Casual Guide to ServiceNow AI Terminology for GRC (Risk)

[Mary Hain]

Below is a non-exhaustive list of popular AI terms relevant to GRC/Risk and AI in general. You can add any missing terms and definitions in the comments. 

Agent: Autonomous entity with agency and decision-making capabilities

Agentic AI: Type of artificial intelligence (AI) that is designed to act autonomously, with a high degree of agency and self-determination

AI Asset Inventory: Centralized repository of all AI-related assets, including models, datasets, and algorithms within an organization so it can track, manage, and govern its AI assets

AI Asset Lifecycle: Process of managing the entire lifecycle of an AI asset, from creation to retirement

AI Control Deduplication: Identifies and removes redundant control objectives and proposes a new common control objective (Now Assist)

AI Control Tower: Centralized dashboard for monitoring and managing AI-related activities, including model performance, data quality, and user adoption

AI Issue Submission Agent: AI-powered, guided conversational experience for employees during the issue submission process (agentic AI)

AI Issue Summarization: Generates a clear, structured summary of the issue ​for the issue record, helping teams resolve concerns faster (Now Assist)

AI Gateway: Supports AI agent interoperability and governance for workflows that cross multiple platforms that use Model Context Protocol (MCP) and A2A

AI Response Assist: Identifies similar past questions from past assessments, then uses this data to auto-fill an assessment, including sources (agentic AI)

AI Risk Identification: Conversational AI agent that auto-pulls entity context; guides users through risk domain selection; then surfaces relevant risks from internal, industry, and external sources into one consolidated list (agentic AI)

AI Search: A feature that uses natural language processing (NLP) to provide more accurate search results

AI Smart Documents: Conversational experience for generating document summaries, insights, FAQs, and more (Now Assist)

AI Voice Assist For Documents: AI-generated voice summary and Q&A using voice commands (Now Assist)

AI-Powered Virtual Agent: A virtual agent that uses AI to provide automated customer service and support

Anomaly Detection: A feature that uses machine learning to detect unusual patterns in data

Artificial Intelligence (AI): The development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making

Attention Mechanisms: A type of deep learning model that involves focusing on specific parts of the input data to make predictions

Chatbots: A type of AI-powered virtual agent that uses NLP to interact with users

Classification: A feature that uses machine learning to categorize data into predefined categories

Clustering: A feature that uses machine learning to group similar data points together

Computer Vision: A subset of AI that involves enabling computers to interpret and understand visual information from the world

Control Rationalization: Identifies and removes redundant control objectives, ensuring a cleaner, more efficient compliance framework in GRC scenarios (Now Assist)

Conversational AI: A type of AI that uses NLP to enable human-like conversations

Convolutional Neural Networks (CNNs): A type of deep learning model that involves processing image or video data

Decision Intelligence: A feature that uses machine learning to provide insights and recommendations to support decision-making

Deep Learning (DL): A subset of machine learning that uses neural networks with multiple layers to learn from data

Enterprise AI Discovery:  Uses machine learning and natural language processing to automatically discover and identify AI-related assets, such as models, datasets, and algorithms, within an organization so it can identify and catalog AI-related assets

Entity Extraction: A feature that uses NLP to extract specific entities from text data, such as names, locations, and organizations

Expert Systems: AI systems that use knowledge and rules to make decisions or solve problems in a specific domain

Few-Shot Learning: A type of machine learning that involves training models to learn from a small number of examples

Gen AI: Artificial intelligence that generates new content, such as text, images, or audio (Now Assist)

Generative Adversarial Networks (GANs): A type of deep learning model that involves two neural networks competing with each other to generate new data

Intelligent Automation: A type of AI that uses machine learning to automate business processes

Knowledge Graphs: A data structure that represents knowledge as a graph of nodes and edges, used to reason and infer new knowledge

Machine Learning (ML): A subset of AI that involves training algorithms to learn from data, without being explicitly programmed

Meta-Learning: A type of machine learning that involves training models to learn how to learn

Model Context Protocol (MCP): A protocol for managing and exchanging context information between AI models and applications to ensure accurate and reliable model performance.

Natural Language Processing (NLP): A subset of AI that involves enabling computers to understand, interpret, and generate human language

Neural Turing Machines (NTMs): A type of deep learning model that involves using neural networks to control a Turing machine

Now Assist: AI-powered virtual agent that uses natural language processing (NLP) to provide personalized, automated support and guidance to help customers and agents resolve specific issues quickly and efficiently

One-Shot Learning: A type of machine learning that involves training models to learn from a single example

Policy mapping​: Uses AI to recommend policies to be mapped to regulatory alerts, reducing time and effort of the manual task (Now Assist)

Predictive Intelligence: A feature that uses machine learning to predict future outcomes and provide recommendations

Process Mining: A feature that uses machine learning to analyze and optimize business processes

Recommendation Engine: A feature that uses machine learning to provide personalized recommendations

Recurrent Neural Networks (RNNs): A type of deep learning model that involves processing sequential data, such as text or speech

Regression: A feature that uses machine learning to predict continuous values

Regulatory Action Plan Generator: Analyzes regulatory alert context and impacted areas to generate AI-driven regulatory action plans based on historical alerts and prior implementations  (agentic AI)

Regulatory Alert Analysis: Automatically analyzes regulatory alerts and augments each regulatory alert with AI-curated information from trusted web and regulatory sources for context (agentic AI)

Reinforcement Learning: A type of machine learning that involves training agents to take actions in an environment to maximize a reward signal

Risk Assessment Summarization: Automatically summarizes risk assessments so teams can quickly grasp the nature, impact, and context of risks without manual deep dives (Now Assist)

Robotic Process Automation (RPA): A type of AI that uses machine learning to automate repetitive tasks

Robotics: A field of AI that involves designing and building robots that can interact with their environment

Sentiment Analysis: A feature that uses NLP to analyze and determine the sentiment of text data

ServiceNow AI Platform: Cloud-based platform that provides a comprehensive set of AI-powered solutions using machine learning and natural language processing to automate processes, improve customer experiences, and gain data insights. AI-powered features include virtual agents, chatbots, and predictive analytics.

Skill: A specific ability or capability of an AI system to perform a task

Text Analytics: A feature that uses NLP to analyze and extract insights from text data

Topic Modeling: A feature that uses NLP to identify underlying topics in text data

Transfer Learning: A type of machine learning that involves transferring knowledge from one task to another, often using pre-trained models

Workflow: A sequence of automated processes and tasks that an AI system performs to achieve a specific goal or objective, often involving data ingestion, processing, analysis, and output generation

Zero-Shot Learning: A type of machine learning that involves training models to learn from no examples at all

Sources:  ServiceNow, IEEE, AAAI

Resources: ServiceNow Responsible AI White Paper

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