Core integration terms
| Term | Plain-language meaning | Why it matters |
|---|---|---|
| AI integration | Connecting an AI system to real software, data, tools, permissions, logs, and infrastructure. | It turns AI from a separate tool into something that can work with real systems, which increases both usefulness and risk. |
| System boundary | The line between what the AI can and cannot access or affect. | Clear boundaries prevent vague, excessive, or accidental access. |
| Production AI integration | An AI connection used in real operations, not only a demo, test, or experiment. | Production use needs stronger monitoring, ownership, rollback, and review. |
| AI system architecture | The arrangement of models, data sources, applications, APIs, permissions, logs, users, and monitoring tools. | Architecture helps people see how the whole integration works instead of focusing only on the AI model. |
| Connector | A software link that lets one system exchange data or actions with another system. | Connectors can save time, but they need access limits and review. |
| Middleware | A layer between systems that helps route, transform, check, or control information. | Middleware can add useful control instead of letting AI connect directly to every system. |
| Webhook | An event-based signal from one system to another, often triggered when something changes. | Webhooks can start AI-assisted processes, but triggers should be limited and logged. |
| Tool calling | A design where an AI system can ask a connected tool to perform a defined action. | Tool calling needs strict limits because it may let AI retrieve data, create records, send messages, or trigger workflows. |
Data and knowledge terms
| Term | Plain-language meaning | Why it matters |
|---|---|---|
| Data readiness | The condition of data before it is connected to AI, including quality, organization, permissions, and usefulness. | AI connected to messy or poorly governed data can produce unreliable results. |
| Data pipeline | A path that moves data from one place to another, often with cleaning, transformation, or validation along the way. | AI integrations often depend on data being available in a usable form. |
| Data lineage | Information about where data came from, how it changed, and where it moved. | Lineage helps with trust, troubleshooting, auditability, and source review. |
| Source metadata | Extra information about a record or document, such as source, date, owner, version, or access level. | Metadata helps AI systems and reviewers understand context and reliability. |
| RAG | Retrieval-augmented generation: a method where AI retrieves information from selected sources before generating an answer. | RAG can reduce guesswork when the AI is grounded in approved documents or data. |
| Grounding | Connecting AI output to specific source material rather than relying only on the model’s general training. | Grounding can improve usefulness and help readers check where an answer came from. |
| Vector database | A database designed to help find information by meaning or similarity, not only exact keyword matches. | Vector databases are often used in RAG systems and document search integrations. |
| Document ingestion | The process of bringing documents into an AI-ready system so they can be searched, retrieved, summarized, or referenced. | Bad ingestion can create missing content, outdated information, duplicates, or permission mistakes. |
Access and security terms
| Term | Plain-language meaning | Why it matters |
|---|---|---|
| Access control | Rules that decide who or what can view, change, approve, or use a system or data source. | AI should not automatically receive access just because a human user or connected app has it. |
| RBAC | Role-based access control: permissions assigned based on defined roles. | RBAC helps AI integrations use specific roles instead of vague unlimited authority. |
| Least privilege | Giving a user, system, or AI agent only the access needed for the approved task. | Least privilege reduces the damage from mistakes, misuse, or unexpected behaviour. |
| Service account | A non-human account used by software or systems to access another system. | AI integrations may use service accounts, so ownership, limits, and logging matter. |
| Credential | Information used to prove identity or gain access, such as a password, token, API key, or certificate. | Credentials should be protected, rotated when needed, and not casually exposed. |
| Secret | A sensitive credential or key that should not be visible to ordinary users or stored carelessly. | Exposed secrets can let unauthorized people or systems access connected services. |
| Approval gate | A required review or permission step before a sensitive action happens. | Approval gates help keep AI from automatically taking actions that need human or policy review. |
| Audit trail | A record of actions, approvals, access, changes, timestamps, and responsible identities. | Audit trails help organizations investigate, prove, correct, and learn from AI-connected activity. |
Security note: This glossary explains defensive and governance concepts only. It does
not provide instructions for bypassing controls, evading logs, exploiting systems, or gaining unauthorized access.
Operations and monitoring terms
| Term | Plain-language meaning | Why it matters |
|---|---|---|
| Observability | The ability to understand what a system is doing from logs, metrics, traces, alerts, and other evidence. | AI integrations should not become invisible automation that no one can explain or review. |
| Logging | Recording events, requests, responses, actions, errors, and system activity. | Logs support debugging, review, monitoring, auditability, and incident response. |
| Tracing | Following a request or action across multiple systems to see what happened along the way. | Tracing matters when AI requests pass through apps, APIs, tools, databases, and model services. |
| Model drift | A change in model behaviour or performance over time. | Drift can reduce reliability even if the integration itself still works technically. |
| Data drift | A change in the data being used by the AI system compared with earlier conditions. | AI results may get worse if the real-world data changes but the system is not reviewed. |
| Latency | The delay between a request and a response. | High latency can make AI integrations frustrating or unsuitable for time-sensitive processes. |
| Rollback | Returning to an earlier version, setting, configuration, or connection after a problem. | Rollback gives teams a recovery path when a change breaks or worsens an AI integration. |
| Incident response | The process for handling serious problems, mistakes, failures, unsafe behaviour, or unusual activity. | AI integrations need a way to pause, investigate, fix, and safely restore service. |
Connected systems and device terms
| Term | Plain-language meaning | Why it matters |
|---|---|---|
| Device identity | A known identity for a physical or virtual device, such as a serial number, MAC address, profile, or registration record. | Connected systems should be known before they are trusted or allowed to act. |
| Configuration profile | A record of what a system is, who owns it, what role it has, what it can access, and what modes it can use. | Profiles help prevent vague authority and support maintenance, review, and revocation. |
| Operating mode | The current state of a system, such as normal, maintenance, restricted, degraded, quarantine, offline, or recovered. | Mode changes should be logged and should affect what the system is allowed to do. |
| Human override | A way for an authorized person to pause, stop, review, or take control from an automated system. | AI-connected systems should support responsible human control where appropriate. |
| Fallback mode | A safer limited state used when normal operation is not available or should not continue. | Fallback modes reduce harm when data, models, networks, sensors, or permissions fail. |
| Quarantine | A restricted state used when a system, device, account, or integration may be unsafe or untrusted. | Quarantine helps prevent a questionable system from continuing normal access until reviewed. |
Integration note: Connected devices and facility systems should be discussed at
the level of identity, permission, logging, safe escalation, human override, and qualified review.
This site does not provide operational instructions for hazardous systems.
Related reading
What Is AI Integration?
The best first article if you want the core definition and examples.
AI Access Control and RBAC
A deeper explanation of roles, permissions, and access boundaries.
RAG Integration Explained
How AI connects to approved knowledge sources and documents.
AI Observability Explained
Why logs, traces, metrics, and review matter after integration.