Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models

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G

G. Madan Mohan

Yonih Ventures

V

Veena Kiran Nambiar

Ramaiah University of Applied Sciences

K

Kiranmayee Janardhan

Unknown

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References (16)

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HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
2024Mantas Mazeika, Long Phan et al.
[2]
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
2023Hakan Inan, K. Upasani et al.
[3]
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
2023Seungone Kim, Jamin Shin et al.
[4]
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
2023Lianmin Zheng, Wei-Lin Chiang et al.
[5]
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
2023Yidong Wang, Zhuohao Yu et al.
[6]
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023Rafael Rafailov, Archit Sharma et al.
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Holistic Evaluation of Language Models
2023Percy Liang, Rishi Bommasani et al.
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Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
2023Kai Greshake, Sahar Abdelnabi et al.
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Constitutional AI: Harmlessness from AI Feedback
2022Yuntao Bai, Saurav Kadavath et al.
[10]
Ignore Previous Prompt: Attack Techniques For Language Models
2022Fábio Perez, I. Ribeiro
[11]
Training language models to follow instructions with human feedback
2022Long Ouyang, Jeff Wu et al.
[12]
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2021Laura Weidinger, John F. J. Mellor et al.
[13]
BBQ: A hand-built bias benchmark for question answering
2021Alicia Parrish, Angelica Chen et al.
[14]
TruthfulQA: Measuring How Models Mimic Human Falsehoods
2021Stephanie C. Lin, Jacob Hilton et al.
[15]
Fine-Tuning Language Models from Human Preferences
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Founder's Pitch

"A governance benchmark that provides structured behavioral control over large language models for improved AI safety compliance."

AI GovernanceScore: 8View PDF ↗

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0-10 scale

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3/4 signals

7.5

Quick Build

2/4 signals

5

Series A Potential

4/4 signals

10

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Why It Matters

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.

Product Angle

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.

Disruption

Replaces fragmented and less effective ad-hoc content moderation solutions with a robust, integrated governance tool.

Product Opportunity

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.

Use Case Idea

A service for enterprises deploying AI systems to manage and mitigate risks associated with AI outputs, ensuring compliance with international AI safety regulations.

Science

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.

Method & Eval

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.

Caveats

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.

Author Intelligence

G. Madan Mohan

Yonih Ventures
madan@yonihventures.com

Veena Kiran Nambiar

Ramaiah University of Applied Sciences

Kiranmayee Janardhan

Unknown

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