Governance in the Cloud Takes Center Stage as Shadow AI Exposes Organizations
Most companies have no idea how many AI integrations are secretly running on their cloud infrastructure, leaving them vulnerable to attacks.
As cloud adoption continues to rise, organizations are struggling to maintain visibility and control over their sprawling digital estates. A key culprit behind this lack of understanding is the proliferation of “shadow AI” – AI systems that operate without explicit approval or oversight. According to recent estimates, 75% of all AI systems in use today are deployed without any formal governance or risk management procedures in place.
Discovering the Invisible AI Menace
So, how do you build a real inventory of the AI systems operating within your organization? The first step is to start looking. Use tools like AWS CloudWatch or Azure Monitor to identify and track all AI-related activity across your cloud infrastructure. This will help you detect not just known AI systems, but also any AI-powered services or applications that may be running undetected in the shadows.
From Shadow to Sunshine: Implementing Effective Governance
Once you’ve uncovered your shadow AI, it’s time to bring these systems under control. Start by classifying all sensitive data at creation, rather than trying to retroactively apply labels later on. Implement Identity Access Management (IAM) based enforcement to ensure that only authorized personnel can access and modify AI systems. And don’t forget to use policy-as-code to ensure that your AI systems comply with organizational policies and regulations.
Operational controls, such as automated monitoring and logging, are also crucial to preventing shadow AI from taking root in the first place. By combining these controls with a proactive approach to discovery and inventory management, organizations can finally get a grip on their AI systems and prevent them from becoming a liability.
