A2A vs MCP: What's the Difference?
MCP connects agents to tools; A2A connects agents to each other. Understand how both protocols divide labor and what security controls each one demands.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
MCP connects agents to tools; A2A connects agents to each other. Understand how both protocols divide labor and what security controls each one demands.
A detection-focused how-to for prompt injection: pattern rules, ML classifiers, LLM judges, and behavioral signals — and where to place each one.
MCP gateway options compared as of October 2026: open-source gateways, API-gateway extensions and governance control planes, and what to look for.
Evaluating alternatives to Zenity for AI agent security: what Zenity focuses on, why teams shop the category, and a factual survey of options.
Evaluating alternatives to Lakera for LLM guardrails: what Lakera focuses on, when teams need more than a content API, and the options.
Evaluating alternatives to Prompt Security after the SentinelOne acquisition: what it covers, why buyers re-shop the category, and the options.
A ready-to-use RFP checklist for evaluating AI governance platforms: identity, policy enforcement, guardrails, spend controls, audit, and compliance.
Evaluating alternatives to Astrix Security for non-human identity governance — and when agent-native identity needs a different layer.
Microsoft's agent governance stack — the open-source Agent Governance Toolkit, Entra Agent ID, Purview — and where an independent control plane fits.
What insurers deploying AI agents in underwriting and claims must meet: the Colorado AI Act, EU AI Act high-risk rules and the NAIC model bulletin.
How law firms and legal departments govern AI agents: privilege protection, matter-level access, ethical walls, and audit-ready evidence.
A practical guide to what AI agent observability must cover — cost, behavior, and policy compliance — and the key criteria for choosing the right tooling.
Calculate AI agent cost per completed task, including failed attempts and retries. Use a reproducible example and a checklist for a defensible benchmark.
What public bodies deploying AI agents must meet under the EU AI Act and administrative law: high-risk uses, transparency, registration and human review.
Set enforceable AI agent budgets with reservation-based enforcement, graduated thresholds, and clear attribution — before overruns reach your invoice.
The OWASP LLM Top 10 read through an agent lens, how it differs from the Top 10 for Agentic Applications, and the controls that address each risk.
AI guardrails and LLM firewalls both inspect content but solve different problems. Learn the distinctions, evaluation approaches, and fail-mode trade-offs.
How to keep AI agent data within jurisdictional boundaries, satisfy GDPR and cross-border transfer rules, and produce the evidence regulators expect.
Any agent with a fetch tool is an SSRF primitive with a natural-language interface. The attack paths, why allow-lists beat denylists, and how to bound egress.
How to give each AI agent its own identity and authenticate it with scoped, short-lived credentials — so every action is attributable and revocable.
An honest framework for deciding whether to build AI agent governance in-house or buy a platform, weighed by risk, team capacity, and time-to-value.
A practical checklist for AI agent incident readiness: inventory, instant revocation, tamper-evident audit trails, runbooks, and communication templates.
An API gateway manages traffic; an AI control plane governs agents. Learn the five critical gaps gateways leave open and what a control plane adds.
Loop-and-burn failures drain AI budgets fast. Learn the blast radius, five root conditions, and the layered controls that stop runaway spend before the invoice.