Deep dives on AI agent governance, MCP security, compliance, and enterprise agentic architecture.
AI red teaming is the adversarial testing of AI, LLM, and agentic systems. Learn how it works, the attack surface, frameworks (OWASP, MITRE ATLAS, NIST), and how to run a continuous program.
A complete guide to the OWASP Top 10 for LLM Applications (2025). Understand each risk (LLM01 to LLM10), real attack examples, mitigations, and how it maps to MITRE ATLAS and NIST AI RMF.
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.
Prompt injection is the top LLM and AI agent security risk. Learn how direct and indirect attacks work, real-world examples, and a defense-in-depth playbook to stop them.
What an enterprise AI platform is, its reference architecture, how to evaluate build vs buy, and how to secure and govern autonomous AI agents.
AI security posture management (AISPM) helps you discover, inventory, and reduce risk across AI models, agents, and pipelines. Learn how AISPM works, how it compares to CSPM and DSPM, and how to start.