As artificial intelligence becomes increasingly integrated into business operations, organizations face the challenge of managing its use effectively. AI is now embedded in numerous platforms, including SaaS applications, browsers, and various other tools. Despite its widespread adoption, most companies still rely on outdated security measures that don’t address where AI interactions occur, resulting in a gap in governance and control.
Understanding the Governance Gap
With AI’s role in enhancing productivity, enterprises are tasked with balancing innovation with governance, compliance, and security. A recent guide highlights that enterprises often misjudge the location of AI-related risks. This guide is the focus of an upcoming virtual event that will delve into understanding and controlling ‘shadow’ AI.
The core issue is not data management but the nature of AI interactions, which existing security tools are ill-equipped to handle. AI’s rapid integration into various platforms has outpaced the development of effective security measures, leaving a significant visibility and control gap.
AI Usage Control: A New Security Paradigm
AI Usage Control (AUC) emerges as a solution, offering a new layer of governance directly at the point of AI interaction. Unlike traditional security methods, AUC focuses on real-time interaction monitoring, using contextual risk signals rather than static lists or network flow analysis.
AUC answers critical questions about AI use, such as who is using it, how, through which tools, and under what conditions. This shift from tool-centric to interaction-centric governance is necessary for the security industry to keep pace with AI developments.
The Importance of Interaction-Centric Governance
Security teams often struggle with securing AI usage by relying on outdated methods like treating AUC as a mere feature or depending solely on network visibility. Such approaches leave significant security gaps due to their lack of focus on real-time interaction monitoring.
Effective AI usage management involves several stages, including discovery of all AI touchpoints, understanding real-time interactions, linking these interactions to identities, and enforcing adaptive policies. Real-time control is essential, moving beyond simple allow/block models to nuanced solutions that protect without disrupting workflows.
Architectural considerations are also critical, as the best solutions integrate seamlessly into existing workflows, providing effective governance without cumbersome deployments.
Future Outlook for AI Security
The shift toward interaction-centric governance reflects the evolving nature of AI security. As enterprises adopt AI more broadly, security measures must adapt to ensure both productivity and risk management. The guide offers a framework for evaluating AI security solutions, emphasizing real-time, contextual control as the key to scalable AI adoption.
Organizations that excel in managing AI usage will gain a competitive edge, leveraging AI’s full potential while maintaining security. The Buyer’s Guide for AI Usage Control provides essential insights for navigating this complex landscape, offering a path forward for secure and effective AI integration.
The virtual event on AI usage and shadow AI promises to shed further light on these critical issues. Interested parties are encouraged to join and explore how to harness AI safely and effectively.
