Glossary
The language of AI governance.
Plain definitions of the terms behind enforced AI - and how each one works in practice.
A working vocabulary for teams shipping AI under scrutiny: what each term means, why it matters, and where it shows up in the control layer.
Governance
AI Governance
The practice of directing and controlling how AI systems are built and used - setting policies, enforcing them at runtime, and proving compliance with evidence.
Control Layer
A dedicated layer in the AI stack that inspects, authorizes, and records every model, agent, and tool interaction - the way identity or TLS sits as infrastructure.
AI Policy (Policy-as-Code)
A machine-readable rule that defines what an AI system may or may not do, expressed as code so it can be versioned, tested, and enforced automatically.
Shadow Mode
Running a new policy against live or replayed traffic without enforcing it, so you can measure its impact before it can block anything.
Audit Ledger
A tamper-evident, queryable record of every AI decision - what was requested, which policy applied, and what was allowed, blocked, or redacted.
Enforcement & Security
AI Guardrails
Controls that constrain what a model or agent can receive or produce at runtime - blocking unsafe inputs and outputs as they happen.
Runtime Policy Enforcement
Applying governance rules inline on every live request, so a policy decision actually shapes the response instead of just being reported.
Adjudication
Deciding the outcome of a governed request by combining fast deterministic checks with model-based judgment only when needed.
Prompt Injection
An attack where malicious instructions hidden in input get a model to ignore its rules - the AI equivalent of an injection vulnerability.
LLM Firewall
A runtime filter for traffic to and from large language models - inspecting prompts and responses and enforcing policy at the boundary.
Zero-Trust AI
Applying zero-trust principles to AI - never trusting a model, agent, or tool call by default, and authorizing every action explicitly.
Agents & MCP
AI Agent
An AI system that can take actions toward a goal - calling tools, APIs, and other systems - not just generating text.
Model Context Protocol (MCP)
An open standard that lets AI models connect to external tools and data sources through a common interface.
MCP Gateway
A control point for Model Context Protocol traffic - an approved server registry, scoped credentials, and policy checks on every tool call.
Tool Call
A request from a model or agent to run an external function - a search, a database query, an API action - and return the result.
Least Privilege
Granting an AI agent only the minimum access it needs for a task, so a compromise or mistake has limited reach.
Blast Radius
The maximum damage an AI action could cause if it goes wrong - the scope a single mistake or attack can reach.
Data
PII Redaction
Detecting and removing or masking personally identifiable information before it reaches a model or leaves your perimeter.
Data Egress
Data leaving your controlled environment - in AI, the sensitive content that flows out in a prompt to an external model.
Synthetic Data
Artificially generated data that mirrors the statistical shape of real data without containing real records.
Retrieval-Augmented Generation (RAG)
A technique that retrieves relevant documents at query time and feeds them to a model so its answer is grounded in your data.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.