Michael
Michael is currently directing organizational efforts to mitigate cyber and governance risks associated with ungoverned artificial intelligence solutions. His work specifically targets the challenges posed by uncontrolled AI implementations, focusing on systems that operate outside formal oversight and control structures. This initiative aims to establish robust governance frameworks to manage the increasing complexity of AI deployment within the enterprise.
A primary area of concern addressed by Michael is the proliferation of Shadow AI, which refers to AI tools deployed and utilized by employees without IT department approval or knowledge. These unmonitored instances create significant security vulnerabilities and compliance gaps, as they bypass standard data protection protocols and audit trails.
In addition to Shadow AI, Michael is evaluating the risks introduced by agentic frameworks. These autonomous systems can execute complex tasks and make decisions with minimal human intervention, potentially amplifying errors or security breaches if not properly constrained. His strategy involves defining clear boundaries and monitoring mechanisms to ensure these agents operate within established ethical and technical guidelines.
The overarching goal of Michael’s initiative is to create a cohesive governance model that balances innovation with risk management. By identifying and regulating ungoverned AI solutions, the organization seeks to maintain data integrity, ensure regulatory compliance, and prevent unauthorized data exposure. This approach ensures that AI adoption remains aligned with broader corporate security policies and operational standards.