Blog

As AI continues to transform industries worldwide and organizations continue to innovate their use of AI in regular practice, they are also faced with growing pressure to demonstrate that their AI systems are secure, trustworthy, and responsible. With regulatory scrutiny and public concern over widespread use of AI on the rise, aligning with established frameworks and standards has become essential for maintaining credibility and mitigating risk.

As the adoption of artificial intelligence (AI) continues to grow and evolve across industries, so do concerns about security, trust, and responsible use and management. In response, as a joint effort between the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), the ISO/IEC 42001:2023 framework was officially published in December 2023.

*Disclaimer: This article was written using a translated copy of the South Korea AI Basic Act* After the European Union paved the way for creating a legal framework for artificial intelligence (AI) in early 2024, many wondered what government or jurisdiction would follow. The year continued with discussions on how to best implement AI governance and debates on where the line stands between sufficient governance and proper opportunity for creativity in the technology industry. Fast forward a couple of months, as the world prepared to welcome in the new year those questions were finally answered. In late December 2024, South Korea stepped forward proposing their own legislation regarding AI. By January 21, 2025, they became the second entity to propose AI regulation with the passing of the AI Basic Act. To address the obvious next question of when these regulations will be enforced, the enforcement date stands as January 22, 2026, giving organizations roughly a year to prepare. It’s also worth noting that this act contains six sections with 43 articles, and we've outlined the key points below.

Since the release of ISO 42001 in late December 2023, it’s been a year of discovery and education regarding this new flagship artificial intelligence (AI) standard in terms of determining its applicability, use case(s), and benefits to organizations. For those who have since determined ISO 42001 is the right framework for them, the next step has been to prepare for certification, and that involves more than a few steps.

Even as AI systems become more advanced and enmeshed in daily operations, concerns regarding whether large language models (LLMs) are generating accurate and true information remain paramount throughout the business landscape. Unfortunately, the potential for AI to generate false or misleading information—often referred to as AI “hallucinations”—is very real, and though the possibility poses some significant cybersecurity challenges, there are ways organizations deploying this technology can mitigate the risks.

For anyone immersed in digital technology, you know that artificial intelligence (AI) is all the rage right now, and for good reason, the use cases for this technology are growing all the time. But as AI continues to enmesh with daily life as well as business, security concerns have grown in parallel, as have questions regarding the implications on organizations and their ongoing compliance efforts. At the top of mind for many has been how AI factors into SOC 2 examinations.

Artificial intelligence (AI)—you’ve heard of it, you’re likely using it, and you know it’s already used everywhere and its reach will only likely increase. These days, the term "AI" is thrown around frequently, but because this technology is actually made up of many different subsets that generally all get thrown under the umbrella of AI, it can sometimes lead to confusion.

Need for Secure LLM Deployments As businesses increasingly integrate AI-powered Large Language Models (LLMs) into their operations via GenAI (Generative AI) solutions, ensuring the security of these systems is on the top of everyone’s mind. "AI Red Teaming" (which is closer to Penetration Testing than a Red Team Assessment) is a methodology to identify vulnerabilities within GenAI deployments proactively. By leveraging industry-recognized frameworks, we can help your organization verify that your LLM infrastructure and execution is done securely.