Artificial intelligence is not a brand-new concept by now. The term was coined as far back as 1955 when computer scientists Herbert Simon and Allen Newell developed the first AI program. This would pave the way for many studies and improvements that would result in what AI is now.
Nowadays, artificial intelligence is used in many areas, including especially in the cyber security industry. In fact, in Cisco’s 2018 cybersecurity report, it’s been found that a third of CISOs have adopted artificial intelligence to improve the effectiveness of their cybersecurity strategies. Many of these leaders have admitted to relying on AI technology to protect their networks and sensitive data.
The adoption of said technology is seen as the logical next step. As technology evolves, so does threats against it, specifically dangerous malware. Hackers have learned to hide their malware better, so far as making them capable of altering their own code that makes it impossible for older technology to detect. But AI-based cybersecurity tech has proven to be effective against them. They can identify these malicious behaviors within network traffic, files, and websites in a network. But while AI algorithms can only determine what they’re trained to identify, combining another type of AI called machine learning opens up new opportunities for the AI system to grow and adapt over time.
Despite the positive results and predictions, a fully AI-automated system is not infallible. Plus, some analysts worry about being replaced by AI machines. But can you take out the human element in cyber security? Many would tell you no because as promising as artificial intelligence and machine learning is, it still has its limitations that require human ingenuity to solve.
Limitations of AI
There are some limitations of cyber threat hunting technology that prevents removing human security analysts from the overall picture of automated cyber security.
One case that would still require reliance on human analysts are false positive alerts. Even with the utilization of cutting-edge technology that has undergone many upgrades and refreshes, analysts still encounter a stream of false positive alerts with traditional security analysis. With this type of situation, security analysts can look at these alerts in ways beyond simple machine logic to determine whether they are worth investigating or not. Before going fully automated in cyber threat hunting should first consider the business aspect of false positives.
Aside from false positives, there are other risks such as inversion attacks, biases, and model drift. But beyond those, AI/machine learning solutions are black boxes, meaning they may protect against attacks, but AI/ML vendors don’t explain what they’re doing or why, leaving organizations at the mercy of the product and vendor. Threat hunting is about detecting malicious activity and identifying weaknesses, and it’s the hunters, more often than not, that find the new vulnerabilities in the environment.
And there’s the fact that security experts aren’t the only ones using artificial intelligence, cyber criminals as well. They can use AI-driven cyber security solutions and test their malware on them, resulting in them creating an AI-proof malware strain.
How AI Can Help in Cyber Threat Hunting
Still, artificial intelligence is an excellent tool for security analysts for improved cyber threat hunting strategies. While AI can’t do anything that humans cannot, it can do things faster. What artificial intelligence aims to do should be to help threat hunters do tasks in a quick and efficient manner. Therein lies the most important benefit of using AI in threat hunting.
AI can handle large amounts of data that would otherwise be time-consuming when done traditionally with human analysts. It automates the process of detecting threats by being able to scan a large number of emails, files, and websites accessed by users within a network in a short amount of time. Even if it’s not 100% accurate, it can minimize the samples by eliminating the benign, which lets security analysts focus on the small number of remainders that may be potentially dangerous.
Coupled with machine learning, AI can then learn to identify malicious attacks based on the behavior of the network as a whole. AI-powered threat hunting can then spot deviations from the usual network activity.
Additionally, time-consuming menial tasks such as alerting on pre-defined detections from previous hunts, gathering log files and producing reports based on identified activity can also be done by AI. This gives threat hunters more time to analyze malicious threats and prepare for zero-day attacks by new, unidentified anomalies.
Upgrade Your NDR with Artificial Intelligence
The cyber security experts at Sangfor recognize the importance of an intelligent threat detection and response system. Thus, the creation of Sangfor Cyber Command, an AI and ML-powered cyber threat hunting solution.
Cyber Command makes use of AI/ML and behavior analysis and aided by global threat intelligence for advanced detection and hunting capabilities, all done in a fast and efficient manner. Plus, its unique Golden Eye feature studies the behavior of compromised assets to strengthen external and internal system defenses, simplifying the threat hunting process for future attacks.
Prioritize your organization’s safety with Sangfor Cyber command.