AI Agent vs. AI Assistant vs. Copilot: The Risk Difference
Assistant, copilot, and agent are not interchangeable marketing terms. Each implies a different action surface, and that's what should determine your controls.
How to think about adopting, governing, and getting ROI from AI agents.
What an AI agent control plane is, why it emerged, how it differs from an API gateway, and the seven capabilities a production agent deployment needs.
Read the guide →Assistant, copilot, and agent are not interchangeable marketing terms. Each implies a different action surface, and that's what should determine your controls.
RPA governance was built for deterministic bots. Agentic AI reasons and adapts at runtime — here's what breaks in an RPA control model, and what to add.
Gartner predicts 40%+ of agentic AI projects will be canceled by 2027, citing cost, unclear value, and risk controls. What governance actually fixes.
AI TRiSM stands for trust, risk, and security management — Gartner's framework for governing AI systems, and where runtime controls fit.
Cato–Aim, Palo Alto–Protect AI, Cisco–Robust Intelligence: what 2026's AI security acquisitions mean when you're shortlisting vendors.
A decision framework for choosing platform-bundled or pure-play AI agent security vendors after 2026's acquisition wave.
An operating model for enterprise AI agent governance: who owns identity, roles, delegated admin, central policy and audit as agents spread across teams.
How SaaS teams embedding AI agents keep multi-tenant data isolated, costs attributed per customer, and agent behavior governed at scale across tenants.
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.
Microsoft's agent governance stack — the open-source Agent Governance Toolkit, Entra Agent ID, Purview — and where an independent control plane fits.
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.
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.
Criteria for evaluating AI agent governance platforms, then a disclosed list of platforms as of October 2026, grouped by approach and what each is best for.
Practical frameworks for quantifying AI agent ROI — cost per outcome, time recovered, and deflection rate — so you can move beyond vanity usage metrics.
A staged rollout playbook for AI agents: inventory risk, run a scoped pilot with guardrails in place, define go/no-go criteria, and expand on evidence.
Self-hosted AI governance gives full data residency control; managed shifts operational burden to the vendor. How to choose for your team.
Agent frameworks are built to orchestrate, not to govern. Evaluate identity, authorization, spend, audit, and sandboxing separately before you commit to one.
Guardrails check what is said; policies govern rate limits and time windows. How the two AI control layers differ, and why neither replaces the other.
Secure multi-agent workflows with authentication at every hop, scoped delegation tokens, and content guardrails on every message—three patterns explained.