AI Cyber Glossary

April 2026

A living reference establishing consistent, governance-ready definitions for artificial intelligence terminology across the health sector.

The glossary was developed in direct response to a critical gap in managing healthcare AI and AI cybersecurity: the absence of shared, sector-specific language that clinical, operational, compliance, and technical stakeholders can use with confidence. As AI adoption accelerates across healthcare organizations of every size, inconsistent terminology creates real risk - in procurement decisions, vendor contracts, regulatory submissions, policy development, and patient safety oversight. As a living document the Glossary is designed to serve as the terminological foundation for all current and future HSCC AI Task Group guidance materials.

Term	Acronym	Audience	Basic Definition	Advanced Definition	Context
Agentic AI		General	AI that makes decisions or performs tasks without needing human input, for example Agentic AI. It doesn’t just wait for you to tell it what to do - it’s designed to act on its own to achieve goals. It makes decisions, chooses actions, and keeps working toward a result without someone needing to guide it step-by-step the whole way.		Think of Agentic AI like a really sharp practice manager. You tell them, “We need to improve patient no-show rates.” They don’t wait for you to tell them exactly how — they analyze schedules, tweak reminders, adjust workflows, and report back on results. Regular AI is like a scheduler who only sends a reminder when you say, “Send this reminder now.” Agentic AI figures out the how and acts.
AI Governance		General	The establishment of clear policies, defined roles, and processes to ensure AI is used ethically, safely, and effectively.		The set of rules your practice creates for how to choose, use, and monitor AI tools to protect patients and staff.
AI Washing		General	The practice of companies exaggerating or misrepresenting their AI products or services to gain a competitive advantage
Algorithm		General	Step-by-step instructions a computer follows to solve problems. Often used to refer to the mechanism by which an AI performs a task.
Artificial Intelligence	AI	General	Systems or machines that mimic human intelligence (e.g., learning, decision-making) to perform tasks and can progressively improve themselves based on the information they collect.	A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.	An incredibly smart and fast assistant that can process information and recognize patterns, but still needs human guidance and oversight.
Artificial Intelligence Performance Monitoring	AI Performance Monitoring	General	Regularly checking how well an AI is working and whether it needs to be adjusted.	The process of regularly collecting and analyzing data on the use of a deployed AI system to evaluate its performance in accomplishing its intended tasks in real-world settings. This monitoring aims to assess performance, detect degradation or changes (e.g., due to data drift), identify misuse, and address safety or usability concerns.
Artificial Intelligence System	AI System	General	A machine that can learn, make decisions, or act in ways that seem smart.	An engineered system that generates outputs such as content, predictions, recommendations, or decisions for a given set of human-defined objectives. AI systems are designed to operate with varying levels of autonomy.
Assistive Artificial Intelligence	Assistive AI	General	AI that helps people do their work better or faster, not replace them.	AI systems designed to assist human users in performing tasks, often by providing recommendations or augmenting human decision-making, without replacing human judgment.
Autonomous Artificial Intelligence	AutonomousAI	General	AI that can make decisions and take actions without needing help from a person.	AI systems capable of performing tasks without human intervention, making decisions or taking actions independently based on their programming and data inputs.
Autonomy Levels		General	A tiered framework describing how much control an AI system has over decisions and actions. Ranges from Level 0 (no automation) to Level 5 (full autonomy without human input). Important for understanding risk and governance implications in clinical and operational AI deployments. See also Autonomous AI
Bias	in AI	General	When AI results are unfair due to errors in data or design.		An AI diagnostic tool trained mostly on data from one demographic group may be less accurate for patients from other groups.
Business Associate Agreement	BAA	General	A mandatory contract under HIPAA that a vendor must sign if they handle PHI on behalf of a healthcare provider.		A non-negotiable contract you must have with any AI vendor that will touch your patient data.
Chain-of-Thought Modeling		Technical		A technique in AI models, particularly large language models, that enables the model to generate intermediate reasoning steps or explanations as part of its output to improve transparency, logic validation, and safety oversight.
Clinical Oversight		General	(AI Context): The governance of clinical activities or processes that involve or are influenced by artificial intelligence. It ensures that AI supports clinical effectiveness, patient safety, and compliance with medical standards.
Computer Vision	CV	General	A field of AI that trains computers to interpret and understand the visual world from images and videos.		Self-driving cars use computer vision to identify pedestrians, traffic lights, and other vehicles on the road.
Consent		General	Permission a person gives for AI to use their information — and in many cases, this permission is required by law before AI can be used.	A process by which individuals authorize the use of their personal or health information, with clear communication of purpose, risks, rights, and the role of AI. Increasingly, state laws are requiring explicit consent before AI can be used in certain elements of care or administrative functions (e.g., clinical decision support, transcription, or patient interaction tools). Consent ensures patients or users understand when AI is being used and agree to that use, aligning with privacy, ethical, and regulatory obligations.	Like signing a HIPAA form before your doctor can share your medical records—except now, you may also need to sign or acknowledge when AI is used to assist in care or manage records.
Continual Machine Learning		General	A type of AI that keeps learning and improving from new data over time.	A machine learning approach where models are designed to learn continuously from new data, allowing them to adapt to changes over time without forgetting previously learned information.
Convolutional Neural Network	CNN	Technical		A type of deep learning model particularly effective for analyzing visual data. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.
Cross-Border Data Control		General	Making sure data that moves between countries is handled safely and legally.	Policies and technical controls that govern the transmission, processing, and storage of data across international boundaries, ensuring compliance with regional laws, privacy regulations, and security standards.
Data Card		General	A structured summary providing essential information about a dataset, including its composition, intended use, limitations, and ethical considerations, to promote transparency and responsible use.
Data Drift		General	The change in data distribution over time, which can affect the performance of machine learning models if they are not updated to accommodate the new data patterns.
Data Labeling		Technical	The process of tagging data with information so an AI can learn from it.		Like marking X-rays to show where a tumor is.
Data Minimization		General	Only using the data that is needed for the AI task.		Like asking only the necessary patient questions for a diagnosis.
Data Mining		General	The process of discovering patterns, trends, and insights from large datasets.		Retail companies use data mining to analyze customer purchasing habits and recommend products.
Data Poisoning		Technical	A cyberattack where an attacker intentionally feeds "bad" data into an AI's training set to make it unreliable or biased.		A sophisticated attack that could corrupt a diagnostic AI model, causing it to make mistakes.
Data Privacy		General	Protecting sensitive information, particularly patient information, used in AI systems.
Deep Learning		General	A type of machine learning where AI uses layered networks (like digital neurons) to learn patterns in large amounts of data. It’s how AI can recognize faces, translate languages, or generate text.	A subset of machine learning involving neural networks with multiple layers (deep neural networks) that can learn complex patterns in large amounts of data.	Think of it as giving AI a brain that learns by seeing lots of examples.
Deep Research		General	An in-depth, focused approach to learning or problem-solving that goes beyond surface-level searching. In AI projects, this might mean carefully exploring complex topics, risks, or user needs before building or deciding.		It’s not just Googling — it’s diving into the full context, evidence, and implications.
De-identification		General	The process of removing personal identifiers from data to protect individual privacy.		A method used to prepare patient data for training AI models, though it doesn't always eliminate the risk of re-identification.
Digital Health Technology	DHT	General	Technologies such as mobile health apps, wearable devices, telemedicine, and health information technology systems that use computing platforms, connectivity, software, and sensors for health care and related uses.
Digital Twin		Technical		A virtual representation of a physical object or system that can be used to simulate, predict, and optimize performance in real-time, often used in healthcare to model patient-specific conditions.
End Of Life	EOL	General	Life cycle stage of a product, starting when (1) the manufacturer no longer sells the product beyond its useful life (as defined by the manufacturer), and (2) the product has gone through a formal EOL process, including notification to users.
Ensemble Methods		Technical		Machine learning techniques that combine predictions from multiple models to improve overall performance, robustness, and accuracy compared to individual models.
Ethical AI		General	The practice of designing and using AI in a way that aligns with moral values and societal good.		An ethical AI principle would be to ensure that an AI system used in healthcare does not perpetuate existing biases against certain patient populations.
Explainability	XAI	General	Being able to understand and explain how an AI system makes its decisions in a way that humans can follow.	The extent to which the internal mechanics of a machine learning system can be explained in human terms, enabling users to understand, trust, and effectively manage AI systems.	It answers the "why" question. Why did the AI flag this specific area on the X-ray as potentially problematic?
Explainable AI	XAI	General	A set of processes and methods that allows human users to understand and trust the results and output created by machine learning algorithms.		An XAI system in finance could provide a clear explanation for why a loan application was denied, rather than just giving a "yes" or "no" answer.
Feature Engineering		General	The process of selecting, modifying, or creating new input features from raw data to improve the performance of machine learning models.
Federated Learning		General	A machine learning approach where models are trained across multiple decentralized devices or servers holding local data samples, without exchanging them, enhancing data privacy.
Fine-tuning		General	Adjusting a pre-trained model with specific data to make it more accurate for a given task.
Foundation Models		Technical		Large-scale machine learning models trained on vast amounts of data that can be adapted to a wide range of tasks, often serving as a base for more specialized models.
Generative Adversarial Network	GAN	General	A class of machine learning frameworks where two neural networks (a generator and a discriminator) contest with each other to produce data indistinguishable from real data.
Generative AI		General	AI that creates new content such as text, images, and video based on input and learned patterns.		Using a tool like DALL-E to generate an image of "an astronaut riding a horse on the moon" by providing a text prompt.
Generative Artificial Intelligence	Generative AI	General	AI that creates new content such as text, images, and video based on input and learned patterns.	AI systems capable of generating new content, such as text, images, or audio, that resembles human-created data, often using models like GANs or transformer-based architectures.	It’s like a chef who has learned thousands of recipes. When you ask for a dish, they don’t just repeat one recipe—they combine what they know to create something new that fits what you asked for.
Guardrails		General	The policies, procedures, and technical controls put in place to ensure that AI systems operate within ethical and legal boundaries and do not cause harm.		A guardrail for a generative AI model could be a filter to prevent it from generating hateful or violent content.
Hallucination		General	When AI generates false or misleading information.		Hallucination in AI is like a patient giving you made-up medical history with total confidence — sounds convincing, but none of it checks out.
HHS 405d HICP	HICP	General	Health Industry Cybersecurity Practices. A set of voluntary, consensus-based cybersecurity guidelines from HHS to help healthcare organizations.		The government's recommended "playbook" for cybersecurity that is highly relevant for protecting AI tools.
HIPAA		General	Health Insurance Portability and Accountability Act. The U.S. federal law setting national standards for protecting patient health information.		The foundational privacy and security law that governs how all patient data, including data used by AI, must be handled.
Human in the Loop Machine Learning		General	A model training approach that involves human feedback in the learning process, allowing for corrections, guidance, and improvements to the AI system.
Internet of Things Device	IoT	General	Physical devices embedded with sensors, software, and connectivity to collect and exchange data over the internet, often used in healthcare for monitoring and diagnostics.
Interoperability		General	The ability of different information systems, devices, or applications to connect, exchange, and use data cohesively and effectively.
Interpretability		General	The degree to which a human can understand the cause of a decision made by a machine learning model, often essential for trust and regulatory compliance.
Kill Switch		General	A tool that can quickly shut down an AI system to stop harm or prevent risk.	A fail-safe mechanism designed to immediately disable or isolate an AI system in the event of malfunction, attack, or unintended behavior, helping to prevent further harm or data compromise.
Large Language Model	LLM	General	AI trained on vast amounts of text to understand and generate language.	A type of AI model trained on extensive text data capable of understanding and generating human-like language, used in applications like chatbots and language translation.	OpenAI's GPT-4 and Google's Gemini are examples of LLMs that power various AI applications.
Locked Model		Technical		A machine learning model that, once trained and validated, is fixed and does not change its parameters or behavior over time, ensuring consistent performance.
Machine Learning	ML	General	AI that learns patterns from data instead of being directly programmed.	A subset of AI involving algorithms that improve automatically through experience by learning patterns from data without being explicitly programmed.	Like a medical student who learns by studying many patient cases (data) to recognize symptoms and predict outcomes for new patients.
Machine Learning Algorithm	ML Algorithm	General	A specific procedure or formula for solving a problem, used in ML to learn from data and make predictions or decisions.
Machine Learning Algorithmic Bias	ML Algorithmic Bias	General	Systematic errors in ML models that result in unfair outcomes, often due to biased training data or flawed model assumptions.
Machine Learning Model	ML Model	Technical		The output of a machine learning algorithm trained on data, used to make predictions or decisions without being explicitly programmed for the task.
Membership Inference		Technical	An attack where a malicious actor tries to determine whether a specific individual's data was part of a model's training set. This is a privacy threat.		An attacker queries a hospital's diagnostic AI with a specific patient's data to infer if that person's records were used in training, thus revealing they were a patient.
Model		General	A trained mathematical representation used by an AI system to make decisions or predictions.
Model Calibration		Technical		The process of adjusting a model's output to align predicted probabilities with actual outcomes, improving the reliability of predictions.
Model Card		Technical		A documentation framework providing details about a machine learning model's intended use, performance, limitations, and ethical considerations to promote transparency.
Model Drift		Technical	The degradation of an AI model's performance over time as new data differs from the data it was trained on.		A diagnostic tool trained on pre-2020 data might become less accurate when analyzing data from a post-pandemic world.
Model Fitting		Technical		The process of training a machine learning model on data so that it can learn the underlying patterns and make accurate predictions.
Model Inversion / Extraction		Technical	An attack that attempts to reconstruct the training data by exploiting the model's outputs. This can expose sensitive information used to train the model.		Trying to reverse-engineer an AI model to "guess" the specific patient data records that were used to build it.
Model Robustness		Technical		The ability of a machine learning model to maintain performance when exposed to new, noisy, or adversarial data.
Model Validation		Technical	Testing that an AI model works correctly and safely before using it.		Like validating that lab equipment works before running patient tests.
Model Weight		Technical		Parameters within a machine learning model that are learned from the training data and determine the influence of input features on the output.
Multimodal		General	Involving multiple types of data inputs (e.g., text, images, audio) to improve the performance and accuracy of AI systems.
Natural Language Processing	NLP	General	AI that enables computers to understand, interpret, and generate human language (spoken or written).	A field of AI focused on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language.	A "medical language translator" or super-efficient scribe that can read, understand, and summarize doctors' notes or patient emails.
Neural Network		General	A computer system modeled on the human brain, consisting of interconnected nodes (or "neurons") that process information.	A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.	Neural networks are used in a wide range of applications, from weather forecasting to medical diagnosis.
Overfitting		Technical	When a model performs well on training data but poorly on new, unseen data.	A modeling error in machine learning where a model learns the training data too well, including its noise and outliers, leading to poor generalization to new data.
Performance Metrics		General	Quantitative measures used to evaluate the performance of a machine learning model, such as accuracy, precision, recall, and F1 score.
Pipeline		Technical	A series of automated steps (preprocessing, inference, postprocessing) to handle inputs and outputs in an AI system.
Privacy Risk Assessment		General	A review of how data could be misused or exposed when using AI.		Like a HIPAA risk analysis, but focused on AI systems.
Privacy-Enhancing Technology		Technical		Tools and methods designed to protect personal data privacy while allowing data to be used for analysis and machine learning.
Prompt		General	The input or instruction given to an AI model to get a response.		Giving a generative AI the prompt, "Write a short poem about a rainy day."
Prompt Injection		General	Tricking an AI system by adding hidden or misleading instructions in its input.	A type of adversarial input attack where an attacker manipulates the input prompt to override, bypass, or manipulate the intended behavior of an AI model—especially large language models—resulting in unauthorized actions or data leakage.	Like writing hidden instructions in a medical chart that cause the system to give the wrong treatment advice.
Protected Health Information	PHI	General	Any individually identifiable health information.		The patient data (names, diagnoses, images) that AI systems may process and that HIPAA is designed to protect.
Reference Standard	in Artificial Intelligence	General	A benchmark or set of criteria used to evaluate the performance of AI models, often representing the best available method or consensus.
Reinforcement Learning		General	A type of machine learning where an AI agent learns to make decisions by performing actions and receiving rewards or penalties.		AI in a game learns to play by being rewarded for good moves and penalized for bad ones.
Responsible AI		General	The practice of designing, developing, and deploying AI with good intention to empower employees and businesses, and fairly impact customers and society.		A company practicing responsible AI would conduct a thorough impact assessment before deploying a new AI system to understand and mitigate potential negative consequences.
Reward Hacking		Technical		A phenomenon in reinforcement learning where an AI system finds unintended ways to maximize its reward signal, often by exploiting loopholes or flaws in its training environment, leading to unsafe or undesired outcomes.
Sector Mapping and Risk Template	SMART Map	Technical		A methodology for mapping clinical and business processes that involve AI to evaluate safety, effectiveness, and governance alignment.
Secure-by-Design		General	Designing AI systems with security built in from the start.	A development approach that integrates cybersecurity controls and risk mitigation strategies across all stages of the AI lifecycle, ensuring that security is proactively embedded into the design, implementation, deployment, and maintenance of AI-enabled systems.
Self-Supervised Machine Learning		Technical		A learning approach where the model learns from the structure of the data itself without explicit labels, often by predicting parts of the data from other parts.
Semi-Supervised Machine Learning		Technical		A machine learning approach that uses a small amount of labeled data and a large amount of unlabeled data to improve learning accuracy.
Software Bill of Materials	SBOM	General	A list of all components in a software product. For AI, it can help in understanding dependencies and security vulnerabilities.
Software Development Life Cycle	SDLC	Technical		A structured process used by development teams to produce high-quality software. In the AI context, it includes secure coding, testing, and validation steps relevant to AI systems.
Supervised Machine Learning		General	A type of machine learning where the model is trained on a labeled dataset, meaning each data point is tagged with the correct output. Also known as Supervised Learning	A type of machine learning where the model is trained on labeled data, learning to predict the output from the input data.
Synthetic Data		General		Artificially generated data that mimics real data, used to train machine learning models while preserving privacy and augmenting datasets.
Test Data		General		A subset of data used to assess the performance of a trained machine learning model, separate from the data used for training.
Testbed		General		A controlled environment used for testing and evaluating the performance of AI systems and technologies.
Threat Modeling		General	A way to figure out what could go wrong with an AI system and how to prevent it.	A structured process used to identify, assess, and prioritize potential threats and vulnerabilities in AI systems across their lifecycle. Threat modeling helps teams anticipate attack vectors, assess risk impact, and design mitigation strategies—especially important in adaptive or opaque AI models.
Token		Technical	A small piece of text that AI uses to understand and process language. It might be a word, part of a word, or even just a character. AI breaks sentences into these smaller chunks called tokens to figure out what you're trying to say.	In AI, especially in Natural Language Processing (NLP) models like GPT, a token is a unit of text used in model computations. Depending on the tokenizer used, a token may represent a word, a subword, or even a single character. For example, GPT models typically use byte pair encoding (BPE) or a variant like tiktoken, where common words are a single token, but uncommon or long words are broken into multiple subword tokens.	The sentence “I love tacos!” might be split into tokens like: “I”, “love”, “tacos”, “!”.
Training Data		General	The data used to teach an AI model how to perform its task. Think of it like examples the model learns from.		A self-driving car's training data would include millions of miles of driving footage.
Transfer Learning		Technical		The model processes input as sequences of tokens, and the cost of using the model (both in compute and pricing) is often measured in the number of tokens. Each token also maps to a vector in the model's embedding space for processing.
Transparency		General	Making it clear how an AI model works, what data it was trained on (when possible), and how decisions are made.		The vendor should be able to clearly tell you what the AI can and can't do, and what data it was built on.
Trustworthiness		General	(AI Context): The degree to which an AI system is reliable, transparent, fair, secure, and aligned with regulatory and ethical expectations. Often linked to risk assessments and governance structures.
Trustworthy AI Bill of Materials	TAI BOM	Technical		A conceptual inventory of elements required to ensure AI trustworthiness, such as data sources, model training details, and risk mitigation strategies.
Tuning Data		General	Data used to fine-tune the parameters of a machine learning model to optimize performance.
Underfitting		General	A modeling error where a machine learning model is too simple to capture the underlying structure of the data, leading to poor performance.
Unsupervised Machine Learning		General	A type of machine learning where the model works with an unlabeled dataset and must find patterns and relationships on its own. Also known as Unsupervised Learning.	A type of machine learning where the model learns patterns from unlabeled data without specific outputs to guide the learning process.
Vendor Vetting		General	The process of thoroughly investigating a third-party vendor before entering into a business relationship.		Using a checklist to ask an AI company tough questions about their security, ethics, and compliance before you buy their product.
Watermarking		General	A technique used to embed information into data or models to protect intellectual property and detect unauthorized use.
Zero Knowledge Attack		Technical		A type of AI-enabled exploit where an attacker gains insights or influences outcomes without having prior knowledge of the underlying data or model, often using indirect signals or prompt patterns to trigger malicious behavior.