This research introduces a novel governmental-inspired framework to ensure LLMs operate ethically across diverse cultural contexts while maintaining core principles.

Key innovations:

• Three-branch system: LLMs (executive), DIKE (legislative), and ERIS (judicial) working in concert
• Contextual adaptation: Framework adjusts to cultural differences while upholding universal ethical standards
• Adversarial dynamics: DIKE-ERIS duality creates robust ethical safeguards
• Practical security: Addresses critical concerns around safe AI deployment and harmful outputs

This approach matters for security professionals by offering a structured methodology to implement ethical guardrails in AI systems, potentially reducing regulatory risks and building user trust.

A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models