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.
How Can You Use AI in Threat Hunting and Analysis?
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.
Why Should You Use AI to Automate Threat Hunting?
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:
- Rapid processing speed: Even if you’re a seasoned professional, you can’t compete with the unmatched processing speed of AI. It can absorb massive data sets in seconds, turning an all-day duty into a quick task.
- Consistent accuracy: For almost half of all IT professionals, over 40% of alerts are false positives. They often spend 20% of their workday chasing these mis-directions. AI can organize reported threats for you, ensuring you only get genuine notifications.
- Unparalleled pattern detection: Since this process often involves unknown threats, knowing what to look for can be challenging. Fortunately, AI can quickly detect patterns humans can’t.
- Task automation: Workplaces overwhelm almost 90% of workers with many repetitive, tedious tasks. With AI, you can enjoy your free time or move on to more critical duties.
- Constant adaptation: The cybersecurity landscape constantly shifts, so it can be hard to predict the next threat. However, since machine learning models can continuously adapt, they can remain one step ahead of attackers.
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.
Tips for Using AI to Analyze and Hunt Cyber Threats
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.
1. Leverage Predictive Analytics
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.
2. Act in a Supporting Role
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.
3. Reduce Bias and Manipulation
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.
4. Leverage Generative AI
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.
Threat Hunting Has Never Been Easier
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.