One of the threads we’ve been picking up is how Artificial Intelligence driven Security Analytics can play into improving the response times and overall efficiency of the Security Operations Center (SOC). Automating Incident Response with machine learning adds enormous value to the SOC. The whole “New Normal” we established last year (which seems a little weird to say, I admit) has already changed how the SOC does what it does. Remote work and distributed teams are where it’s at, and it seems like it’s where we’ll be for a good long time to come. Even with the vaccine rolling out to the general public, the change is probably here to stay.
To be honest, with the massive reduction in commuter traffic, I’m OK with it.
Revisit and Refresh
A quick glance at any cybersecurity headline will show that the number of attacks hasn’t dropped. Worse, this just reflects the ones that have made the news. For every major event that gets written up on one of the security sites or hits an influential blog, there are an untold number more that are remediated without ever hitting the paper, and several times that number that go undetected.
Trying to stay on top of the sheer flood of attacks has been an ongoing challenge for the SOC, which is why we’ve been talking about using artificial intelligence driven security analytics to improve the effectiveness and efficiency of the Security Operations team. A large focus in this effort is reducing the workload the team faces by giving them a clear picture of what’s happening in the environment and offloading whatever is practicable to an automated system. After all, the more the machine takes off their plate, and the clearer the picture of what they’re facing, the more effective the SOC staff can be. Automating Incident Response with machine learning makes all the difference.
Recognize and Respond
There are a number of places where Artificial Intelligence (AI) can make the SecOps team’s life easier, and we’ve talked about them before. First, identifying threats based on risk and highlighting them for the operations team. Second – dragging true positive threats out of the flood of data coming in from across the security stack. And third, giving threats context lets the team react to the most important threats as they happen. AI also gives them the ability to automate a lot of the mundane issues they see come across the screen every day.
That automation plays heavily into the Incident Response part of the SecOps team’s role. A lot of what’s involved in the initial response to an event can be easily automated. Or at least it should be easy to automate. One of the challenges with rule-based automation is getting the rules right. While it’s pretty easy to hit the basics, setting up rules that can handle cases closer to the edge is more difficult. You have to find the balance between rules that catch the bad actors without making them so strict that they interfere with normal operations. After all, there is little the SecOps team likes to hear less than user complaints that they can’t get their job done because of something the SOC controls.
The advantage with automating Incident Response with machine learning is that it can be considerably more flexible than a purely rules-based system. Where rules will have fixed responses that may, or may not, be easily adapted to a changing situation, an Artificial Intelligence driven Security Analytics system can learn and adapt on the fly. Where it starts with a rule, it can then alter and update that rule to meet a changing threat surface, a changing environment, and changing attacker methods.
Learn to Be Flexible
Behind the AI are machine learning algorithms that learn what’s normal in the environment over time. It’s ultimately what a SecOps analyst does with manually configured rules. They see what’s happening. They see the new threats as they evolve. They see how the rules effect security and, nearly as important, effect overall performance, and rewrite them as needed.
Automating Incident Response with machine learning takes a lot of the burden off the Human analysts. The machine can do the same tasks, automate its responses, and keep everything running effectively and smoothly. By lightening the workload, the analysts can focus on the important parts and have confidence that the system has their back.
Watch the Webinar
If you want to know more about how Artificial Intelligence driven Security Analytics automates Incident Response, watch our webinar.
Webinar: Automating Incident Response with Machine Learning