AI Governance, Risk, and Compliance Glossary

AI Alignment refers to the field of study focused on ensuring that artificial intelligence systems operate in accordance with human values, intentions, and ethical standards.

AI GRC Project Rejection Rate is a metric used to measure the percentage of Governance, Risk, and Compliance (GRC) projects that an organization rejects due to concerns.

AI Governance refers to the framework and practices that guide the development, deployment, and management of artificial intelligence systems. It encompasses policies

AI Guardrails refer to a framework of guidelines, safety protocols, and ethical standards designed to ensure the responsible and safe deployment of artificial intelligence systems.

AI Law refers to the body of law related to court cases, including Artificial Intelligence adjudication. It encompasses various legal issues related to the development, deployment, and use of AI systems, including intellectual property, data & privacy, liability, consumer protection, antitrust issues, human oversight, human rights, and ethics.

AI policy refers to rules, regulations, and guidelines designed by some form of governing authority (private or public) that govern the development, deployment, and use of AI technologies. 

AI Policy Compliance refers to the adherence to regulations, guidelines, and ethical standards governing the use of artificial intelligence technologies. This encompasses ensuring

AI Red Teaming is a proactive security practice that involves simulating cyberattacks on artificial intelligence systems to identify vulnerabilities, weaknesses, and potential threats

AI Risk refers to the potential for negative consequences arising from the development and deployment of AI systems, including bias, discrimination, cybersecurity threats,

AI Risk Management is the process of identifying, assessing, and mitigating risks in AI development and deployment. It ensures compliance with regulations and safeguards against ethical,

AI Safety is measures taken to ensure that AI systems operate within defined boundaries and do not cause harm to individuals or society.

AI Security refers to the practices and technologies designed to protect artificial intelligence systems from threats, vulnerabilities, and malicious attacks. It encompasses safeguarding data integrity,

AI Threat Intelligence leverages artificial intelligence to analyze, detect, and respond to cybersecurity threats in real-time. By using machine learning and advanced analytics,

AI Vulnerability Testing is a systematic process designed to identify and evaluate weaknesses in artificial intelligence systems. This critical assessment involves simulating attacks,

AI-in-the-loop is a re-framing of the concept of "human-in-the-loop" that acknowledges the importance of people in the decision-making process of AI. It recognizes the value of human expertise and decision-making capabilities while leveraging the strengths of Artificial Intelligence to support and assist in the decision-making process.

Accountability refers to the attribute of being responsible for the actions and decisions of AI, as well as the impact AI systems have on individuals and society.

Adversarial attacks are deliberate manipulations of input data designed to deceive machine learning models, particularly in fields like computer vision and natural language processing.

Adversarial Machine Learning is a subfield of artificial intelligence focused on developing models that can withstand attacks from malicious inputs. This discipline explores

Artificial General Intelligence (AGI) refers to AI systems that can perform any cognitive task a human could.

Artificial Intelligence (AI) is a field of computer science and engineering that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. AI involves developing algorithms, computer programs, and systems that can learn from data and make decisions or predictions based on that learning. 

An assessment is the evaluation of AI systems to determine their performance and overall impact.

Attack insertion refers to a strategic technique in cybersecurity where malicious actors exploit vulnerabilities in a system or network by inserting harmful code or commands.

Attestation is the action of a designated user being a witness to or formally certifying a report.

An audit is an independent inspection or evaluation of whether an AI system meets specific criteria, typically performed to ascertain the accuracy of statements provided by an organization. 

Auditing is the process of making AI systems transparent and accountable, enabling the monitoring and assessment of their behavior.

An Autonomous System is an AI system that can operate independently, without human intervention.

Backdoor attacks are a form of cybersecurity threat where an attacker secretly creates a hidden entry point into a system, allowing unauthorized access to bypass normal authentication methods.

Behavioral AI Safety refers to the practices and methodologies aimed at ensuring the safe and ethical deployment of artificial intelligence systems that interact with human behaviors.

Social bias refers to human-created biases, such as stereotypes, that may be reflected in AI systems. Statistical bias refers to the systematic error in an AI system's predictions that arise from biased data or algorithms.

Bias detection refers to the systematic identification of prejudiced or unfair tendencies in data, algorithms, or decision-making processes. It involves analyzing datasets and models to uncover biases that may lead to discriminatory outcomes,

Bias mitigation refers to the strategies and techniques employed to identify and reduce biases in data, algorithms, and decision-making processes. This practice is essential

Black Box Attacks refer to a type of adversarial machine learning exploit where an attacker manipulates input data to deceive a model without knowing its internal structures or parameters.

Bot detection refers to the process of identifying automated software programs, or bots, that mimic human behavior on websites and applications. Effective bot detection employs advanced algorithms

Brand risk refers to the potential negative impact on a brand's reputation, image, and financial performance resulting from various factors, including but not limited to customer dissatisfaction, product or service quality issues, regulatory violations, legal disputes, and public relations crises.

Compliance audits are systematic evaluations conducted to assess an organization's adherence to regulatory standards, internal policies, and industry guidelines.

Compliance risk refers to the potential for financial loss, legal penalties, and reputational damage that organizations face when they fail to adhere to laws, regulations, and internal policies. This risk encompasses various areas, including data protection, financial reporting, and industry-specific regulations.

Compliance-Aware AI refers to artificial intelligence systems designed to operate within regulatory frameworks, ensuring adherence to legal standards and industry guidelines.

Confidential Computing is a cutting-edge technology designed to enhance data privacy and security by keeping sensitive data encrypted and secure while in use.

Confidential Containers are secure, specialized storage solutions designed to protect sensitive information and materials from unauthorized access. Ideal for businesses handling

Conformity assessment is the process of determining whether a product, system, or service meets specific standards or requirements, as defined by organizations

Content filtering is the process of controlling access to digital content based on predefined criteria, such as keywords, categories, or user preferences. Commonly used in web browsers,

Conversational AI is an AI system designed to interact with humans through natural language processing and generation.

Credible AI Red Teaming refers to the practice of rigorously testing artificial intelligence systems by simulating adversarial attacks and identifying vulnerabilities.

Cybersecurity in AI refers to the intersection of artificial intelligence and cybersecurity practices aimed at protecting computer systems, networks, and data from cyber threats.

Data Integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data remains unaltered during storage, retrieval, and processing, maintaining its authenticity and quality.

Data poisoning is a type of adversarial attack where malicious actors intentionally introduce false or misleading data into a machine learning model's training set.

Data Privacy in AI refers to the protection of personal and sensitive information during the collection, processing, and storage phases of artificial intelligence systems.

Data Provenance refers to the documentation and tracking of the origins, history, and transformations of data throughout its lifecycle. It encompasses the processes, sources,

Data quality refers to the degree to which data used to train, test, and deploy an AI system is accurate, relevant, complete, consistent, and free from bias, errors, or other issues that could affect the performance or outcomes of the AI system.

Deepfake detection refers to the use of advanced technologies and algorithms to identify manipulated media, such as videos or audio, created using artificial intelligence techniques.

Differential Privacy is a robust data protection framework that ensures individual privacy when analyzing large datasets. By adding controlled noise to the data or query results,

Domain-Specific Security refers to tailored security measures and protocols designed to protect specific industries, applications, or environments. Unlike generic security solutions,

Dynamic AI Defense refers to an advanced cybersecurity approach that leverages artificial intelligence and machine learning to detect, analyze, and respond to threats in real time.

The EU AI Act is the world's first comprehensive AI regulation, designed to ensure AI systems are safe, transparent, and uphold fundamental rights. It classifies AI based on risk levels—prohibited, high-risk, limited-risk, and minimal-risk—with stricter rules for high-risk applications. The Act promotes innovation while enforcing compliance and accountability.

Encrypted Model Inference refers to the process of executing machine learning model predictions while keeping the input data and the model itself secure through encryption techniques.

Endpoint Security for AI refers to the protection mechanisms implemented to secure devices and systems that utilize artificial intelligence technologies. This involves safeguarding endpoints such as computers,

A Use Case Audit Trail records all the actions performed by users during a specific time in the Enkrypt AI Platform on a particular Use Case. The Audit Trail can be used to perform an audit for the Use Case and trace all system, compliance, and reporting activity that has been logged from stakeholders and contributors.

Enkrypt AI provides pre-packaged, industry-specific solutions to ensure reliable, compliant, and ethical AI use. Read the examples below on how industries like Life Sciences, Finance, and Insurance are using our tailored solutions to realize a quick time-to-value on AI security and compliance. To learn more, click here.

Enterprise AI Security refers to the integration of artificial intelligence technologies into the security frameworks of organizations to protect sensitive data, systems, and networks.

Ethical hacking in AI refers to the practice of using artificial intelligence tools and techniques to identify and exploit vulnerabilities within systems and networks, ensuring their security.

Evidence refers to the data and information that demonstrate an AI system’s performance, reliability, fairness, and compliance with relevant regulations or standards.

Explainability refers to the degree to which the internal mechanisms of an artificial intelligence (AI) system or machine learning model can be understood by humans.

Explainable AI Security refers to the methodologies and practices that ensure artificial intelligence systems are transparent, interpretable, and accountable in their decision-making processes.

Failure modes in AI refer to the various ways artificial intelligence systems can malfunction or produce undesirable outcomes. These modes can arise from data bias, algorithmic errors,

Fairness in AI recognizes that AI systems distribute both benefits and harms, requiring thoughtful consideration of their impact. It includes individual fairness, where similar individuals receive similar predictions

Fairness in AI refers to the ethical principle that artificial intelligence systems should operate without bias, ensuring equitable treatment across diverse groups. It involves designing algorithms that mitigate discrimination based on race, gender, age, or other attributes, promoting transparency, accountability, and inclusivity.

False positives in AI security refer to instances where a security system incorrectly identifies benign activities or entities as threats.

Federated Learning Security refers to the measures and protocols designed to protect data privacy and integrity in federated learning systems.

A Firewall for AI Systems is a security measure designed to protect artificial intelligence applications from unauthorized access, cyber threats, and data breaches.

Foundational Model refers to a paradigm for building AI systems, where any model that is trained on broad data (generally using self-supervision at scale) can be fine-tuned and adapted to a wide range of downstream tasks and purposes, making it a versatile and powerful approach to AI development.

Fuzz Testing for AI is a software testing technique designed to identify vulnerabilities and bugs in artificial intelligence systems by inputting random, unexpected, or malformed data.

GPT Alignment refers to the process of ensuring that Generative Pre-trained Transformers (GPT) operate in harmony with human values, intentions, and ethical standards.

General-purpose AI refers to AI Systems that have a wide range of possible uses, both intended and unintended by the developers. They can be applied to many different tasks in various fields, often without substantial modification and fine-tuning.

General-Purpose AI (GPAI) Risks refer to the potential dangers and challenges associated with the development and deployment of advanced artificial intelligence systems that operate across various domains.

Generative AI is AI that can generate novel content by utilizing existing text, audio, or images. 

Generative AI Security refers to the strategies and technologies implemented to protect generative artificial intelligence systems from threats, vulnerabilities, and misuse.

Governance Artifacts are the outputs of governance—documents that translate information about AI systems into relevant information for diverse stakeholders (i.e., transparency reports, audit reports, algorithmic impact assessments, model cards, data set cards, etc.).

Governance in Secure AI refers to the frameworks, policies, and practices that ensure artificial intelligence systems are developed, deployed, and managed responsibly and ethically.

"Guardrails for AI" refers to the frameworks and guidelines established to ensure the ethical, safe, and responsible development and deployment of artificial intelligence technologies.

Hallucination Detection refers to the process of identifying and mitigating false information generated by artificial intelligence, particularly in natural language processing and machine learning models.

Harmful Content Filtering refers to the systematic process of identifying and removing or blocking inappropriate, dangerous, or offensive material from online platforms.

Human Oversight in AI refers to the practice of involving human judgment and intervention in the decision-making processes of artificial intelligence systems.

Human-Centered AI refers to the development, deployment, and use of AI systems that prioritize humans' needs, values, and experiences, with the goal of enhancing their capabilities, augmenting their decision-making, and improving their overall well-being. 

Human-Centered Design (HCD) is an approach to designing products, systems, and services that prioritize users' needs, wants, and limitations. It involves empathizing with the end-users and understanding their context, behaviors, and preferences to create effective, efficient, and satisfying solutions. 

Human-in-the-loop security refers to an integrated cybersecurity approach that combines automated systems with human oversight to enhance threat detection and response.

Humans-in-the-Loop (HITL) is an approach in AI development that involves human oversight and control over the machine learning process. HITL is typically used to validate and refine machine learning models and to handle exceptions and edge cases that may be difficult for the AI system to handle on its own. 

Humans-on-the-Loop (HOTL) is an extension of HITL, which involves humans providing feedback to the AI system to improve its performance over time.

Impact Assessment refers to a specific type of transparency report designed to help stakeholders assess the societal impact—both harms and benefits—of an AI system; it is generally created and reviewed before a system is put into use.

Inclusivity in AI refers to the practice of developing and deploying AI technologies in a way that considers and values diverse perspectives, needs, and experiences and ensures that the benefits of AI are accessible to all members of society. 

Information Security for AI refers to the strategies, technologies, and practices designed to protect artificial intelligence systems and the data they process.

Input sanitization is a crucial security practice that involves validating and cleaning user inputs before processing them in software applications.

Interpretability refers to the degree to which a human can understand and explain the predictions or decisions made by a machine learning model or artificial intelligence system.

Intrusion Detection in AI refers to the use of artificial intelligence algorithms and machine learning techniques to monitor network traffic and identify suspicious activities or potential security breaches.

Jailbreak detection refers to the process of identifying whether a mobile device, particularly those running on iOS, has been modified to bypass security restrictions imposed by the operating system.

Just-In-Time AI Security Patching is an innovative approach to cybersecurity that utilizes artificial intelligence to deliver timely updates and patches for software vulnerabilities.

Kernel exploits in AI models refer to vulnerabilities that occur within the underlying operating system kernels when interacting with artificial intelligence frameworks.

Knowledge Graph Security refers to the practices and technologies employed to protect the integrity, confidentiality, and availability of knowledge graphs.

Large Language Model Security refers to the practices and technologies designed to protect large language models (LLMs) from various threats, including data breaches, adversarial attacks, and misuse.

Laws and AI Regulations refer to the legal frameworks and guidelines established to govern the development, deployment, and use of artificial intelligence technologies.

"Limitations of AI guardrails refer to the constraints and challenges inherent in the safety measures designed to govern artificial intelligence behavior.

Machine learning algorithms can be used for a wide range of applications, including image recognition, natural language processing, fraud detection, predictive maintenance, and personalized recommendations, among others.

Machine Learning Security refers to the practices and technologies designed to protect machine learning systems from vulnerabilities and attacks. This encompasses safeguarding data integrity, model robustness, and privacy while preventing adversarial attacks that can manipulate AI outcomes.

Malicious Prompt Injection refers to a cybersecurity attack where adversaries exploit vulnerabilities in AI systems by embedding harmful or misleading prompts within user inputs.

Model attacks refer to a range of malicious techniques aimed at exploiting vulnerabilities in machine learning models. These attacks can compromise data integrity, manipulate predictions, or extract sensitive information by targeting the model’s architecture, training data, or inference processes.

Model Cards are a type of governance artifact that document basic technical information about an ML model, which is relevant regardless of where and how the model is going to be deployed; Model Cards can be created by model builders before a final end application for the model has been identified. 

Model Extraction Attacks refer to a type of cybersecurity threat where an adversary attempts to replicate a machine learning model by querying it and analyzing its responses.

Model interpretability refers to the ability to understand and explain the decisions made by machine learning models. It involves techniques that make complex algorithms transparent, allowing stakeholders to grasp how inputs influence outputs.

Model robustness refers to the ability of a predictive model to maintain performance and accuracy when exposed to varied conditions, such as changes in input data, noise, or underlying distributions. In machine learning and statistical modeling, robust models are essential for ensuring reliability, generalization, and resilience against overfitting.

Multi-Stakeholder AI Security refers to a collaborative approach involving various entities—such as governments, businesses, academia, and civil society—to address the security challenges posed by artificial intelligence.

Multi-stakeholder collaboration in AI refers to the involvement of multiple stakeholders from different sectors and backgrounds in the development, deployment, and governance of AI systems.

Natural Language Processing Security (NLP Security) refers to the measures and protocols implemented to protect systems that utilize Natural Language Processing technologies from various threats.

Network Security for AI refers to the protective measures and protocols designed to safeguard artificial intelligence systems, data, and infrastructure from cyber threats.

Neural Network Security refers to the safeguarding of artificial intelligence systems, particularly those utilizing neural networks, against vulnerabilities and threats.

Non-Adversarial Robustness refers to the resilience of machine learning models against non-malicious data perturbations and variations.

Open-Source Model Security refers to the strategies and practices that ensure the safety and integrity of open-source software and models.

Out-of-Distribution Detection (OOD Detection) refers to the process of identifying data that significantly differs from the training dataset in machine learning and artificial intelligence models.

Overfitting occurs when a machine learning model learns noise and details from training data too well, resulting in poor generalization to new data.

PII Protection in AI refers to the measures and protocols implemented to safeguard Personally Identifiable Information (PII) when using artificial intelligence technologies.

Penetration Testing for AI refers to the systematic evaluation of artificial intelligence systems to identify vulnerabilities and security weaknesses. This process involves simulating cyber-attacks to assess the robustness of AI algorithms, data integrity, and overall system resilience.

Policy refers to a set of guidelines, principles, and rules that are developed and implemented to guide decision-making and behavior in a specific domain.

Policy-Adherence in AI refers to the compliance of artificial intelligence systems with established regulations, ethical guidelines, and organizational policies. It ensures that AI technologies operate within legal frameworks, promote fairness, transparency, and accountability, and align with societal values.

Post-Training Model Auditing refers to the comprehensive evaluation of machine learning models after they have been trained. This process involves assessing model performance, detecting biases, ensuring compliance with regulatory standards, and verifying the integrity of predictions.

Practical AI Guardrails refer to the structured guidelines and frameworks designed to ensure safe, ethical, and responsible use of artificial intelligence technologies.

Privacy is the right to control the collection, use, and disclosure of personal information, ensuring autonomy and protection from unauthorized access. The NIST defines privacy as safeguarding confidentiality, preventing undue data intrusion, and maintaining control over personal information.

Privacy-Preserving AI refers to artificial intelligence techniques designed to protect individual privacy while enabling data analysis and machine learning. By employing methods such as differential privacy, federated learning, and secure multiparty computation, Privacy-Preserving AI ensures that sensitive information remains confidential.

Programmatic AI Assessments refer to the use of automated methods to evaluate the performance and effectiveness of AI systems. These assessments can be used to evaluate different aspects of an AI system, such as its accuracy, efficiency, and robustness. 

Project Failure Rate is a percentage of proposed and started projects that don't make it to production for any given reason.

Project Rejection Stage refers to the phase in a project's lifecycle when a decision is made to terminate the project on a specific or aggregate basis. The later the Projection Rejection Stage, the more costly.

Prompt Injection Attacks are a type of security vulnerability targeting AI language models. In these attacks, malicious users manipulate input prompts to alter the model's responses, potentially leading to the disclosure of sensitive information or the generation of harmful content.

Quantization security risks refer to vulnerabilities that arise during the process of reducing the precision of data in machine learning and digital signal processing.

Query-Based Attacks on AI Models refer to sophisticated techniques where adversaries exploit AI systems by crafting specific queries to extract sensitive information, manipulate outputs, or cause misclassification.

Red Teaming for AI is a proactive cybersecurity strategy that involves simulating real-world attacks on artificial intelligence systems to identify vulnerabilities and weaknesses.

Regulation refers to rules, guidelines, or laws set by governing bodies to ensure safety, fairness, and ethical standards in industries. It shapes behavior to protect the public interest and promote security and competition.

Regulatory compliance for AI refers to the adherence of artificial intelligence systems to established laws, guidelines, and ethical standards governing their development and use.

Reinforcement Learning Security refers to the application of reinforcement learning (RL) techniques to enhance cybersecurity measures and protect systems from threats.

Resilient AI systems refer to artificial intelligence frameworks designed to withstand and adapt to unexpected challenges, disruptions, and adversities.

A proposed mechanism that allows AI developers to publish their models or systems to the open-source ecosystem with guidelines for what constitutes responsible (or irresponsible) use.‍

Responsible AI Security refers to the ethical and proactive measures taken to safeguard artificial intelligence systems and their data. This includes ensuring data privacy, preventing bias, and enhancing transparency in AI algorithms.

Risk tolerance in AI refers to the level of uncertainty and potential loss that organizations or stakeholders are willing to accept to achieve their objectives.

Robustness refers to the ability of an algorithm or model to maintain its performance and accuracy under different conditions, such as changes in the input data, noise or outliers in the data, or attacks designed to manipulate or disrupt the model's behavior. 

Rulemaking guidelines are instructions that regulate the development and implementation of rules by governing bodies. They ensure the process is transparent, inclusive, and efficient, resulting in effective and enforceable regulations.

Safe AI Architectures refer to structured frameworks designed to ensure the secure and ethical development of artificial intelligence systems. These architectures prioritize safety, reliability, and compliance with regulations, mitigating risks associated with AI deployment.

Safety Alignment of AI refers to the process of ensuring that artificial intelligence systems operate in accordance with human values, ethics, and safety standards.

Safety in AI refers to ensuring that AI Systems do not cause harm to people or the environment, while security in AI refers to protecting AI Systems and their data from unauthorized access or malicious attacks.‍

Secure AI pipelines refer to the comprehensive processes that ensure the safe and efficient development, deployment, and management of artificial intelligence models.

Secure Model Deployment refers to the process of safely integrating machine learning models into production environments while ensuring data protection, compliance, and operational integrity.

Secure Multi-Party Computation (SMPC) for AI is a cryptographic technique that enables multiple parties to collaboratively compute functions over their private data without revealing the data itself.

Security risks in Large Language Models (LLMs) refer to potential vulnerabilities that can be exploited by malicious actors. These risks include data leakage, adversarial attacks, and model inversion, which can compromise user privacy and lead to unauthorized access to sensitive information.

Security-Aware AI Development refers to the integration of security principles throughout the artificial intelligence development lifecycle. This approach emphasizes identifying and mitigating potential vulnerabilities in AI systems, ensuring data privacy, and maintaining compliance with regulations.

Self-Supervised Learning Risks refer to potential challenges and vulnerabilities associated with self-supervised learning methods in artificial intelligence and machine learning.

AI systems are socio-technical because they combine technical elements (algorithms, data, machine learning) with social factors (human input, ethics, and societal impact).

A standard is a set of guidelines or requirements that regulate a product, process, or service to ensure quality, safety, and performance. Developed by organizations like ISO, standards promote consistency and interoperability across industries.

The cost of a project being terminated for RAI or Governance reasons later in development (or after production) vs. earlier in the AI/ML Development Lifecycle.

Supply Chain Security for AI refers to the comprehensive strategies and practices designed to protect artificial intelligence systems throughout their supply chain lifecycle.

Synthetic Data for AI Security refers to the use of artificially generated datasets to enhance the training and evaluation of artificial intelligence models in security applications.

Technical evidence refers to any kind of objective, verifiable data or information that is collected, analyzed, and used to support a technical argument, hypothesis, or claim. Examples include performance or fairness metrics and plots demonstrating a system’s behavior on a particular use case.

Threat Modeling for AI is a systematic approach to identifying, assessing, and mitigating potential security threats associated with artificial intelligence systems.

Toxicity detection refers to the process of identifying harmful substances or toxic effects in various environments, including biological systems, chemical samples, and consumer products.

Transfer Learning Security refers to the application of transfer learning techniques while ensuring data protection and model integrity in machine learning systems.

Transformative AI defines AI Systems by their consequences rather than their capabilities. 

Transparency in AI is not only about explainability, but also about ensuring that people comprehend what your AI System does and how it does it.

A transparency report is a broad category of artifacts that are produced about AI systems that provides transparency about how they work and what potential risks/harms they pose; the term "transparency report" is a category rather than referring to one specific type of artifact.

Transparent AI decision-making refers to the practice of ensuring that artificial intelligence systems operate in a clear and understandable manner, allowing stakeholders to see how decisions are made.

Trust refers to the confidence and belief that individuals or entities have in the reliability, integrity, and capability of a system or a person. In AI, trust is crucial because it directly impacts user adoption, satisfaction, and overall success of AI systems. 

AI Trust Risk refers to the potential loss of confidence and trust from stakeholders in AI Systems due to biases in the data used to train the AI system, errors in the algorithmic decision-making process, lack of transparency in how decisions are made, or unethical or illegal uses of the technology.‍

Trustworthy AI refers to artificial intelligence systems that are designed to be reliable, ethical, and transparent. These systems prioritize user safety, privacy, and fairness while ensuring accountability in their decision-making processes.

Unintended AI behaviors refer to actions or outcomes produced by artificial intelligence systems that diverge from their intended purpose or design. These behaviors can arise from biases in training data, flaws in algorithms, or environmental factors influencing decision-making processes.

Universal Adversarial Perturbations (UAPs) are strategically crafted modifications applied to input data, such as images, that can universally deceive machine learning models, particularly in deep learning and computer vision.

User Privacy in AI refers to the protection of personal information and data collected by artificial intelligence systems. It encompasses practices and technologies designed to ensure that user data is handled securely, transparently, and ethically.

Vector Database Security refers to the measures and protocols implemented to protect vector databases, which store data in multi-dimensional formats for efficient retrieval and analysis.

Verification of AI Systems refers to the systematic process of evaluating artificial intelligence models and algorithms to ensure their accuracy, reliability, and compliance with specified requirements.

Voice Cloning Detection refers to the technology and methodologies used to identify and distinguish synthetic voices generated by artificial intelligence from authentic human voices.

Vulnerability scanning in AI refers to the automated process of identifying security weaknesses in artificial intelligence systems and their underlying infrastructure.

Watermarking AI-generated content refers to the practice of embedding identifiable marks or symbols within digital media created by artificial intelligence. This process serves to protect intellectual property, ensure authenticity, and deter unauthorized use or reproduction.

Web Security for AI APIs refers to the protective measures and protocols implemented to safeguard artificial intelligence application programming interfaces (APIs) from cyber threats and vulnerabilities.

Whitelisting in AI systems refers to the practice of allowing only approved entities, applications, or data sources to access and interact with an AI model or its outputs.

XSS (Cross-Site Scripting) attacks in AI systems refer to security vulnerabilities where malicious scripts are injected into trusted web applications, compromising user data and system integrity.

YAML-Based AI Configurations Security refers to the practices and measures implemented to protect AI system configurations defined in YAML (YAML Ain't Markup Language) format.

Zero Trust AI Frameworks are security models that operate under the principle of "never trust, always verify." They leverage artificial intelligence to continuously assess and authenticate users, devices, and applications within an organization’s network.

Zero-Day Exploits in AI refer to vulnerabilities in artificial intelligence systems that are unknown to the software developers and have not yet been patched. These exploits can be exploited by malicious actors to manipulate AI algorithms, compromise data integrity, or launch cyberattacks.