Have you noticed it's been hard to keep pace with AI news lately? In this article, I examine how traditional risk frameworks apply to emerging AI technologies, with a particular focus on Google's Agent-to-Agent (A2A) communication and Anthropic's Multi-Context Planning (MCP).
This piece explores:
- The n^a potential workflows created by Agent-to-Agent communication
- How traditional GRC frameworks fall short with modern AI implementations
- Three practical risk mitigation strategies for enterprise AI deployment
If you're responsible for technology risk management in your organization, this analysis provides a practical framework for approaching AI integration with appropriate safeguards.
As AI systems become more autonomous and capable of communicating with each other, the complexity of potential interactions grows exponentially. Enterprise risk teams need to develop new approaches to manage these interactions, particularly in regulated industries.
Traditional governance, risk, and compliance (GRC) frameworks were designed for human-to-human and human-to-machine interactions. They struggle to account for complex machine-to-machine interactions, especially when those machines possess adaptive learning capabilities.
Read the full article to learn about specific frameworks and approaches that work in today's rapidly evolving AI landscape.