Cybersecurity

Cybersecurity, also known as computer security and information technology security, protects internet-connected systems from malicious cyber-attacks and focuses on protecting hardware, software, and data systems from unauthorized manipulation, control, and theft.

There are many cybersecurity approaches for protecting digital assets, including application security, information security, network security, operational security, disaster recovery, business continuity planning, and end-user education.

Data security is the term used to describe digital data protection, such as information stored in databases, from destructive outside forces, unwanted actions from hackers or cyberattacks, and accidental errors introduced by unauthorized users. Data security is a high-level term used to describe various sub-services, including data privacy, synthetic data, data encryption including cryptography and homomorphic encryption, and data integrity.

According to McGraw-Hill, synthetic data is "any production data applicable to a given situation that is not obtained by direct measurement." Craig S. Mullins describes production data as "information that is persistently stored and used by professionals to conduct business processes."

Data encryption is a security method used to encode information that users can only access or decrypt with the correct encryption key. Encrypted data is sometimes referred to as ciphertext and will appear scrambled or unreadable to individuals without the proper access information. Two types of data encryption are homomorphic encryption, which allows an individual to perform calculations on encrypted data without decrypting it first, and cryptography, which requires a user to have a key to view the information.

Data integrity is defined as the overall accuracy, completeness, and consistency of data. Additionally, data integrity references regulatory compliance and data safety; one example is GDRP compliance and security. The integrity of data is maintained by processes and standards that are implemented in the design phases. Sub-sectors of data security include adversarial machine learning and artificial intelligence (AI) safety.

Adversarial machine learning is a machine learning technique that attempts to fool or trick models through deceptive input to cause a malfunction in the machine learning model. This is done to fix breaks in code and better protect machine learning models from providing inaccurate data with vulnerabilities that can compromise the entirety of data sets.

AI safety is an important developing technology as the development of deepfakes grows more prominent. Deepfakes are a synthetic AI form where users take on the identity of other people. Synthetic media generation of photorealistic avatars and actors causes concern for identity theft and biometric data collection, resulting in companies developing software to detect deepfakes proactively.

Network security is a set of rules and configurations developed and designed to protect the confidentiality, integrity, and accessibility of computer networks. Network security softwares work to prevent and monitor access, misuse, and modification of computer networks or other network-accessible devices or resources.

Application security is the process of finding, fixing, and enhancing the protection and securities involving applications. App security is typically implemented in the development stages, but oftentimes includes various tools and methods to help protect apps after they are deployed for use. Application security has become more necessary over the years as hackers have turned attention to applications over traditional websites and emails. Automated methods for assessing the effectiveness of application security have been developed including static program analysis and dynamic program analysis.

Static program analysis is used in application security to ensure a product is protected before it is deployed. Static analysis takes place in a non-runtime environment and is also referred to as static application security testing (SAST). SAST is the method of looking at an application and testing its securities from the inside out. The test is performed without the deployment of the application and instead focuses on the source code, byte code, or application binaries for signs of vulnerability regarding security. Once the SAST is complete, data and control paths are modeled for security weakness analysis of the internal structure and security of the application, not the overall functionality.

Dynamic analysis, also known as dynamic application security testing (DAST) is a testing method that looks at the application from the outside, while the program or application is operating. DAST programs test the integrity of the application through manipulations to find vulnerabilities. The dynamic test simulates an outside attack against an application and analyzes the application's reactions to determine the level of vulnerability, if a vulnerability exists.

Endpoint security is the process of securing endpoints or entry points on end-user devices such as laptops, desktops, and mobile devices from malicious attacks. Endpoint security systems and software protect the endpoints on the network or in the cloud from cybersecurity threats. Endpoint security has evolved from the well-known anti-virus software programs into software that can defend endpoints from sophisticated malware. Endpoint security is the combination of various software including antivirus, identity and access management, mobile device management, authentication, fraud detection, identity theft, email security, and anti-phishing.