The world is abuzz with advancements in AI technologies like ChatGPT and generative AI (GenAI). And as we see a significant portion of new companies entering the market being centered around AI in one form or another, this introduces a unique set of challenges and opportunities for the world of information security and compliance.

Sam Li, Thoropass’s Co-Founder, and CEO, recently sat down with Vaik Mugunthan, CEO of Dynamo AI, to discuss the key reasons regulation is urgently needed, what exists today, and how businesses can safeguard themselves.

You can watch the full video here:

New risks and challenges in the AI era

The proliferation of GenAI and large language models (LLMs) has created new risks and challenges for regulators, consumers, and industry practitioners alike.

As businesses embark on their AI journeys, it’s crucial to familiarize themselves with the evolving compliance landscape and understand the regulations on the horizon. Knowledge of these regulations is key to protecting both the company and its customers.

AI is advancing across industries at an unprecedented speed. At Dynamo AI, a major focus is tracking these regulatory changes to ensure that enterprises deploy AI models in a privacy-preserving and regulation-compliant manner. Their research and experience have identified four key areas that AI regulations aim to safeguard.

Dynamo AI’s four key areas of AI compliance

1. Security and Privacy: Traditional security policies and systems are no longer sufficient to address the complexities of AI. Enhanced measures are needed to prevent risks such as data leaks or breaches of personally identifiable information (PII). For instance, companies like Dynamo AI in partnership with other leaders in the field, play a key role in offering solutions that prioritize privacy and security.
2. Misinformation and Hallucinations: One of the critical concerns with AI is its potential to generate false or misleading information, commonly referred to as “hallucinations.” These fabricated outputs can seem deceptively real, posing significant risks if unchecked. It’s also crucial to guard against AI models spreading misinformation through deepfakes or other manipulative content.
3. Protection of Proprietary Data: Protecting proprietary data and intellectual property rights is another key aspect of AI regulation. Standards must be set to ensure AI systems are safe, secure, privacy-preserving, and trustworthy. Before deployment, enterprises must engage in robust testing, evaluation, and risk mitigation.
4. Safety and Transparency: Regulations like the EU’s AI Act enforce clear guidelines, prohibitions, and enforcement mechanisms for AI systems operating within the European Union. This framework categorizes AI systems into four tiers based on the sensitivity of the data involved and the specific use case, explicitly prohibiting practices deemed to pose unacceptable risks.