Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI adoption continues to move at a breakneck speed for businesses across the globe, as shown in a Coatue report comparing AI adoption to early internet adoption and found that AI growth is happening at two times the speed of the former.

In the modern workplace, GenAI models have become powerful assets due to their ability to introduce efficiency, up level product innovation, and expedite how teams close the gap on competitors. However, these powerful tools also introduce significant risks related to data security and governance. Companies that aren’t actively figuring out how to govern the GenAI they’ve adopted will inevitably be left vulnerable.

For small and medium-sized enterprises (SMEs), staying abreast of cybersecurity developments is critical — not just to growth, but to the company’s bottom line. But security evolves quickly, and it can be hard to keep up. In the midst of this fast-evolving security environment, AI has made a bold entrance. So bold, in fact, that only 10% of SMEs aren’t using AI and don’t plan to, according to a recent JumpCloud research report.

As more and more companies adopt low-code platforms and launch AI applications, the need for proper data security has never been greater. While it’s true that low-code platforms give users the freedom to develop powerful AI solutions with minimal programming knowledge or experience, this same level of flexibility also inadvertently exposes applications to potential data breaches.

AI has completely changed how we live, work and play. With its unparalleled efficiency, ongoing learning abilities and its detailed precision, it makes short work out of what used to be more complex and cumbersome tasks. Although AI systems are incredibly powerful and only growing in capacity and scale, they’re not without their challenges. Like other types of programs and infrastructures, AI is not immune to vulnerabilities and security issues.