Resources
The vocabulary, kept straight.
Plain-language definitions of the AI and governance terms that come up when AI agents enter a business that has to answer for how its systems behave. Accurate, practical, and built to be cited.
Terms
AI and governance, defined plainly.
AI foundations
- AI agent
- A software system that uses a language model to take actions toward a goal, not just answer a question. An agent can call tools, read and write data, and chain several steps together, which is why the access it holds and the oversight around it matter more than for a simple chatbot.
- Fine-tuning
- Further training of an existing model on a focused set of examples so it performs better on a specific task or in a specific style. It changes the model`s weights, unlike prompting or retrieval, which leave the model unchanged.
- Hallucination
- Output from a language model that is fluent and confident but factually wrong or unsupported by its sources. Because models generate plausible text rather than retrieve verified facts, hallucination is a built-in risk that grounding and human review are meant to contain.
- Inference
- The act of running a trained model to produce an output from a given input, as opposed to training the model in the first place. Most of the ongoing cost and latency of an AI feature comes from inference, since it happens on every request.
- Large language model (LLM)
- A model trained on very large amounts of text to predict and generate language, which lets it answer questions, summarize, write, and reason over text. An LLM does not store facts the way a database does; it produces likely continuations, which is why grounding and review matter.
- Model context (memory)
- The information a model can see for a single request, including the prompt, prior turns, and any retrieved documents, all bounded by a context window. Anything outside that window is not available to the model unless it is supplied again or stored in an external memory.
- Prompt
- The instruction and context given to a model to produce a response. Prompt design strongly affects output quality, and in production a prompt is often assembled from a fixed template plus the user`s request and any retrieved data.
- Retrieval-augmented generation (RAG)
- A pattern that retrieves relevant documents from a trusted source and supplies them to the model so its answer is grounded in that material. RAG reduces hallucination and lets a system answer from private or current data without retraining the model.
Governance and control
- AI governance
- The set of rules, roles, and controls that decide how AI is allowed to be used, who is accountable for it, and how its risks are managed. It is the structure that lets a company adopt new capability without inheriting unchecked exposure.
- AI readiness
- A measure of how prepared a business is to deploy AI safely and usefully, across its data, processes, controls, and people. A readiness review identifies where AI creates real leverage and what has to be in place before any agent touches live systems.
- Approval gate (human-in-the-loop)
- A checkpoint where a person must review and approve a decision before an agent acts on it. Gates are placed on the actions that carry real risk, so automation handles the routine work while a human stays in control of the consequential calls.
- Audit trail
- A durable record of each meaningful action a system took, who or what initiated it, and when. In regulated settings the trail is captured as the system runs rather than reconstructed after the fact, so it can stand as evidence during a review.
- Governed AI
- AI that operates inside explicit controls: scoped permissions, human approval on risky actions, audit trails, and instrumentation that proves how it behaved. The point is to capture the productivity of AI while keeping its actions visible, bounded, and accountable.
- Guardrails
- The technical and policy limits that constrain what an AI system can do or say, such as blocked topics, output checks, action limits, and required approvals. Good guardrails are enforced by the system rather than left to the model`s good behavior.
- Permissioning (least privilege)
- Deciding and enforcing exactly what each agent or user is allowed to access and do, granting only what a task requires. Least privilege keeps access narrow by default, so a system can touch the data and actions for its job and nothing more.
- Risk tier
- A classification that ranks an action or use case by how much harm it could cause, so controls can be matched to stakes. Low-tier work can run automatically, while high-tier actions route through approval gates and tighter logging.
- Role-based access control (RBAC)
- An access model that grants permissions to defined roles, then assigns people or systems to those roles, rather than wiring permissions to each individual. It makes access easier to reason about and audit, since you review a handful of roles instead of every account.
- Row-level security (RLS)
- A database control that limits which rows a given user or request can read or change, enforced by the database itself rather than by application code. In multi-tenant systems it is the backstop that keeps one customer`s data from being exposed to another.
- Shadow AI
- AI tools used inside a company without approval, oversight, or security review, often by well-meaning staff pasting work into public chatbots. It is a governance risk because sensitive data can leave the business and no audit trail exists for what was done.
Regulated AI
- Business Associate Agreement (BAA)
- A contract required under US HIPAA between a covered entity and any vendor that handles protected health information on its behalf. The BAA binds the vendor to the same safeguards and breach-notification duties, and is a prerequisite before a vendor or AI service may process PHI.
- Compliance instrumentation
- Building the controls and records a regulation expects directly into a system, so evidence of how the rules were followed is produced by the system itself. It replaces the scramble of assembling proof by hand at audit time.
- Data residency
- The requirement that data be stored and processed in a specific country or region, often to satisfy a law or contract. For AI deployments it shapes which models, cloud regions, and providers are permissible, since sending data to an out-of-region service can breach the rule.
- EU AI Act
- A European Union regulation that governs AI systems using a risk-based approach, placing stricter obligations on higher-risk uses and prohibiting a narrow set of practices outright. It applies broadly to providers and deployers whose systems are used in the EU, and is phasing in over several years.
- NIST AI Risk Management Framework (AI RMF)
- A voluntary framework published by the US National Institute of Standards and Technology to help organizations identify, assess, and manage the risks of AI systems. It is organized around core functions for governing, mapping, measuring, and managing AI risk, and is widely used as a reference for responsible AI practice.
- PHI (Protected Health Information)
- Individually identifiable health information that is protected under US HIPAA, covering the medical and payment details that can be tied to a specific person. Any AI system that processes PHI inherits HIPAA obligations, including safeguards, access controls, and a signed BAA with the provider.
- PII (Personally Identifiable Information)
- Information that can identify a specific individual, on its own or combined with other data, such as a name, email, government ID, or device identifier. How PII may be collected, stored, and shared is governed by privacy laws, which constrain what an AI system can do with it.
IntellaGrow
- Command Center
- IntellaGrow`s governed AI operating system: the environment where agents are deployed with permissioning, human-in-the-loop approval on risky decisions, audit trails, and compliance instrumentation built in. It is how capability ships with the guardrails attached rather than bolted on later.
- Fractional AI leadership
- A senior technology leader who owns a company`s AI strategy and accountability on a part-time basis, rather than as a full-time executive hire. You get the judgment and ownership of a chief technology or AI officer for the share of their time your stage actually needs.
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