The AI governance glossary.
Plain definitions of the terms that run through enterprise AI governance — from gateways and control planes to agent identity and shadow AI. Written to be quoted; each entry ends with how it works in Brutor.
What is A2A (Agent2Agent)?
A2A is an open protocol, governed by the Linux Foundation, that lets AI agents built on different frameworks discover and work with each other. Each agent publishes an agent card — a machine-readable description of its capabilities — at a well-known URL, and other agents send it tasks over a standard message format. Version 1.0 added cryptographically signed agent cards, giving agents verifiable identity rather than self-declared descriptions.
What is agent identity?
Agent identity is the practice of giving each AI agent its own first-class identity — instead of a shared API key — so that every action an agent takes can be attributed, authorized, and revoked individually. It matters because agents act autonomously: when something goes wrong, “which key was used” is a much weaker answer than “which agent did what, under whose authority.”
What are agent skills?
Agent skills are packaged, versioned units of capability for AI agents — typically a SKILL.md instruction file plus scripts, templates, and resources — that an agent loads progressively when a task calls for them. The convention originated with Anthropic’s Agent Skills specification — and while unqualified “skills” can mean anything from prompt libraries to assistant plug-ins, agent skills names this specific portable format. Most platforms treat skills as artifacts to author, version, and sync to agents; the governance question is what happens when the skill actually runs.
What is AI compliance?
AI compliance is demonstrating that an organization’s AI use meets the requirements of laws and frameworks — the EU AI Act, SOC 2, ISO/IEC 42001, HIPAA, GDPR — with evidence rather than assertions. It has two halves: operating AI within the rules, and being able to prove it when the auditor arrives. The frameworks increasingly expect runtime records: not “we have a policy,” but “here is every decision the policy made.”
soc2.cc6.6, eu_ai_act, iso_42001), exports are per-framework and auditor-ready, and an ISO 42001 readiness tracker guides your progress toward certification. Compliance →What is an AI control plane?
An AI control plane is the management layer that governs an organization’s entire AI estate — models, tools, agents, and spend — through one point of policy, identity, and observability. The term borrows from network and Kubernetes architecture: the data plane moves traffic, the control plane decides what’s allowed. Where an AI gateway routes and secures calls, a control plane adds the estate-wide layer: inventory, cost, compliance evidence, and control over AI that doesn’t route through it.
What is an AI gateway?
An AI gateway is a proxy that sits in the request path between applications and AI providers, applying routing, security, and cost controls to every call. It evolved from the API gateway: where API gateways manage REST traffic, AI gateways understand LLM requests, MCP tool calls, and agent-to-agent messages — and add AI-specific controls like prompt guardrails, token budgets, and model failover. One integration point, many providers, uniform policy.
What is AI governance?
AI governance is the set of controls and evidence an organization applies to its AI use: who may use which models and tools, within what limits, and with what record. It spans two worlds that are converging — organizational governance (policies, risk assessments, compliance workflows) and runtime governance, where rules are enforced in the request path as AI is actually used. Analysts increasingly treat runtime enforcement as the part that makes the rest real: a policy that nothing enforces is documentation.
What is an API key vault?
An API key vault stores AI provider credentials centrally and encrypted, so applications and developers never hold raw OpenAI, Anthropic, or other provider keys. Clients get scoped stand-in credentials instead, resolved server-side on each call. The payoff is blast radius: a leaked scoped key is revoked in one place with its spend limits attached, and rotating a provider key no longer means hunting it down across codebases and CI systems.
What is an argument policy?
An argument policy is a governance rule that parses the arguments of a tool call — the SQL statement, URL, shell command, file path, or JSON payload — and applies syntax-aware rules to them. Text-level checks can’t tell a harmless query from a destructive one; an argument policy understands the language being passed, so it can allow reads and refuse writes in the same tool.
What is batch processing (for AI)?
Batch processing submits a large set of model requests as one asynchronous job with a completion window — typically 24 hours — instead of call-by-call: the pattern behind classification backlogs, document pipelines, and evaluation runs, usually at a lower per-token price from providers. The governance catch is that batch traffic is easy to treat as exempt — yet it’s often the highest-volume path your data takes to a model.
What is FinOps for AI?
FinOps for AI applies cloud financial-operations discipline to AI spend: allocating costs to teams and products, setting budgets, tracking unit economics (cost per token, per call, per agent run), and catching anomalies before the invoice does. It’s now a mainstream FinOps Foundation practice area, complicated by the fact that AI spend spans API calls you route, SaaS subscriptions you buy, and infrastructure you run.
What are AI guardrails?
AI guardrails are in-path checks on what goes into and comes out of AI models: detecting and blocking (or redacting) personal data, prompt-injection attempts, secrets like API keys, and toxic content. Input guardrails protect the model from malicious or careless prompts; output guardrails protect users and data from what the model returns. Streaming output guardrails — applied while a response is still being generated — are the harder, rarer variant.
What is human-in-the-loop (HITL)?
Human-in-the-loop is the pattern of pausing an automated action for human approval before it proceeds. For AI agents it’s the safety valve for high-impact operations: the agent can prepare the action, but a person authorizes it. Good implementations make approval auditable and unforgeable — the approval applies to one specific action, once.
What is a knowledge base (in AI)?
In an AI platform, a knowledge base is a curated collection of company content — documents, wikis, tickets — indexed as vectors so models can answer from your facts (see RAG). The hard questions are less about embeddings than about boundaries: who may query which collection, how content stays in sync with the system it came from, and whether the source’s structure and permissions survive ingestion.
What is an LLM (large language model)?
An LLM is a neural network trained on massive text corpora to understand and generate language — the model class behind ChatGPT, Claude, and Gemini. In enterprise conversation, “LLM” has stretched into shorthand for AI models in general, including ones that generate images, audio, and video; when precision matters, model is the broader word and LLM is its text-first subset. Operationally they behave alike — token-based pricing, prompt-driven behavior, provider APIs — which is why they can be governed alike.
What is MCP (Model Context Protocol)?
MCP is the open standard that lets AI applications and agents use external tools and data through one uniform interface. An MCP server exposes capabilities — query a database, search a wiki, file a ticket — and any MCP-aware client can discover and call them. Introduced by Anthropic in 2024 and now governed under the Linux Foundation’s Agentic AI Foundation, it has become the de facto way enterprise agents reach systems of record.
What is an MCP gateway?
An MCP gateway proxies Model Context Protocol traffic the way an API gateway proxies REST: MCP servers are registered centrally, clients connect through one endpoint, and the gateway applies authentication, per-tool access control, and logging to every call. It solves MCP’s enterprise problem — dozens of servers, hundreds of tools, every client connecting directly — by putting one governed hop in the middle.
What is an MCP registry?
An MCP registry is a catalog where MCP servers are published and discovered — the address book of the tool ecosystem. The official MCP registry specification (December 2025) defines how registries federate, so an enterprise can run a private subregistry that mirrors the public ecosystem and adds its own internal servers. A registry answers “what exists”; it takes a gateway to control what’s actually called: the registry is the yellow pages, the gateway is the switchboard.
What is an OAuth proxy?
An OAuth proxy stands between AI clients and the SaaS systems their tools reach: it runs the OAuth flow on the user’s behalf and keeps the resulting tokens server-side. Without one, agent frameworks and MCP clients end up holding long-lived Jira, Slack, and GitHub tokens on laptops. With one, the client carries a short-lived stand-in credential; the real token stays vaulted and auto-refreshed, and every call runs under the actual user’s identity.
What is OpenTelemetry?
OpenTelemetry (OTel) is the open, vendor-neutral standard for emitting traces, metrics, and logs, carried over its OTLP protocol to whichever observability backend you run — Grafana, Datadog, Splunk. For AI infrastructure it’s the difference between a black box and a component your SREs monitor like any other: gateway latency, token throughput, and error rates land in the dashboards they already watch.
What is RAG (retrieval-augmented generation)?
RAG grounds a model’s answer in retrieved content: the question is embedded, the most relevant chunks are fetched from a vector index of your documents, and the model answers from those chunks instead of from training memory alone. It’s the standard remedy for stale knowledge and invented facts — and the reason “which documents can the model see?” became a governance question: retrieval reach is data access.
What is Rust (and why does it matter in a gateway)?
Rust is a systems programming language that pairs C-class performance with compile-time memory safety and no garbage collector. For software that sits in the request path, that combination is the point: no garbage-collection pauses means predictable tail latency under load, and the memory-safety bug class behind many proxy and parser CVEs is ruled out at compile time instead of patched afterward.
What is semantic caching?
Semantic caching answers a repeated question from cache even when it’s phrased differently: requests are embedded as vectors, and a new request that lands close enough to a stored one returns the saved response instead of a fresh model call. On high-repetition traffic it cuts both cost and latency. The caveat is scope: a cache shared across access boundaries can leak one team’s answers to another, so cache reach has to follow permissions.
What is a semantic policy?
A semantic policy is a governance rule written in natural language and evaluated by an AI judge model on each request — catching intent that pattern-based rules miss. “Never commit to pricing” or “don’t extract customer personal data to files” can’t be expressed as a regex; a judge model can recognize both, however they’re phrased. Mature implementations run new policies in shadow mode — logging what would have been blocked — before switching them to enforce.
What is shadow AI?
Shadow AI is AI use an organization hasn’t sanctioned and often can’t see: personal chatbot accounts handling company data, API keys nobody registered, agents and MCP servers running on laptops. It’s the successor to shadow IT, moving faster — because trying AI requires nothing but a browser. The governance response has three steps: discover what exists, inventory it, and bring what can be routed under policy.
What is a token (in AI)?
A token is the unit an LLM reads, generates, and bills by — a fragment of a word, roughly four characters of English. Prompts and responses are measured in tokens, and a model’s context window — how much it can consider at once — is a token count too. The word collides with an unrelated security meaning: access tokens such as API keys and JWTs. “Token limits” can be a budget question or a credential question, depending on which kind is meant.
What is a User Portal?
A User Portal is the front door to your organization’s own AI: one workspace where employees chat with approved models, call approved tools, and search company knowledge. That’s a different thing from an assistant you buy, like Microsoft Copilot or ChatGPT — a sealed product with the vendor’s models and the vendor’s interface, which you can observe and cost but never route (see FinOps for AI). A User Portal is the reverse: you choose the models behind it, your policies screen every call, and every conversation lands in your own audit trail.
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