Unlike other modern technologies, artificial intelligence (AI) is autonomous and adaptive — meaning you can use it to automate your threat-hunting process. Instead of spending all day on mundane, time-consuming tasks, you can fast-track your analysis and investigation.
Traditional threat hunting is a tedious process. Manually analyzing logs, reviewing alerts and identifying indicators of compromise is incredibly time-consuming. However, if you automate with AI, you can free up your time and focus on more critical matters.
You can trigger search processes with AI or detect hidden threats while analyzing. Since it works in real time, it can even send you alerts as soon as it identifies something. While the software autonomously reviews logs and tries to identify patterns in the attacker’s tactics, you can focus on the in-depth parts of your investigation.
You should consider automating your threat hunting and analysis process because there are virtually no downsides to doing so. You become more productive, reduce your workload and improve your organization’s cybersecurity without lifting a finger.
Here are the main benefits of automating threat hunting with AI:
If you’re like most people in the industry, you deal with near-constant alerts and emerging threats. If you want to keep up with everything and maintain cybersecurity, AI is one of the best tools at your disposal.
While AI-powered automatic threat hunting and analysis is relatively straightforward, there are some things you may overlook at first. Here are some important considerations to keep in mind.
Predictive analytics is one of the most popular AI applications of 2023 because it’s incredibly practical. You can feed your model historical and current data sets to get an idea of what you should look for. It can dramatically improve your investigation’s pace and accuracy. After all, the entire point of threat hunting and analysis is to be proactive.
Although most technologies can’t match your AI model’s complex analysis and processing capabilities, it still needs human support. Even if it can technically perform without your intervention, it will be much more accurate and precise if you step in. After all, algorithms can’t think critically or possess abstract thoughts.
Although AI outperforms most other technologies and functions well even under extreme operating parameters, it may encounter an issue it can’t overcome on its own. It’s best to let it take over the repetitive, mundane tasks — like evidence gathering or pattern analysis — and help it with complex investigation responsibilities.
Even if you’re sure your initial training data was bias-free, there’s always a chance of manipulation down the line. In fact, you could poison today’s most popular data sets. It only costs $60 to manipulate 0.01% of COYO-700M or LAION-400M — some of the most extensive and widely used ones in existence.
A 0.01% impact may seem insignificant, but even a inor percentage of poisoned information can permanently impact algorithms. To prevent this, only train your model on secure data sets. If you want to use an open-source resource, make sure it uses cryptographic hash functions or some other method to prove data integrity.
Before using your AI to hunt cyber threats, consider training it with synthetic data. An artificial data set is ideal since there are very few accurate, valuable details on unknown threats. Generative models can produce relevant, realistic information. For example, they could create potential use cases, limitations and identifying signs for an emerging malware.
Alternatively, you can use generative AI to streamline your daily duties. If you enter the right prompt, you can get it to automate your workflows. The process takes seconds and could save you hours, so it’s a valuable use of your resources.
Automated threat hunting is faster and more accurate than the manual method. Instead of spending all of your time sifting through alerts and false positives, you can streamline your entire process.