Glossary

A

• Active Learning A training method in which the model picks the unlabeled examples it is least sure about and asks a person to label them, so fewer labels teach it more. AI & Machine Learning
• Agentic Workflows Workflows in which an AI agent breaks a task into steps and carries them out with connected tools, checking each result before it moves on. Core Concepts
• AI Alignment The work of making an AI system pursue the goals its owners intend, within the values and limits they set for it. Security & Operations
• Air-Gapped Environment A computer network with no physical or wireless connection to the internet or to any other network that is not equally secured. Security & Operations
• API Gateway A single entry point in front of a set of APIs that checks each request against security and rate limits before passing it to the right service. Security & Operations
• API Integration A connection that lets two or more applications exchange data and trigger actions in each other through their APIs. Core Concepts

C

• Change Data Capture A way of recording each change made to a database as it happens, so other systems can act on the new data without reloading the whole database. Data Architecture
• CI/CD An automated pipeline that tests every code change as it is merged and releases the changes that pass, so software ships in small, frequent steps. Security & Operations
• Cloud Computing Computing resources such as servers and storage, rented over the internet from a provider that owns and runs the hardware and bills by use. Security & Operations
• Compute Gravity The tendency for processing to be placed beside large stores of data, because moving computation costs far less than moving terabytes across a network. AI & Machine Learning
• Compute Sovereignty Full ownership and legal control of the hardware where an organization's workloads run, including which jurisdiction governs it. Core Concepts
• Computer Vision The field of AI that lets software read images and video, from recognizing objects and faces to inspecting parts on a production line. AI & Machine Learning
• Confidential Computing Keeping data protected even during processing, by running the work inside a hardware-isolated enclave that the host system cannot read. Security & Operations
• Context Window The amount of text, counted in tokens, that a language model can take in and reason over at one time. Core Concepts
• Control Boundaries Enforced technical perimeters that protect sensitive data, workflows, identity, and compliance, built into infrastructure rather than bolted on as policy. Core Concepts
• Control Gravity Zynolabs' term for the pull that enterprise policies, standards, ownership, and observability must exert across every layer of a system. Core Concepts

D

• Data Anonymization Removing or masking the details in a dataset that could identify a person, so the data can be used without exposing who it describes. Security & Operations
• Data Drift A shift over time in the data a model sees in production, away from the data it was trained on, which can wear down its accuracy without raising any error. AI & Machine Learning
• Data Exfiltration The unauthorized movement of data out of an organization's systems, by an outside attacker or by someone already inside. Security & Operations
• Data Governance The policies and named owners that decide how an organization's data may be used and how its quality and security are maintained. Data Architecture
• Data Gravity The pull a large dataset exerts on the applications and services that use it, which end up running near it because moving the data is slow and costly. Data Architecture
• Data Lake A central store that keeps raw data of any kind in its original format until a team or a model needs it. Data Architecture
• Data Lake Architecture The design of a data lake's layers, from the zone where raw data lands to the processed and cataloged zones that serve it to analysts and models. Data Architecture
• Data Lakehouse A data platform that pairs a data lake's inexpensive file storage with the table structure and transactions of a data warehouse. Data Architecture
• Data Lineage A record of where a piece of data came from and every system and transformation it passed through on the way to a report or model. Data Architecture
• Data Mesh An approach that hands ownership of data to the business domains that produce it, with each domain publishing its data as a product for others to use. Data Architecture
• Data Mobilization Getting enterprise data out of its silos and into a form that AI systems and analytics can search and use. Data Architecture
• Data Pipeline A series of automated steps that carries data from the systems that create it to the places that use it, cleaning and reshaping it on the way. Data Architecture
• Data Silo Data held by one team or system that other parts of the organization cannot easily reach, often stored in a format no one else uses. Data Architecture
• Data Sovereignty The principle that data answers to the law of the place where it is stored, which limits where regulated organizations may keep and process it. Data Architecture
• Data Warehouse A central database that brings together cleaned, structured data from many systems so it can be queried for reporting and analysis. Data Architecture
• Deep Learning A form of machine learning built on neural networks with many layers, each layer learning more abstract patterns from the output of the one before. AI & Machine Learning
• DevSecOps Security checks that run alongside development and operations from the first line of code, so whoever ships the software shares responsibility for its security. Security & Operations
• Differential Privacy A mathematical guarantee that results computed from a dataset reveal almost nothing about any one person in it, achieved by adding calibrated noise. Security & Operations
• Digital Transformation Rebuilding how an organization works around digital systems and data, from its internal processes to the way it serves its customers. Core Concepts
• Disaster Recovery The plans and standby systems that bring an organization's technology back after a major failure or disaster, within agreed limits on downtime and data loss. Security & Operations

E

• Edge AI Running AI models on devices and local servers close to where the data is produced, such as the cameras and controllers on a production line. Data Architecture
• Edge Computing Processing data on or near the devices that produce it, which cuts the delay and network traffic of sending everything to a distant data center. Data Architecture
• Enterprise Architecture The overall design of how an organization's processes, data, applications and infrastructure fit together and support its business goals. Core Concepts
• Event-Driven Architecture A software design in which services communicate by publishing and reacting to events, such as an order being placed, as they happen. Data Architecture
• Explainable AI Methods that show why an AI model produced a given output, so people can check its reasoning before they rely on it. Security & Operations

F

• Federated Learning Training one shared model across many devices or sites that each keep their own data, sending only model updates to a central server. AI & Machine Learning
• Few-Shot Learning Getting a model to handle a new task from only a handful of examples, often given directly in the prompt. AI & Machine Learning
• Fine-Tuning Training an existing foundation model further on a smaller, specialized dataset so it performs better on a particular task or domain. AI & Machine Learning
• FinOps The practice of tracking and managing cloud and AI infrastructure spending, with engineering and finance teams sharing responsibility for the cost. Security & Operations
• Foundation Model A large model pretrained on a wide mix of data, which can then be adapted to many different tasks through fine-tuning or prompting. Core Concepts

G

• Generative Adversarial Network Two neural networks trained against each other, one producing synthetic data and the other judging whether it is real. AI & Machine Learning
• Generative AI AI models that create new text, images, code or audio when prompted, drawing on patterns learned from their training data. Core Concepts
• GPU Virtualization Splitting one physical GPU into isolated slices that several virtual machines or containers can use at the same time. Data Architecture
• Graph Database A database that stores data as nodes connected by relationships, so queries can follow those connections directly. Data Architecture
• Graph Neural Network A neural network designed to learn from data shaped as a graph, using the links between nodes as part of what it learns. AI & Machine Learning

H

• Hallucination When an AI model states something false or invented with the same confidence as a fact, often in fluent, plausible language. Core Concepts
• High Availability Designing a system with redundant parts and automatic failover so it stays up when a component fails, measured against an agreed uptime target. Security & Operations
• Homomorphic Encryption Encryption that lets a system compute on data while it stays encrypted, so the result is correct once decrypted and the data is never exposed. Security & Operations
• Human Approval Boundaries Explicit gates built into automated workflows marking where automation stops and a person decides, preserving human accountability in AI operations. Core Concepts
• Hyperparameter Tuning Searching for the training settings, such as learning rate and batch size, that give a model its best performance. AI & Machine Learning

I

• Inference Engine The part of an AI system that applies what it knows, whether logical rules or a trained model's weights, to new inputs to reach a conclusion. Security & Operations
• Infrastructure as Code Defining infrastructure such as servers and networks in version-controlled files, so every environment can be built the same way every time. Security & Operations

K

• Knowledge Graph A structured map of the things an organization cares about, such as customers or parts, and how they relate, which software can query and reason over. Core Concepts
• Kubernetes Open-source software that schedules containerized applications across a cluster of machines, restarting and scaling them as conditions change. Security & Operations

L

• Large Language Model LLM An AI model trained on a very large body of text that can read and write language, from answering questions to drafting whole documents. AI & Machine Learning
• Load Balancing Spreading incoming requests across several servers so no single one is overloaded and the service stays responsive. Security & Operations

M

• Machine Learning A branch of AI in which software learns patterns from examples and gets better at a task as it sees more data, with no hand-written rule for each case. Core Concepts
• Managed AI Operations The ongoing stewardship of a deployed private AI system: operation, governance, evaluation, and tuning that keep it accurate, safe, and auditable. Core Concepts
• Microservices Building an application as a set of small services that each do one job and can be deployed and scaled on their own. Data Architecture
• MLOps Machine Learning Operations The practices and tools for moving machine learning models into production and keeping them monitored and current once they are live. AI & Machine Learning
• Model Drift A decline in a model's accuracy over time, as the conditions it was trained on stop matching the ones it now sees. AI & Machine Learning
• Model Inference Running a trained model on new input to get a prediction or generated output, the stage where a model does its everyday work. AI & Machine Learning
• Model Inversion Attack An attack that queries a model and studies its outputs to rebuild sensitive details of the data it was trained on. Security & Operations
• Model Quantization Storing a model's weights at lower numerical precision, such as 8-bit integers, so it needs less memory and runs faster with little loss in quality. AI & Machine Learning
• Model Registry A central catalog of trained models that tracks every version, along with the metadata and approvals needed to move it into production. Security & Operations
• Multi-Modal AI AI that works across more than one kind of data at once, such as reading a document's text and its diagrams together. AI & Machine Learning

N

• Natural Language Processing The branch of AI that lets software work with human language, from reading and classifying text to translating and generating it. Core Concepts
• Natural Language Understanding The part of natural language processing that works out what a piece of text means, including what the writer intends by it. AI & Machine Learning
• Neural Network A model built from layers of connected nodes whose weighted links are adjusted during training until the network maps inputs to the right outputs. Core Concepts

O

• Observability How well a team can tell what is happening inside a running system from what it reports, such as its logs and metrics. Security & Operations
• On-Premises Software and hardware that run in an organization's own facilities, on equipment it owns and operates. Core Concepts

P

• Parameter-Efficient Fine-Tuning Ways of adapting a large model to a task by updating only a few added or selected parameters, such as LoRA adapters, while the rest stay frozen. AI & Machine Learning
• Path to Ownership Zynolabs' four-stage engagement that moves an enterprise from renting AI to owning it: Snapshot, Architecture, Buildout, and Managed AI Operations. Core Concepts
• Private AI AI systems that run inside an environment the organization controls, so its data and models stay under its own authority. Core Concepts
• Prompt Engineering Writing and testing the instructions given to a language model until it reliably returns the answer, in the format the task requires. Core Concepts
• Prompt Injection An attack that hides instructions in a model's input, such as a document it reads, to make it ignore its rules or take actions it should not. Security & Operations

R

• Reinforcement Learning Training an agent by trial and error, rewarding the actions that lead toward a goal so it learns which choices pay off. AI & Machine Learning
• Retrieval-Augmented Generation RAG Retrieval-augmented generation: relevant passages are first pulled from a trusted document collection, and the model writes its answer from them. Data Architecture
• Role-Based Access Control RBAC Granting access to systems and data by job role, so each person's permissions come from the role they hold. Security & Operations

S

• SecOps Running security and day-to-day IT operations as one joint effort, so watching for threats and responding to them is part of routine work. Security & Operations
• Secure Enterprise Boundaries The architectural perimeter within which private AI runs: ownership, access, identity, evidence, and control designed as one structure. Core Concepts
• Self-Attention The mechanism in transformer models that scores how much each word in a passage matters to every other word, so meaning is read in context. AI & Machine Learning
• Semantic Caching Reusing a stored model response when a new prompt means the same as an earlier one, even if it is worded differently. Data Architecture
• Semantic Search Search that matches the meaning of a query to the meaning of documents, so results can be relevant even when they share no keywords. AI & Machine Learning
• Service Mesh Infrastructure that manages the traffic between microservices, handling encryption and monitoring for every call so the application code does not have to. Security & Operations
• Shadow IT Apps and online services that staff adopt for their work without the approval or knowledge of the IT department. Core Concepts
• Sovereign AI AI capability an organization or nation holds entirely under its own control, so no outside provider can reprice it or switch it off. Core Concepts
• Supervised Learning Training a model on examples that are already labeled with the right answer, so it learns to predict labels for new data. AI & Machine Learning
• Synthetic Data Data generated by software to match the statistical patterns of a real dataset, without containing records about real people or events. Core Concepts

T

• Technical Drag The compounding operational friction and cost incurred by maintaining outdated systems or rushed, poorly architected solutions. Core Concepts
• Tensor Processing Unit A chip Google designed specifically to speed up the math of training and running neural networks. Data Architecture
• Tokenization Splitting text into tokens, the word pieces a language model reads and counts, before the model processes it. AI & Machine Learning
• Transfer Learning Reusing what a model learned on one task as the starting point for a related task, so the new task needs less data and training. AI & Machine Learning
• Transformer Architecture The neural network design behind most large language models, which uses self-attention to process every part of a sequence in parallel. AI & Machine Learning
• Triple Horizon Architecture Zynolabs’ proprietary framework for structuring digital transformation across Business, Operations, and Infrastructure horizons. Core Concepts

U

• Unsupervised Learning Machine learning on data with no labels, where the model finds structure on its own, such as clusters of similar records. AI & Machine Learning

V

• Vector Database A database built to store embeddings, the numeric fingerprints of text or images, and to find the ones closest in meaning to a query. Data Architecture
• Vendor Lock-In Dependence on a single provider that makes leaving too costly, because your data and the workflows built on it sit under that provider's control. Core Concepts

Z

• Zero Trust Architecture A security model that trusts no user or device by default, checking identity and permission on every request to every resource. Security & Operations