EXPERTISE
Concise executive perspectives on the architecture, governance, control and assurance questions that matter as AI moves deeper into enterprise operations.
EXPERTISE AREAS
These pages provide answer-first explanations and connect directly to relevant advisory services and research.
Generative AI systems create or transform content using foundation models. In enterprise environments, the risk surface extends beyond the model to prompts, retrieval systems, data sources, orchestration, plugins, identity, applications and runtime controls.
Explore expertise →Agentic AI refers to systems that can pursue goals through multi-step reasoning and action, often selecting tools, invoking APIs, using memory and interacting with other systems or agents with varying levels of human supervision.
Explore expertise →ISO/IEC 42001:2023 specifies requirements for establishing, implementing, maintaining and continually improving an Artificial Intelligence Management System (AIMS). It provides a management-system structure for governing AI-related responsibilities, risks, objectives, processes and controls.
Explore expertise →AI risk management is the structured process of identifying, assessing, treating, monitoring and communicating risks created or amplified by AI systems across their lifecycle, including impacts from data, models, applications, users, agents, third parties and operating context.
Explore expertise →Security governance translates business risk into decision rights, architecture principles, control requirements, ownership and assurance. In AI-enabled enterprises, the governance model must extend from human users and applications to models, workloads, agents and other machine identities.
Explore expertise →RESEARCH
The research library is published separately under /research/ using WordPress.
INDEPENDENT ADVISORY
For AI strategy, governance, Agentic AI, security, assurance or cyber-risk requirements, describe the decision you are facing and the context around it.