Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI is a part of just about every organization—whether you're deploying AI, leveraging vendors who use it, or perhaps even building a model yourself. With AI moving faster than the pace of regulation, it’s natural for concerns around AI security and responsible usage to be top of mind. ‍ We hear from customers and prospects often who are looking for guidance to prove and demonstrate AI compliance and best practices.

Compliance officers and IT executives are under constant pressure in today’s rapidly evolving regulatory landscape to ensure that their organizations not only meet current regulatory mandates but also prepare for future challenges. The integration of artificial intelligence (AI) into compliance-related operations, particularly automated audits, is transforming the approach to regulatory oversight.

More than 80% of Fortune 500 companies rely on ChatGPT Enterprise, not just as a productivity tool, but as a platform for building custom GPTs, leveraging knowledge files, and enabling collaboration through Canvas. As AI Agent adoption accelerates across every corner of the enterprise, business users are innovating faster than ever, often without security oversight.

The digital age has introduced both immense opportunities and unprecedented challenges for businesses. Data is a powerful asset, yet managing its governance, security, and compliance requires intricate strategies and solutions. Leveraging artificial intelligence (AI) in these areas has become a game-changer in transforming how organizations of all sizes approach their data capabilities.

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.

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.