New Technologies in Cybersecurity

Behavioral Analytics

Behavioral analytics looks at data to understand how people behave on websites, mobile applications, systems, and networks. Cybersecurity professionals can use behavioral analytics platforms to find potential threats and vulnerabilities.

Analyzing patterns of behavior can lead to identifying unusual events and actions that may indicate cybersecurity threats.

For example, behavioral analytics may find that unusually large amounts of data are coming from one device. This may mean a cyberattack is looming or actively happening. Other indicators of malicious activity include odd timing of events and actions that happen in an unusual sequence.

Benefits of using behavioral analytics include early detection of potential attacks and the ability to predict future attacks. Organizations can automate detection and response using behavioral analytics.

Blockchain

Blockchain is a type of database that securely stores data in blocks. It connects the blocks through cryptography. Blockchain allows information to be collected, but not edited or deleted.

Cybersecurity professionals can use blockchain to secure systems or devices, create standard security protocols, and make it almost impossible for hackers to penetrate databases.

Benefits of blockchain include better user privacy, reduction of human error, greater transparency, and cost savings by removing the need for third-party verification.

Blockchain also eliminates the security problem of storing data in one place. Instead, data gets stored across networks, resulting in a decentralized system that is less vulnerable to hackers.

Challenges of using blockchain include the cost and inefficiency of the technology.

Cloud Encryption

Cloud services improve efficiency, help organizations offer improved remote services, and save money. However, storing data remotely in the cloud can increase data vulnerabilities. Cloud encryption technology changes data from understandable information into an unreadable code before it goes into the cloud.

Cybersecurity professionals use a mathematical algorithm to complete cloud encryption. Only authorized users with an encryption key can unlock the code, making data readable again. This restricted access minimizes the chance of data breaches by unauthorized attackers.

Experts agree that cloud encryption is an excellent cybersecurity technology for securing data. Cloud encryption can prevent unauthorized users from gaining access to usable data. Cloud encryption can also foster customer trust in cloud services and make it easier for companies to comply with government regulations.

Context-Aware Security

Context-aware security is a type of cybersecurity technology that helps businesses make better security decisions in real time.

Traditional cybersecurity technologies assess whether or not to allow someone access to a system or data by asking yes/no questions. This simple process can cause some legitimate users to be denied, slowing productivity.

Context-aware security reduces the chance of denying entry to an authorized user. Instead of relying on answers to static yes/no questions, context-aware security uses various supportive information like time, location, and URL reputation to assess whether a user is legitimate or not.

Context-aware security streamlines data-accessing processes and makes it easier for legitimate users to do their work. However, end-user privacy concerns pose a challenge.

Defensive Artificial Intelligence (AI)

Cybersecurity professionals can use defensive artificial intelligence (AI) to detect or stop cyberattacks. Savvy cybercriminals use technologies like offensive AI and adversarial machine learning because they are more difficult for traditional cybersecurity tools to detect.

Offensive AI includes deep fakes, false images, personas, and videos that convincingly depict people or things that never happened or do not exist. Malicious actors can use adversarial machine learning to trick machines into malfunctioning by giving them incorrect data.

Cybersecurity professionals can use defensive AI to detect and stop offensive AI from measuring, testing, and learning how the system or network functions.

Defensive AI can strengthen algorithms, making them more difficult to break. Cybersecurity researchers can conduct harsher vulnerability tests on machine learning models.

Extended Detection and Response (XDR)

Extended detection and response (XDR) is a type of advanced cybersecurity technology that detects and responds to security threats and incidents. XDR responds across endpoints, the cloud, and networks. It evolved from the simpler traditional endpoint detection and response.

XDR provides a more holistic picture, making connections between data in different places. This technology allows cybersecurity professionals to detect and analyze threats from a higher, automated level. This can help prevent or minimize current and future data breaches across an organization's entire ecosystem of assets.

Cybersecurity professionals can use XDR to respond to and detect targeted attacks, automatically confirm and correlate alerts, and create comprehensive analytics. Benefits of XDR include automation of repetitive tasks, strong automated detection, and reducing the number of incidents that need investigation.

Manufacturer Usage Description (MUD)

Manufacturer usage description (MUD) is a standard created by the Internet Engineering Task Force to strengthen security for IoT devices in small business and home networks.

IoT devices are vulnerable to network-based attacks. These attacks can lead to loss of private data or cause a machine to stop working properly. IoT devices need to be secure without costing too much or being too complicated.

Benefits of using MUD include simply, affordable improved security for IoT devices. Cybersecurity professionals can use MUD to make devices more secure against distributed denial of service attacks. MUD can help reduce the amount of damage and data loss in the event of a successful attack.

Zero Trust

Traditional network security followed the motto "trust but verify," assuming that users within an organization's network perimeter were not malicious threats. Zero Trust, on the other hand, aligns itself with the motto, "never trust, always verify."

A framework for approaching network security, Zero Trust makes all users authenticate themselves before they get access to an organization's data or applications.

Zero Trust does not assume that users inside the network are more trustworthy than anyone else. This stricter scrutiny on all users can result in greater overall information security for the organization.

Cybersecurity professionals can use Zero Trust to deal more safely with remote workers and challenges like ransomware threats. A Zero Trust framework may combine various tools, including multi-factor authentication, data encryption, and endpoint security.

Regulation

As the frequency of cyberattacks continues to grow significantly each year, governments are beginning to use and promote best practice regulations. In the past, the governments did not often get involved in cybersecurity issues.

Security Magazine, an industry publication for cybersecurity professionals, predicts that 2022 will be the year that governments start to play a bigger role in regulating how organizations ensure user information security.

Potential regulatory changes include executive orders regarding cybersecurity standards for government suppliers, penalties for companies that do not engage in best practices, increased demand for cyberinsurance, and ransomware disclosure laws. Greater regulation will likely lead to improved security standards.