How to Safeguard GenAI Workloads in Exposed Environments

With a staggering 34% of all GenAI workloads publicly exposed, the need for swift detection of active risk has never been more urgent. Security teams, especially SecOps, are under immense pressure to identify and mitigate threats to AI models and data without delay. Mounting compliance pressures, fueled by recent and significant regulatory actions, have put security teams on high alert. The need to detect, prioritize, and remediate risks in real time, correlating assets and leveraging runtime insights, is more pressing than ever. Within the last six months, President Biden issued an executive order, the NTIA called for independent audits of high-risk AI systems, and the EU adopted the world's first legal regulation around AI. This all leads to security teams needing to detect, prioritize, and remediate active AI risk by: - Detecting threats in real-time to prioritize the most critical risk to AI workloads and data - Correlating AI assets with activity and visualizing risk with interconnected resources - Leveraging runtime insights to identify in-use AI packages with the highest risk to prioritize the most critical vulnerabilities to fix first Join this webinar to learn how to gain visibility into AI workloads, identify active risk and suspicious activity in real-time, and ensure compliance with emerging AI guidelines.