Master The Terminology

Personal Cybersecurity

Two-Factor Authentication (2FA): An additional layer of security requiring not only a password and username but also something that only the user has on them, such as a piece of information only they should know or have immediately to hand.

Firewall Firewall: A network security system that monitors and controls incoming and outgoing network traffic based on predetermined security rules.

Malware Malware: Malicious software designed to harm, exploit, or otherwise compromise a device, service, or network.

Phishing: A method of fraudulently obtaining personal information by pretending to be a trustworthy entity in electronic communications.

Encryption: The process of converting information or data into a code to prevent unauthorized access.

Virtual Private Network (VPN): A service that encrypts your internet connection and hides your IP address, making your online actions more secure and private.

Data Protection

Data Breach: An incident in which sensitive, protected, or confidential data is accessed, disclosed, or stolen by an unauthorized individual.

Data Encryption: The process of converting data into a format that is unreadable without the correct decryption key, used to protect data from unauthorized access.

Backup: The process of copying and storing data so that it may be used to restore the original after a data loss event.

Secure Socket Layer (SSL): A standard security technology for establishing an encrypted link between a server and a client—typically a web server (website) and a browser.

Personal Identifiable Information (PII): Information that can be used on its own or with other information to identify, contact, or locate a single person, or to identify an individual in context.

Data Privacy

Cookies: Small pieces of data stored on a user's device by a website to remember information about the user, such as login credentials or site preferences.

Privacy Policy: A statement or legal document that discloses how an organization gathers, uses, discloses, and manages a customer's data.

GDPR (General Data Protection Regulation): A regulation in EU law on data protection and privacy in the European Union and the European Economic Area, providing individuals with more control over their personal data.

Anonymization: The process of removing personally identifiable information from data sets, so that the people whom the data describes remain anonymous.

Data Minimization: The principle of collecting the least amount of personal data necessary for the purpose for which it is processed.

Online Reputation Management

Digital Footprint: The trail of data you leave behind when using the internet, including social media posts, websites visited, and other online activities.

Reputation Monitoring: The process of tracking online mentions of a person or organization to manage and influence their online reputation.

Search Engine Optimization (SEO): The practice of increasing the quantity and quality of traffic to your website through organic search engine results, often used to influence online reputation.

Content Removal: The process of legally or administratively removing negative or harmful content from the internet that affects an individual's online reputation.

Social Media Management: The process of creating, scheduling, analyzing, and engaging with content posted on social media platforms, often to maintain or improve online reputation.

Technology Terms

IP Address: A unique string of numbers separated by periods that identifies each computer using the Internet Protocol to communicate over a network.

Cloud Computing: The delivery of computing services—including servers, storage, databases, networking, software, and analytics—over the internet ("the cloud").

API (Application Programming Interface): A set of rules that allows different software entities to communicate with each other.

Operating System (OS): The software that supports a computer's basic functions, such as scheduling tasks, executing applications, and controlling peripherals.

Bandwidth: The maximum rate of data transfer across a given path; the capacity of a network to transmit data.

Blockchain: A decentralized digital ledger that records transactions across many computers so that the record cannot be altered retroactively.

Internet of Things (IoT): The network of physical objects—"things"—that are embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet.

Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

Cybersecurity Terms

Antivirus Software: A program designed to detect and destroy computer viruses and other malicious software.

Botnet: A network of private computers infected with malicious software and controlled as a group without the owners' knowledge, often used to send spam or launch attacks.

Ransomware: A type of malware that threatens to publish the victim's data or perpetually block access to it unless a ransom is paid.

Spyware: Software that enables a user to obtain covert information about another's computer activities by transmitting data covertly from their hard drive.

Zero-Day Exploit: A vulnerability in software that is unknown to the vendor, for which a fix is not yet available, and which hackers can exploit.

DDoS (Distributed Denial of Service): An attack in which multiple compromised computer systems attack a target, such as a server or website, causing a denial of service for users of the targeted resource.

Trojan Horse: A type of malware that disguises itself as a legitimate program to gain access to a user’s system and cause harm.

Pharming: A cyber attack intended to redirect a website's traffic to another, fake site, usually to steal personal information such as passwords or credit card numbers.

SSL/TLS (Secure Sockets Layer/Transport Layer Security): Protocols for establishing authenticated and encrypted links between networked computers.

Penetration Testing: The practice of testing a computer system, network, or web application to find vulnerabilities that an attacker could exploit.

Types of Artificial Intelligence

Narrow AI (Weak AI): Narrow AI is a type of artificial intelligence that is designed and trained to perform a specific task or a narrow range of tasks. Unlike general AI, narrow AI does not possess consciousness or general cognitive abilities and is focused solely on executing predefined functions.

1. Examples: Voice assistants like Siri or Alexa, recommendation algorithms used by Netflix or Amazon, and facial recognition systems.

General AI (Strong AI): General AI refers to artificial intelligence that has the capability to understand, learn, and apply knowledge across a wide range of tasks, similar to the cognitive abilities of a human. General AI can perform any intellectual task that a human can do, with the ability to reason, plan, and solve problems autonomously.

1. Current Status: General AI remains theoretical and has not yet been achieved. It is a long-term goal in AI research.

Artificial Superintelligence (ASI): Artificial Superintelligence represents a level of intelligence that surpasses human intelligence in every aspect, including creativity, problem-solving, and emotional intelligence. ASI would be able to outperform humans in virtually every field, potentially leading to transformative changes in society.

1. Potential Impact: ASI is speculative and poses ethical and existential questions about the future of humanity and the control of such advanced AI systems.

Machine Learning (ML): Machine Learning is a subset of AI that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data. ML systems improve over time as they are exposed to more data, without being explicitly programmed for every task. Types:

1. Supervised Learning: The algorithm is trained on a labeled dataset, meaning that each training example is paired with an output label.

2. Unsupervised Learning: The algorithm is given data without explicit instructions on what to do with it, allowing it to identify patterns and relationships.

3. Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties based on its actions.

 

Deep Learning: Definition: Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to analyze and interpret complex patterns in data. Deep learning models are particularly powerful for tasks such as image and speech recognition, natural language processing, and autonomous driving.

1. Example: Deep learning is used in applications like self-driving cars, where it helps the vehicle understand and interpret its surroundings to make real-time decisions.

Natural Language Processing. Natural Language Processing is a branch of AI that focuses on the interaction between computers and humans through natural language. NLP enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful.

1. Examples: Language translation services like Google Translate, chatbots that can understand and respond to queries, and sentiment analysis tools.

Other Key Terms

Deep Fakes: Deep Fakes are a type of synthetic media in which a person’s likeness is replaced with someone else’s using deep learning techniques. These AI-generated videos or images can make it appear that someone is saying or doing something they never actually did, often with highly convincing results.  

1. Risks: Deep fakes can be used maliciously to spread misinformation, manipulate public opinion, defame individuals, or even commit fraud.

Disinformation: Disinformation refers to the deliberate spread of false or misleading information intended to deceive or mislead an audience. Unlike misinformation, which is incorrect information spread without malicious intent, disinformation is purposeful and often used as a tool to influence public opinion or obscure the truth.

1. Examples: Fake news stories, doctored images or videos, and false social media posts intended to create confusion or manipulate behavior.

Threat Actors: Threat Actors are individuals, groups, or organizations that intentionally create or exploit vulnerabilities in systems to carry out malicious activities. They can be motivated by various goals, such as financial gain, political influence, espionage, or disruption. Types:

1. Hacktivists: Individuals or groups who carry out cyberattacks to promote a political agenda or social change.

2. State-Sponsored Actors: Cyber operatives working on behalf of a nation-state to conduct espionage, disrupt operations, or gather intelligence.

3. Cybercriminals: Individuals or organized groups that engage in illegal activities for financial gain, such as ransomware attacks, identity theft, and online fraud.

4. Insider Threats: Employees or associates within an organization who exploit their access to internal systems for malicious purposes, either for personal gain or to harm the organization.