Cybersecurity 101

Unlike generative AI (GenAI) that requires prompts to get results, agentic AI is an autonomous system that uses tools (including GenAI) to perform tasks automatically - that is, with very little or no human intervention. Agentic AI can independently set goals, plan multi-step processes, make decisions, and take actions to achieve objectives.

Ability of computer systems to perform tasks that usually require human intelligence, like learning, reasoning, problem-solving, perception, and decision-making, enabling machines to understand language, recognize patterns, and act autonomously to achieve goals.

A highly realistic, AI-generated image, video, or audio, created to convincingly impersonate someone or some event. In cybersecurity, deepfakes are used to trick users into taking an action, like redirecting funds or sharing confidential information.

A cloud-native security solution that centralizes next-generation antivirus with self-learning, AI-powered analytics for Windows, macOS, and Linux desktops, laptops, and servers. Goes beyond signature-based antivirus, using behavioral analytics to stop malware, ransomware, and zero-day threats that traditional solutions miss.

A security solution that combines Endpoint Protection Platform (EPP) technologies, advanced Endpoint Detection and Response (EDR), and self-learning AI-powered agents and services to protect computers, laptops, and servers from threats invisible to traditional solutions.

A type of artificial intelligence (AI) that can generate high-quality text, images, and other content based on massive amounts of data it has been trained on. Relies on human or non-human prompts to begin the creation process. ChatGPT is an example of a popular GenAI tool.

A WatchGuard security service that automates malware discovery and classifies current and future threats in mere seconds with AI-powered intelligence.

An AI tool trained on massive data sets of language to understand and generate human-like text. Capable of processing written instruction via human or non-human prompts, then responding in conversational language to create fresh content and answer complex questions.

Technologies based on algorithms that can learn from data, enabling systems to identify patterns, make decisions, and improve themselves through experience and data.

A fully managed cybersecurity service that continuously monitors your IT environment, including endpoints, networks, cloud applications, and user accounts, to detect and stop threats before they cause harm. Unlike traditional tools, which only alert users to possible issues, MDR combines advanced AI-driven analytics and human expertise to investigate and respond to attacks in real time.

Uses AI, machine learning, and behavioral analysis that is not available in traditional antivirus solutions to proactively detect and stop known and unknown threats. Goes beyond traditional signature-based methods to catch modern malware, ransomware, and fileless attacks before they cause damage.

An authentication method in which one login (typically with username and password) allows access to multiple applications and services, providing convenience for users and better and centralized oversight for IT teams.

Stands for Extended Detection and Response. While EDR focuses on identifying and responding to threats at the endpoint level, XDR broadens the scope by collecting telemetry data and automatically correlating detections across multiple security domains, including endpoint, identity, email, network, and cloud. Using AI and machine-learning technologies, XDR then performs automatic analysis to integrate them into a centralized security system.

A cybersecurity strategy based on the principle of "never trust, always verify," assuming threats exist everywhere. Rather than relying on a single technology, it implements multiple security controls, including multi-factor authentication, EDR, Zero Trust Network Access, and dark web credential monitoring. In addition, users only have access to the specific parts of the network they need and not more.

A security model that requires strict identity verification for every person and device, inside or outside the network perimeter, trying to access resources on a private network.

A security framework that assumes threats are everywhere and therefore verifies every user and device attempting to access resources, and grants least-privileged access to specific applications rather than to the entire network. A foundational security model within SASE.