These terms cover a broad spectrum of generative AI and cybersecurity concepts, making them a useful reference for understanding both fields.
Adversarial Attack: Attempting to deceive or manipulate a GenAI model to cause it to make incorrect predictions or outputs.
AI Alignment: Ensuring that the objectives of AI models align with human values and expectations.
API (Application Programming Interface): A tool that allows developers to integrate GenAI capabilities into other applications.Attention Mechanism: A method that allows models to focus on specific parts of the input data.
Behavioral Analytics: Use of AI to analyze user behavior to detect irregular activities.
Bot Mitigation: The use of AI to identify and manage bot traffic, distinguishing between malicious bots and legitimate users.
Context Window: The amount of text that a language model can process at once.
Creativity/Originality Parameter: A setting in generative models that adjusts how novel or diverse the generated output is.
Data Augmentation: Techniques to create more training data by modifying existing data.
Data Poisoning: A type of adversarial attack where attackers manipulate the data used to train AI models.
Deep Learning: A subset of machine learning involving neural networks with many layers.
Diffusion Model: A class of generative models that iteratively improve data by reducing noise.
Embedding: A numerical representation of words, sentences, or other data forms.
Ethical AI: The study and implementation of responsible practices in AI.
Ethical Considerations: Topics around privacy, bias, misinformation, and the responsible use of GenAI.
Explainable AI (XAI): AI systems designed to provide understandable reasons behind their predictions.
False Positive/Negative Rate: Metrics in AI to evaluate the accuracy of threat detection models.
Federated Learning: AI approach where models are trained across decentralized devices or servers.
Few-Shot Learning: Training AI with very few examples to accomplish a task.
Fine-Tuning: The process of adapting a pre-trained model on a specific dataset or task.
GAN (Generative Adversarial Network): A type of generative model that consists of two competing networks.
Generative Model: A type of AI model designed to generate new data that resembles a training dataset.
GPT (Generative Pre-trained Transformer): A family of large language models developed by OpenAI.
Hallucination: When a GenAI model generates incorrect or fictitious information.
Hyperparameter: The settings configured before training a model, such as learning rate and batch size.
Inpainting: The ability of image generation models to fill in missing parts of an image.
Large Language Model (LLM): A generative AI model with billions (or trillions) of parameters.
Latent Space: The mathematical space where generative models represent data features.
Malware Classification: AI used to categorize and detect malware.
Machine Learning (ML): A branch of AI that enables systems to learn and improve from experience.
Multi-Modality: The capability of a model to handle and generate data across different types, like text, images, and audio.
Natural Language Processing (NLP): AI technology that helps systems understand human language.
Neural Network: The structure that underlies GenAI models.
Network Traffic Analysis (NTA): The use of AI to analyze network traffic data.
One-Shot Learning: Training AI with very few examples to accomplish a task.
Parameter: Numerical values within a neural network that influence the model's output.
Phishing Detection: AI systems designed to identify and block phishing attempts.
Predictive Analytics: AI technique that uses historical data to predict potential future security incidents.
Prompt: The input given to a GenAI model to guide its output.
Prompt Engineering: The process of crafting specific input prompts to achieve desired responses from GenAI models.
Reinforcement Learning from Human Feedback (RLHF): A method of fine-tuning models by using feedback from humans.
Self-Supervised Learning: A training method where models learn from unlabeled data.
Style Transfer: A technique that allows generative models to create new content by blending elements of one style with another.
Temperature: A hyperparameter that controls the randomness of model outputs.
Token Limit: The maximum number of tokens (words or subwords) that a model can process in one input or response.
Tokenization: The process of breaking down text into smaller units.
Transformer: A deep learning architecture that excels at understanding context in sequences.
UEBA (User and Entity Behavior Analytics): AI-driven technology that monitors users' and entities' behavior.
Zero-Shot Learning: When an AI model performs a task without having been explicitly trained on similar examples.