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MCP is the answer to a simple question: how does an LLM call a tool, read a database, or open a file in a way that any compliant AI application can consume? This guide explains the architecture, the transport layer, and three concrete use cases — and shows where a runtime control layer like Vaikora fits.

This is a working crosswalk that maps NIST AI RMF 1.0 functions (Govern, Map, Measure, Manage) and ISO/IEC 42001:2023 controls to concrete Vaikora capabilities — policy engine, 7-factor risk scoring, SHA-256 hash-chained audit, content-free logging, and automated reporting. The blog version below highlights the highest-cited rows from each function and explains the design choices behind the mapping.

I agent protocols are the standard ways autonomous AI systems discover services, exchange messages, and call tools across organizations. The four protocols that matter today are MCP (Model Context Protocol), A2A (Agent-to-Agent), ACP (Agent Communication Protocol), and ANP (Agent Network Protocol).

This article explains how AI agent activity can be surfaced in AWS Security Hub using Vaikora. Vaikora’s capabilities include providing deep visibility into AI agent operations and generating comprehensive audit logs for real-time monitoring, compliance tracking, and threat analysis.

Regex prompt sanitization fails because LLM payloads are not strings — they are encoded instructions, and a language model interprets meaning, not bytes.

Most CrowdStrike deployments have the same blind spot. Endpoints are covered, IAM behavior is logged, network traffic is monitored. But the AI agents running on that infrastructure, making thousands of decisions per day, generate zero signals in Falcon unless something hits the endpoint in a way that looks like traditional malware.

A Data Protection Impact Assessment (DPIA) for an AI workflow is the GDPR Article 35 record that documents the data flows specific to LLM applications — prompts, completions, embeddings, tool calls, RAG retrieval — together with the legal basis, retention schedule, identified risks, and the mitigations that bring those risks down to an acceptable level.

AI compliance in 2026 is what a CISO must prove to a board, an auditor, or a regulator about the AI systems running in production. This is the eight-item list of evidence patterns that survives the 2026 audit cycle. Each item below names what to prove, the canonical evidence pattern, and the framework references that ask for it.

Today’s security teams face the challenge of identifying not just known threats, but also emerging and unknown threats that can bypass conventional defenses. This is where artificial intelligence (AI) and machine learning (ML) are transforming the field.