Automating Threat Hunting

Automation is perfect for taking the burden of repetitive and tedious tasks, such as threat
hunting. You now have the option of training your AI to recognize threats for you, instead of
you having to spend all day trying to do the same.

Why use AI for Threat Hunting?

The process of traditional threat hunting is laborious. But, you may free up time and
concentrate on other important tasks if you use AI to automate.
AI can start search operations, and analysis can reveal concealed risks. Once it recognizes
a threat, it can immediately notify you in real-time. You can concentrate on the in-depth
portions of your inquiry while the program independently goes through logs and looks for
trends in the attacker’s strategies

The Benefits of AI

Speed: No matter how experienced an IT professional you are, you cannot match the
processing speed of AI. If you train the AI well enough, it will be able to
instantaneously recognize threats before you do.
Accuracy: A risk in threat hunting is false positives. Inevitably, you are bound to find
such non-threats among the serious issues. AI can be very helpful for conducting
multiple checks and then organizing the results for your consideration.
Pattern Detection: It can be difficult to know what to look for because this process
frequently contains unknown hazards. Thankfully, AI is able to identify patterns faster
than humans.
Task Repetition: The most common use for AI is taking the burden of tedious tasks.
The same is true here, saving critical time for you and letting you focus on the more
important tasks.
Adaption: Given enough time and data, AI can grow indefinitely and into what you
want. The more you expose them to threat hunting, the better they will get.

Utilizing AI Effectively in Threat Hunting

1. Predictive Analytics: Feed your AI old and current sets of data so that it might
recognize future patterns or threats more specifically. It can significantly increase the
accuracy and speed of your investigation as being proactive is the main goal of
danger hunting and analysis.
2. Supportive Abilities: Even though AI performs better than most other technologies
and keeps up well even in the most demanding conditions, it occasionally runs into
problems that it is unable to resolve on its own. It is better to delegate to it the more
difficult investigative duties and let it handle the monotonous, repetitive activities like
gathering evidence or analyzing patterns.
3. Reduce Bias: The problem that you might eventually run into is unintentionally
introducing bias to the AI. Train your model only on secure data sets to avoid this.
Make sure an open-source resource verifies data integrity using cryptographic hash
functions or another technique.
4. Generative AI: Consider training your AI with simulated data before deploying it to
seek down cyber threats. Since accurate, meaningful knowledge about unknown
risks are scarce, an artificial data set would be excellent. Generative models are
capable of producing accurate and pertinent data. They might, for instance, develop
prospective use cases, constraints, and warning indicators for newly discovered
malware.

Automotive threat hunting is the next step in taking you security to the next stage. Utilizing all
the tools available to you is only natural and will surely improve the quality of your work.