Published on November 8th, 2021 📆 | 7520 Views ⚑0
4 Benefits of Using AI in Cybersecurity
Cybersecurity best practices are greatly aided by using Artificial Intelligence (AI) and Machine Learning (ML) technology, as shown by this sector’s growth. According to one study, the market for artificial intelligence in cybersecurity is expected to reach $46.3 billion by 2027.
AI drastically improves a business’s cybersecurity posture by applying the technology to help identify, isolate, or remediate potential cyber threats from penetrating a business’s network.
Read more: AI vs Machine Learning: What Are Their Differences & Impacts?
4 Benefits of Using Artificial Intelligence in Cybersecurity
Here are some of the tangible benefits AL/ML brings to cybersecurity:
The technology gets better over time: As AI/ML learns a business network’s behavior and recognizes patterns on the network over time, it becomes more difficult for hackers to penetrate a business’s network.
AI/ML can handle lots of data: NGFW firewalls scan hundreds of thousands of files daily with no degradation of service to the network users.
Faster detection and response time: Using AI/ML software in a firewall and anti-malware on a laptop or desktop is more effective and responsive to threats, limiting the need for human intervention.
Better overall security: AI/ML provides protection at the macro and micro levels, making it very difficult for malware to penetrate a business network. This frees up IT teams to deal with more complex threats, improving overall security posture.
How AI Stops Cyber Attacks
Artificial Intelligence helps cybersecurity at the macro and micro level. From a macro perspective, a good example is how next-generation firewalls (NGFW) protects the enterprise. Embedded ML algorithms detect and block suspicious files without using any type of historical, signature-based database to compare the new cyber threat against.
The ML algorithm is used to detect specific behaviors of a file; if the file meets specific thresholds, the file is isolated and analyzed.
The ML algorithm is used to detect specific behaviors of a file; if the file meets specific thresholds, the file is isolated and analyzed. Each time the ML algorithm is used, the NGFW firewall learns from previous analyzed behaviors and becomes more proficient at detecting suspicious files. In this way, NGFW firewalls don’t use any offline tools that slow down the network throughput, so users don’t experience a lag in network response time.
From the micro, device-level viewpoint, anti-malware software uses detection methodologies based in heuristic analysis. In short, AI identifies potential malware it has never seen before.
Antivirus software works differently. Antivirus software uses signature-based detection, which means it uses a previously identified signature comparison of a known virus in a signature database. If the antivirus software has never seen this virus, the antivirus software will not stop the cyber threat.
Read more: AI Equity in Business Technology
How Does AI Solve Cybersecurity Challenges?
As of June 2020, there were nearly 16 billion recorded breaches. Further, one in about 100 emails is a phishing attempt. AI and machine learning can track thousands of different phishing sources, including the ability to distinguish between deceptive and valid web sites.
With heuristics-based detection on end-user devices, zero day attacks have a difficult time penetrating a business network.
With AI/ML implemented on network devices and heuristics-based detection on end-user devices, zero day attacks have a difficult time penetrating a business network. Another example of this is AI software for facial recognition, which adds another layer of protection to a user’s device — and another layer of protection for the network.
Overall, AI and ML significantly increases the responsiveness and uptime reliability of a business’s network with an approved cybersecurity posture.
Read more: Self-Serve AI Interview with Jonas Ryberg, Pactera Edge
What Are the Concerns About AI in Cybersecurity?
AI is not without limitations. Without extensive and varied data sets for testing, Al can render false positives and incorrect results. If AI blocks a required business file, it can impact business processes and operations. Also, it can be time-intensive acquiring all the data sets to do the thorough testing that allows AI to learn valid anomalies.
AI can also be used by cybercriminals to inject data models that are advantageous to the attacker. Hackers can construct adversarial images to penetrate face ID security and expose a business network to a hacker.
Read more: What Is Adversarial Machine Learning?
What Is the Future for Artificial Intelligence in Cybersecurity?
More advanced AI technologies are being implemented to mitigate attacks. One such tool is Natural Language Processing (NLP). NLP relies on machine learning to develop rules by analyzing example data sets. Another concept born from AI is Deep Learning, which combines AI and ML to gain knowledge the way humans do.
Deep Learning can be used to detect and stop legitimate cyber threats while drastically reducing false positives.
Deep Learning uses neural networks, which is a cache of algorithms modeled to mimic the human brain. The neural network consists of millions of parameters to classify and recognize data sets and patterns. Deep Learning is used in network devices and malware software to detect and stop legitimate cyber threats while drastically reducing false positives.
Natural Language Processing helps systems easily detect and identify spam and other social engineering techniques by learning forms of communication and language patterns.
Artificial Intelligence is the foundation of ML, NLP, and Deep Learning, and they all contribute to improving the cybersecurity posture of the organization. Without a doubt, these tools are essential to protecting businesses going forward.
Read next: AI Software Trends for 2021