#trendsofinformationsecurity

1. Hardware Authentication

Usernames and passwords are no longer adequate to offer infallible protection to hardware. A more secure pattern of authentication is required.

Previously, Intel has initiated a major breakthrough in its domain by introducing Sixth-Generation Core vPro Processor. These powerful user authentication chips are embedded into the hardware itself. With this processor, they are adding hardware-enhanced features that can validate a user’s identity. They have put their efforts to build a device of security which requires three things from the users - what they know, such as a password; who they are, such as a username; and what they have, such as a token. In the case of Authenticate, the device becomes the what-you-have.

Scott Crawford, Research Director for Information Security at 451 Research stated, “We've seen this in other manifestations, such as licensing technologies and tokens”. He further adds, “The most immediate application for the technology is for authenticating an endpoint in a traditional IT environment — laptops, desktops, and mobile devices using Intel chipsets.”

2. User-behavior analytics

User behavior analytics (UBA) is technology that identifies any behavior by the user that’s out of the ordinary. Any anomalous behavior triggers a red flag to system defenders that all is not well. This technique is widely used to target social media and online advertisements to the right set of audience. The use of big data analytics can help in identifying anomalous behavior by a user. This emerging technology can potentially close a blind spot that keeps cybersecurity experts from identifying an attack. It can also alert a manager that a particular employee needs some extra security training.

See Also: Top Information Security Consulting Companies

"If you think of the attack chain as initial penetration, lateral movement, and then compromise, theft, and exfiltration of sensitive data, the middle links in that attack chain have not been very visible to enterprise security pros, and that's why the interest in user behavior analytics today," Crawford said.

3. Data Loss Prevention

Data loss prevention is the process of identifying confidential data or technologies like encryption and tokenization. Here are few examples which can help in protecting the data:

● Preventing cyber-attackers from monetizing data.

● Securing data in a protected or encrypted form as it’s used across an extended business enterprise.

● Protecting data down to a field and subfield level.

4. Deep Learning

Deep learning is being used to analyze data such as logs, transactions and real-time communications to detect threats or unwarranted activities. Kris Lovejoy, president of Acuity Solutions, maker of an advanced malware detection platform stated, "With its ability to decipher between good and bad software, at line speed, machine-learning technologies will offer a significant boon to security practitioners who seek to decrease time to advanced threat detection and eradication," Like UBA, deep learning tracks anomalous behavior – this time on the part of entities rather than individual users. For example, business analytics may focus on data center trends and anomalies.

5. The Cloud