Risks and Threats of AI in Brand Protection
Expert-defined terms from the Advanced Certificate in AI Brand Protection Strategies course at Stanmore School of Business. Free to read, free to share, paired with a globally recognised certification pathway.
Adversarial Examples #
inputs to machine learning models designed to cause the model to make a mistake; these can be used to test the robustness of AI systems or to maliciously manipulate them.
Artificial Intelligence (AI) #
the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
Brand Protection #
the practice of protecting a brand's reputation and value by monitoring and enforcing its intellectual property rights, preventing counterfeiting and grey market activities, and ensuring the quality and safety of products and services sold under the brand.
Deepfakes #
manipulated audio or video content that uses AI techniques to create realistic-looking or sounding fake media; these can be used for malicious purposes such as spreading misinformation or impersonating individuals.
Generative Adversarial Networks (GANs) #
a type of AI model that consists of two parts: a generator, which creates new data, and a discriminator, which tries to distinguish between real and fake data. GANs can be used to create realistic-looking images, videos, and audio.
Intellectual Property (IP) #
creations of the mind, such as inventions, literary and artistic works, symbols, names, images, and designs, that are protected by law through patents, copyrights, trademarks, and related rights.
Malware #
software designed to harm or exploit a computer system, network, or individual; this can include viruses, worms, trojans, ransomware, and spyware.
Natural Language Processing (NLP) #
a field of AI that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language in a valuable way.
Phishing #
a cybercrime in which a target is contacted by email, telephone, or text message by someone posing as a legitimate institution to lure individuals into providing sensitive data such as personally identifiable information, banking and credit card details, and passwords.
Ransomware #
a type of malware that encrypts a victim's files and demands a ransom to restore access to the data; this can be used to extort money from individuals or organizations.
Robotic Process Automation (RPA) #
the use of software robots or "bots" to automate routine tasks and processes, freeing up human workers to focus on higher-value activities.
Spear Phishing #
a targeted form of phishing attack that is tailored to a specific individual or organization, often using personalized information to increase the likelihood of success.
Supervised Learning #
a type of machine learning in which the model is trained on a labeled dataset, where the correct output is provided for each input. The model learns to generalize from this training data to new, unseen inputs.
Threat Intelligence #
information about potential or current attacks that threaten an organization, which is used to inform security decisions and strategies.
Transfer Learning #
a technique in machine learning where a pre-trained model is used as a starting point for a new task, allowing the model to leverage the knowledge and features learned from the original task.
Unsupervised Learning #
a type of machine learning in which the model is trained on an unlabeled dataset, where the correct output is not provided for each input. The model learns to identify patterns and structure in the data without explicit guidance.
Zero #
Day Exploit: a software vulnerability that is unknown to the software vendor or security community, allowing an attacker to exploit the vulnerability before a patch is available.
Artificial Neural Networks (ANNs) #
a type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes or "neurons" that process information and learn from experience.
Chatbots #
AI-powered conversational agents that can interact with humans in natural language, often used for customer service, sales, and marketing applications.
Counterfeit Goods #
products that are manufactured and sold with the intention of deceiving consumers into believing that they are buying genuine products from a particular brand, often at a lower price.
Cybersecurity #
the practice of protecting computer systems, networks, and data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data Augmentation #
a technique used to increase the size and diversity of a training dataset by applying various transformations to the existing data, such as rotation, scaling, and cropping.
Data Poisoning #
a type of attack on machine learning models where an attacker manipulates the training data to cause the model to make mistakes or produce biased results.
Deep Learning #
a subset of machine learning that uses artificial neural networks with multiple layers to learn and represent complex patterns in data.
Digital Forensics #
the process of collecting, analyzing, and preserving electronic evidence in a way that is legally admissible in court.
Explainable AI (XAI) #
the practice of designing AI models that can provide clear and understandable explanations for their decisions and recommendations.
Federated Learning #
a distributed machine learning approach where multiple devices or organizations collaboratively train a model on their local data, without sharing the raw data itself.
Grey Market #
a market for goods that are sold through unauthorized or unofficial channels, often at a lower price than the genuine products.
Inference Attacks #
attacks on machine learning models that aim to infer sensitive information about the training data or the model itself, such as the presence of certain features or patterns.
Insider Threats #
security risks that originate from within an organization, often due to negligence, malicious intent, or compromised credentials.
Machine Learning Operations (MLOps) #
the practice of managing and scaling machine learning models in production environments, including data management, model training, deployment, monitoring, and maintenance.
Model Inversion Attacks #
attacks on machine learning models where an attacker attempts to reverse-engineer the training data or the model itself, often to extract sensitive information.
Multi #
Armed Bandit Problem: a problem in reinforcement learning where an agent must balance exploration (trying out different actions to gather information) and exploitation (choosing the action that is expected to yield the highest reward).
Natural Language Understanding (NLU) #
the ability of a machine to understand and interpret human language in a meaningful way, including syntax, semantics, and pragmatics.
Optical Character Recognition (OCR) #
the process of converting images of text into machine-readable text, often used for document digitization and data extraction.
Overfitting #
a common problem in machine learning where a model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data.
Reinforcement Learning #
a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Semantic Segmentation #
the process of partitioning an image into multiple regions based on their semantic meaning, such as separating objects from the background or distinguishing between different types of objects.
Shadow IT #
the use of IT systems, devices, software, applications, and services without explicit organizational approval, often leading to security and compliance risks.
Social Engineering #
the practice of manipulating individuals into divulging confidential or personal information, often used for malicious purposes such as phishing and scamming.
Supervised Fine #
Tuning: a technique in transfer learning where a pre-trained model is further trained on a new, smaller dataset to adapt the model to a specific task.
SyntaxNet #
an open-source neural network framework for natural language processing, developed by Google and used for tasks such as part-of-speech tagging, named entity recognition, and parsing.
Transfer Learning #
a technique in machine learning where a pre-trained model is used as a starting point for a new task, allowing the model to leverage the knowledge and features learned from the original task.