Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As AI becomes deeply integrated into critical business operations and adopted by increasing numbers of departments and employees, the volume and sensitivity of data flowing into these systems has grown exponentially. Companies now face a dual challenge: harnessing AI's potential while managing the substantial data risks it introduces.

Modern infrastructure is already complex, characterized by distributed environments, multi-cloud deployments, and dynamic change. Now add Large Language Models (LLMs) to the mix, and the challenge grows exponentially. Engineering leaders are under pressure to deliver innovation fast, while also safeguarding against breaches, misconfigurations, and human error. That’s why initiatives like eliminating static credentials, enforcing just-in-time access, and reducing SSH key sprawl are gaining traction.

Is your team's favorite new productivity tool also your biggest data leak waiting to happen? Generative AI (GenAI) assistants like ChatGPT, Microsoft Copilot, and Google Gemini have quickly moved from novelty to necessity in many workplaces. These tools use machine learning and advanced algorithms to help employees draft content, analyze data, and even write code faster than ever before.