Machine vs Workload vs Agent Identity: The Differences
Machine identity, workload identity, and agent identity are not synonyms. What each term means, where they overlap, and what agents add.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
Machine identity, workload identity, and agent identity are not synonyms. What each term means, where they overlap, and what agents add.
Non-human identity credentials from issuance to revocation: provisioning, scoping, rotation, monitoring, and decommissioning without sprawl.
The Nov 2025 MCP authorization flow, step by step: protected-resource metadata, client registration, PKCE, and RFC 8707 resource-bound tokens.
Third-party MCP servers are supply-chain risk: registry provenance, tool-description audits, version pinning, and an approval workflow that scales.
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.
A 2026 survey of MCP gateway options — open-source gateways, API-gateway extensions, and governance control planes — and how to choose between them.
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.
How insurers govern AI agents in underwriting and claims: consequential-decision controls, fairness monitoring, and exam-ready audit evidence.
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.
What AI agents cost in 2026: public model list prices, worked per-task and per-month scenarios, and the budget heuristics that follow.
How government organizations govern AI agents: citizen-facing decision controls, transparency-grade audit trails, sovereignty, and procurement.
Set enforceable AI agent budgets with reservation-based enforcement, graduated thresholds, and clear attribution — before overruns reach your invoice.
How each OWASP LLM Top 10 risk category maps to agentic AI deployments — and the governance controls that address them at the infrastructure layer.
Runtime security enforces per action, in-band; posture management assesses configurations out-of-band. What each catches and why you need both.
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.
How to give each AI agent its own identity and authenticate it with scoped, short-lived credentials — so every action is attributable and revocable.