Experts accept that Artificial Intelligence (AI) and Machine Learning (ML) have both drawbacks and constructive outcomes on network security. Artificial intelligence algorithms use live data and training data to figure out how to respond to various environments. They learn by repeating and adding additional data as they come. The post highlights some of the interesting areas like the impact, the potential benefits and some of the challenges encountered during the application of AI in cybersecurity.
There is no doubt that AI has had a significant impact in a lot of dimensions around the digital space, including the cybersecurity space. A report by Norton showed that the global cost of typical data breach recovery is $3.86 million. AI, machine learning, and threat intelligence can recognize patterns in data to enable security systems learn from past experience. In addition, AI and machine learning enable companies to reduce incident response times and comply with security best practices.
Some of the areas within cybersecurity that AI has been believed to improve includes threat hunting, vulnerability management, penetration testing and network security to name a few. Conventional security strategies use signatures or indicators of compromise to identify cyber threats. This strategy may function well for recently experienced threats, however they are not convincing enough for the newer threats that have not been found at this point. Replacing traditional techniques with AI can increase the detection rates up to 95%, but you will get an explosion of false positives.
Organizations are struggling to prioritize and manage the large amount of new vulnerabilities they encounter on a daily basis. Conventional vulnerability management techniques will in general trust that attackers will exploit the potential vulnerabilities prior to neutralizing them. While traditional vulnerability databases are critical to manage and contain known vulnerabilities, AI and machine learning techniques like User and Event Behavioral Analytics (UEBA) can examine baseline behavior of user accounts, endpoint and servers, and identify irregular behavior that might indicate a zero-day unknown attack.
Customary network security has two time-intensive aspects, making security policies and understanding the network topography of an organization. Organizations can use AI to improve network security by learning network traffic designs and suggesting both useful gathering of remaining burdens and security strategy.
Some of the limitations and challenges associated with the application AI include but not limited to resources, data sets, the potential access of AI to attackers and use of fuzzing programs. It’s no doubt to state that organizations have to invest a good amount of revenue towards maintaining these technologies. The learning technology of AI from data sets is still in the comprehensive building stage which needs security testers to get hands on these data sets ASAP in order to provide efficient cyber defense. Cyber-attacks have become more too easy to achieve and has been modified to ensure the sophistication levels stay at par. Thanks to automation, AI and ML to have additionally served as platforms to perform such attacks.
It would be recommended to have a heterogeneous mixture of employing both manual intervention and AI. This can bring about 100% discovery rate and limit false positives. Though there are potential flaws with the technology enabling cybercriminals to use AI to cause destruction, getting protection against AI systems will be a paramount factor to decide the path of AI in cyberspace.