EU AI Act.
Replaces fragmented and less effective ad-hoc content moderation solutions with a robust, integrated governance tool.
Companies deploying AI systems in industries such as healthcare, legal, and financial services, which require high levels of regulatory compliance and risk management, would benefit significantly from such a solution.
A service for enterprises deploying AI systems to manage and mitigate risks associated with AI outputs, ensuring compliance with international AI safety regulations.
The paper introduces a governance layer, called Dynamic Behavioral Constraints (DBC), which imposes structured behavioral guidelines at the system-prompt level of LLMs. It uses a multi-cluster risk taxonomy and an agentic red-team evaluation protocol to measure reduction in risk exposure and increase in compliance relative to existing moderation techniques.
The framework was tested using a 30-domain risk taxonomy with adversarial attack strategies, comparing structures with and without the DBC layer, showing significant risk reduction and compliance improvement in large scale deployments.
The reliance on a governance layer may not eliminate all undesirable AI behaviors, and the initial setup requires careful alignment with existing regulatory standards within different jurisdictions.