AI is transforming compliance by helping businesses navigate complex regulations while providing a competitive edge. By automating data collection and risk management, companies can streamline operations and turn compliance challenges into opportunities for growth.
AI is transforming compliance by helping businesses navigate complex regulations while providing a competitive edge. By automating data collection and risk management, companies can streamline operations and turn compliance challenges into opportunities for growth.
This post explores the ethical considerations surrounding large language models (LLMs) and their implications for responsible AI use. It evaluates various LLM providers based on criteria such as privacy, bias, reliability, and accountability, highlighting the importance of ethical practices in AI development.
Artificial intelligence is rapidly enhancing financial crime, outpacing traditional compliance systems and creating new challenges for the financial industry. To build trust and ensure accountability, explainability must be established as a baseline requirement for any AI system utilized in financial compliance.
Ontario’s Bill 149 mandates that employers with twenty-five or more employees disclose the use of artificial intelligence in job postings starting January 1, 2026. This legislation aims to ensure transparency and fairness in hiring practices as organizations increasingly rely on AI in recruitment processes.
The FDA’s recent rollout of the internal AI tool, Elsa, aims to address the challenges of regulatory document review, but experts warn that creating effective regulated AI is highly complex. Erez Kaminski, CEO of Ketryx, suggests that a neuro-symbolic approach, combining neural networks and rule-based AI, may be essential for managing the intricate demands of regulatory environments.
The Cloud Security Alliance (CSA) highlights that many AI adoption efforts fail because companies attempt to integrate AI into outdated processes that lack the necessary transparency and adaptability. To address this issue, the CSA introduces the Dynamic Process Landscape (DPL) model, which emphasizes structured and compliant workflows for successful AI implementation.
AI guardrails are essential safety mechanisms that ensure artificial intelligence systems behave properly by filtering out harmful content and preventing mistakes. They play a crucial role in maintaining trust and compliance, protecting both users and sensitive data.
LG Uplus has received the international certification for responsible AI management, ISO/IEC 42001, signifying its commitment to transparent and trustworthy AI services. This achievement highlights the company’s proactive approach to AI governance, ensuring ethical standards and risk management in AI technology deployment.
Priya Dharshini Kalyanasundaram leverages her experience in vendor management and compliance to drive AI safety innovations in the workplace, focusing on measurable impacts. Her work includes developing a computer vision system designed to enhance warehouse safety by monitoring worker posture and equipment usage, aiming to reduce incidents significantly.