Safeguarding Against AI-Generated Malware in the Era of Cybersecurity.

Bad computer programs created by artificial intelligence are becoming more common. In the past, people would look for bad code by comparing it to known bad code. But now, computers are learning to find bad code using other methods like artificial intelligence and machine learning.

The malicious side of AI

Artificial intelligence can help security professionals keep our digital world safe. It can quickly sort through lots of data and tell us which security problems are most important. But, bad people can use AI too. For example, a student used AI to create a robot that posted hateful messages on the internet. There is even an AI called ChatGPT that can help bad people do cyberattacks. Cybercriminals are very interested in using AI to create bad computer programs. People who work in cybersecurity are keeping an eye on this problem.

The prevention – Tactics, Techniques and Procedures

As cyber threats become more advanced, bad people use many different ways to attack our computers. Cybersecurity experts use something called "tactics, techniques, and procedures" to understand how the bad people are attacking. They can then use this information to find out who the bad people are and stop them from doing more damage. Understanding TTPs is very helpful when responding to a cyber attack and finding ways to prevent it from happening again.

The role of AI and ML in cybersecurity

Cybersecurity experts use AI and machine learning to help them quickly identify and respond to cyber threats. These technologies can analyze data and find patterns that might indicate a threat. This is important because bad people are always finding new ways to attack our computers. By using AI and machine learning, experts can stay one step ahead of the bad guys. Some security products already use these technologies to help keep us safe. The benefits of using AI and machine learning in cybersecurity include being able to quickly identify new patterns of attack, creating better threat analysis, and automating response to attacks.

Using AI and machine learning in cybersecurity is important because it has many benefits, such as:

Automated attack detection: AI can process millions of attack vectors in seconds and detect new attack patterns that are not yet known.

Zero-Trust Model: Without AI, analyzing diverse datasets is not useful because human behavior is predictable. AI can help create a comprehensive threat analysis necessary to maintain a functional zero-trust model.

Threat Management: AI can assist cybersecurity teams by automatically interpreting attack signals, prioritizing warnings and incidents, and customizing defenses based on the attacker's size and speed.

Use-cases of AI in cybersecurity

a) A company claims that their AI-based cybersecurity tool aids banks and other financial organizations in identifying security threats and adversaries while analyzing transactions to find weak security risks. 

b) Another software has an intelligent antivirus programme that uses AI  to find, stop, and foresee threats. This tool does not require virus signature updates, in contrast to typical antivirus software, but it will eventually learn to recognise harmful applications from beginning to end.

c) In another example, AI-ML-backed software analyses network traffic statistics in order to determine the baseline behavior of each user and device in the company. The software learns to recognise a critical departure from the usual user behavior and immediately notifies the organization of cyber hazards after receiving input and other training datasets from subject matter experts.

Conclusion

In today's cybersecurity landscape, the adoption of artificial and machine learning has become crucial to stay ahead in the ongoing arms race. Their capabilities extend beyond just detecting known threats. By utilizing the MITRE ATT&CK framework, organizations can strengthen their incident-response and threat-hunting capabilities, ultimately increasing their ability to identify and mitigate security risks. This, in turn, can safeguard their valuable assets and reduce the likelihood of a successful attack.

Furthermore, robust Security Information and Event Management (SIEM) systems can also play a vital role in the future of cybersecurity. By collecting and analyzing data from multiple sources, these systems can help organizations in identifying and responding to security risks. By combining data from various sources, including network devices, servers, and applications, they can provide a comprehensive overview of an organization's security posture.