Deep dives on AI agent governance, MCP security, compliance, and enterprise agentic architecture.
MCP tool poisoning hides malicious instructions in an MCP tool's description or response so an AI agent executes them as trusted commands. Learn how the attack works, its variants, and how to prevent it.
AI threat detection finds and contains malicious, rogue, or compromised AI-agent behavior at runtime. Learn how it works, the agent threat landscape, core components, best practices, and how it compares to traditional security.
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.
What an AI audit is, what it examines, the audit process, audit trails, frameworks (NIST AI RMF, ISO 42001, SOC 2), who performs it, and how to become audit-ready for AI and autonomous agents.
A practical guide to EU AI Act compliance: who it applies to, the four risk tiers, provider and deployer obligations, GPAI rules, the 2026 timeline, penalties, and a step-by-step path to getting compliant.
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.
A complete guide to MCP identity: how authentication, authorization, OAuth 2.1, SSO, and least-privilege access work for Model Context Protocol servers, clients, and agents.