Deep dives on AI agent governance, MCP security, compliance, and enterprise agentic architecture.
AI guardrails are runtime controls that constrain what an LLM or AI agent can take in, output, and do. Learn the types, architecture, agent-specific controls, and best practices.
The NIST AI Risk Management Framework (AI RMF 1.0) is voluntary U.S. guidance for managing AI risk. Learn its four functions (GOVERN, MAP, MEASURE, MANAGE), the Generative AI Profile, how it compares to ISO 42001 and the EU AI Act, and how to adopt it.
AI risk management is the continuous practice of identifying, assessing, and controlling the risks of AI systems and agents. Learn the risk categories, frameworks (NIST AI RMF, ISO 42001), program lifecycle, and best practices.
AI observability is how teams see, evaluate, and govern LLM and AI agent behavior in production. Learn the core pillars, key metrics, challenges, and how to choose an approach.
What an enterprise AI platform is, its reference architecture, how to evaluate build vs buy, and how to secure and govern autonomous AI agents.
Non-human identities (NHIs) like service accounts, API keys, and AI agents now outnumber people many times over. Learn what NHIs are, why they are a security risk, and how to manage and secure them.
LLM observability is how teams trace, monitor, and evaluate large language model apps in production. Learn the three pillars, key metrics, architecture, and best practices.
Shadow AI is the unsanctioned use of AI tools, agents, and MCP servers inside your org. Learn the real risks, examples, and how to detect and govern it.