In traditional environments, compliance was something you achieved and audited. In the cloud, it's something you have to maintain. A misconfigured storage bucket, an over-permissioned API call or a container behaving outside its baseline can expose sensitive data before a human analyst even opens their laptop. Static rules and point-in-time assessments simply cannot keep pace with infrastructure that spins up, drifts and scales in real time.

AI-powered applications running in cloud environments also create new risks, from unauthorized access to sensitive training data and excessive model permissions to shadow AI deployments and AI-generated code introducing misconfigurations into production environments.

This session explores what genuine cloud compliance looks like when your environment is constantly changing. We'll examine how security teams are shifting from reactive auditing to continuous control monitoring, embedding compliance validation directly into deployment pipelines, baselining workload behavior to catch anomalies before they become breaches and using auto-remediation to close misconfigurations the moment they appear.

Join this session to learn:

• How to shift compliance left by embedding controls directly into your deployment pipelines before misconfigurations ever reach production
• How to govern AI workloads in cloud environments, manage emerging compliance requirements and reduce risks associated with AI models, data exposure and shadow AI deployments
• Why continuous control monitoring against frameworks like CIS Benchmarks and SOC 2 delivers stronger security posture than periodic audits
• Practical approaches to auto-remediation that close vulnerabilities in real time without disrupting development velocity