AI Glossary

The use of malicious inputs or techniques to deceive, manipulate, or exploit AI models. In cybersecurity, adversarial AI can bypass defenses, cause misclassification, or degrade system performance.

An AI system capable of autonomous decision-making and taking actions to achieve defined goals without constant human intervention. In cybersecurity, agentic AI can automate complex defense or attack strategies.

A software entity powered by artificial intelligence that performs tasks on behalf of a user or system. AI agents in security contexts can monitor threats, respond to incidents, or execute automated workflows.

Cyberattacks enhanced or automated by AI to increase speed, scale, and sophistication, including adaptive phishing, automated exploitation, and evasion.

In AI, bias refers to systemic errors that produce unfair outcomes. Fairness is the mitigation of these biases to ensure equitable and ethical AI performance.

A policy framework outlining principles to protect individuals from harmful AI use. It addresses privacy, discrimination, transparency, and accountability in AI systems.

The protection of AI workloads, models, and data hosted in cloud environments. It combines cloud security practices with AI-specific safeguards against data leakage, model theft, and adversarial attacks.

The automated creation of source code using AI models like LLMs. In cybersecurity, it speeds development but also requires security reviews to prevent vulnerabilities.

The adherence of AI systems to relevant laws, regulations, and industry standards. In cybersecurity, this includes data protection, auditability, and governance frameworks for responsible AI use.

The ability to interpret and understand how an AI model makes decisions. Essential for trust, compliance, and debugging in security systems.

The principle of ensuring that AI systems produce unbiased and equitable outcomes across all user groups. In cybersecurity, AI fairness reduces the risk of discriminatory threat assessments or access controls, supporting compliance and ethical AI deployment.

When an AI model generates false or misleading outputs that appear plausible. In security, hallucinations can lead to misinformation or faulty automation.

The process of using a trained AI model to generate predictions, classifications, or responses based on new input data. In cybersecurity, inference powers real-time threat detection, anomaly identification, and automated decision-making without retraining the model.

Unauthorized access, manipulation, or disruption of AI systems, models, or data pipelines. Can lead to compromised outputs, service outages, or exposure of sensitive information.

A method to bypass an AI system’s safety constraints, enabling it to produce restricted or harmful outputs. A critical concern for AI security testing.

The algorithmic structure trained on data to perform tasks such as detection, classification, or generation. Security experts evaluate models for robustness and resistance to attacks.

A proactive security exercise that simulates adversarial attacks against AI systems to identify vulnerabilities and improve resilience.

The process of identifying, evaluating, and mitigating risks related to AI systems. In cybersecurity, it covers model robustness, supply chain vulnerabilities, and compliance risks.

The integration of AI into security operations to automate threat detection, incident response, and SOC workflows. Enhances speed and accuracy in cybersecurity defense.

Predefined safety and compliance boundaries that prevent AI from generating unsafe or unauthorized outputs. Essential in enterprise AI deployments.

The use of AI tools to proactively search for hidden cyber threats across networks and systems, often detecting patterns missed by human analysts.The use of AI tools to proactively search for hidden cyber threats across networks and systems, often detecting patterns missed by human analysts.

The practice of making AI system operations, decision-making processes, and limitations understandable to stakeholders. In cybersecurity, transparency supports trust, auditing, and regulatory compliance.

Short for Trust, Risk, and Security Management in AI. A governance approach to ensure AI systems are safe, reliable, and compliant.

The confidence stakeholders have in an AI system’s reliability, fairness, and security. Built through transparency, testing, and responsible use.

AI Bill of Materials. A detailed inventory of AI components, datasets, and dependencies. Helps track provenance and manage AI supply chain risks.

The continuous assessment and improvement of an organization’s AI-related security posture, including model governance and threat monitoring.

Amazon Web Services’ tools and frameworks for securing AI workloads, models, and data. Includes threat detection, compliance controls, and AI service protection.

A technique where an attacker or user alters an AI system’s stored conversation history to influence future responses or behaviors. It can be exploited to bypass safeguards, inject malicious instructions, or distort an AI agent’s decision-making

An AI-powered conversational interface that interacts with users via text or voice. In cybersecurity, chatbots can automate support and incident triage.

Integrated AI architectures combining multiple models or agents to perform complex tasks. In cybersecurity, they enable multi-layered defense strategies.

The process of designing and structuring the context given to AI systems to optimize their outputs. Crucial for accurate cybersecurity automation.

Unauthorized use of a system’s resources to mine cryptocurrency, often through malware or browser exploits. AI can both detect and be exploited for such attacks.

Malicious or unethical use of AI for cybercrime, espionage, or disinformation campaigns. Increasingly relevant in threat intelligence.

Unauthorized transfer of data from a system, often by malicious insiders or external attackers. AI can detect anomalies indicating exfiltration attempts.

The process of discovering patterns and insights from large datasets. In cybersecurity, data mining can reveal hidden threats or user behavior anomalies.

A cyberattack strategy that undermines AI model performance by deliberately corrupting datasets through injecting malicious data, mislabeling, or other means.

The safeguarding of data used in AI systems across its lifecycle, including collection, storage, training, and inference. Protects against breaches, leaks, and data poisoning attacks.

A subset of machine learning using neural networks with many layers to model complex data patterns. Powers many AI-driven cybersecurity tools.

The process of continuously monitoring and improving the security posture of sensitive data across systems and AI workloads.

AI processing that is performed locally on devices rather than in the cloud, reducing latency and enhancing data privacy in cybersecurity applications.

A data integration process that collects, cleans, and moves data into storage or analytics systems. Vital for preparing AI security datasets.

Short for evaluations, these are systematic tests to measure AI system performance, safety, and reliability under varied scenarios.

The ability to interpret and understand how an AI model makes decisions. Essential for trust, compliance, and debugging in security systems.

The process of adapting a pre-trained AI model to a specific task or domain. In security, fine-tuning improves detection accuracy for targeted threats.

AI models that create new content, such as text, code, or images. Cybersecurity uses include threat simulation and automated phishing generation.

A framework for managing an organization’s governance, risk management, and regulatory compliance, now extended to AI systems.

This term refers to human involvement in machine learning processes and AI workflows. Human experts provide insights, expertise, and judgment to

Combines multiple AI approaches—such as symbolic reasoning and machine learning—for better accuracy and adaptability in cybersecurity.

A centralized hub for developers to access tools, documentation, and services. In security, it ensures controlled access to AI development resources.

An AI framework for building applications powered by large language models with memory, chaining, and context control capabilities.

A machine learning model trained on massive datasets to understand and generate human-like text. Used in code generation, analysis, and chatbots.

The unauthorized hijacking or exploitation of large language models, often to inject malicious prompts, steal intellectual property, or manipulate outputs for cyberattacks.

The operational discipline of deploying, monitoring, and maintaining large language models in production environments.

The Model Context Protocol is an open standard that allows developers to create secure, bidirectional connections between AI-powered tools and their data sources.

The MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems, a knowledge base of adversary tactics, techniques, and case studies for AI systems. Guides security teams in defending AI assets.

A subset of AI that uses algorithms to learn from data and make predictions or decisions without explicit programming.

Practices for managing the lifecycle of machine learning models, from training to deployment, ensuring reliability and scalability.

The integration of machine learning, security, and operations practices to safeguard AI systems throughout their lifecycle.

An open standard introduced by Anthropic in late 2024 that standardizes how AI models connect with external data sources and tools, enabling greater interoperability between LLMs and external systems.

The unauthorized extraction or replication of a proprietary AI model, often for competitive or malicious purposes.

A computational model inspired by the human brain, used in deep learning to process complex data relationships.

AI techniques that allow computers to understand, interpret, and generate human language. Used in chatbots, threat detection, and intelligence gathering.

AI systems whose outputs can vary for the same input due to probabilistic processes, requiring special handling in security contexts.

When an AI system fabricates the existence of software packages or components, leading to potential supply chain risks.

The integration of AI capabilities across all aspects of an organization’s operations, tools, and infrastructure. In cybersecurity, pervasive AI enables continuous, automated defense at scale.

AI systems designed to forecast outcomes or behaviors based on historical data patterns. Used in threat prediction and risk assessment.

The practice of crafting effective prompts to guide AI model outputs, optimizing performance for security use cases.

A malicious attempt to manipulate an AI system’s behavior by embedding harmful instructions into input prompts.

An AI approach that combines language generation with external knowledge retrieval for more accurate and context-rich answers.

The development and deployment of AI systems that are ethical, fair, transparent, and secure. In cybersecurity, responsible AI practices reduce bias, ensure compliance, and minimize misuse risks.

The unapproved use of AI tools and models within an organization, creating compliance and security risks.

The malicious registration of packages with names similar to legitimate ones to trick developers into installing them.

The dataset used to teach an AI model how to perform a task. In security, its quality directly impacts detection accuracy.

A new approach to software development where developers create code through AI-powered platforms simply by describing desired outcomes in natural language, without requiring manual coding or prior programming knowledge.