Practices
Practices are where hypotheses turn into reusable ways of working. When the environment changes quickly, you need patterns that help people, agents, and enterprises respond without pretending every situation is identical.
Each practice distills a Strategic Principle Hypothesis into concrete guidance: roles, pre‑conditions, steps, prompts, and artifacts you can adapt to your own context. They are designed so that humans can use them in workshops and projects, while agents can use the structure to support planning, assessment, and implementation.
Today, many practices draw on post‑AI enterprise lenses such as identity‑aware AI security and AI‑transformed security operations, but the category is intended to grow across governance, operating models, software architecture, and eventually non‑enterprise domains as the sensemaking commons expands.
Identity-Aware AI Security Practice
Implement identity-aware authorization as the primary AI control plane and run it as a closed loop across policy, retrieval, abstraction, security operations, and governance.
Post-AI Security Operations Practice
Extend security operations so every AI interaction is observable, analyzable, and convertible into better identity-aware policies and governance decisions.
Enterprise AI Governance Practice
Run Enterprise AI Governance as a peer board that steers AI use-cases, risk, and controls across identity-aware AI security pillars in line with enterprise values, obligations, and strategy.
Strategic Operations Governance Practice
Run the enterprise as transformation stacks with shared backlogs, cadences, and decision rights so humans and AI agents advance strategy together instead of in fragmented, hyperactive silos.
Agentic Enterprise Architecture Practice
Design and operate your enterprise “software” as an AI-driven agentic fabric that sits above systems and data, governed by codified business rules, unified data management, and identity-aware security, rather than bespoke application UIs.