Implementing artificial intelligence (AI) into your security operations center (SOC) can transform your organization’s ability to respond to threats, reduce the burden on overstretched analysts, and even offer long-term cost-reduction benefits. But what metrics should you track to assess your implementation’s success? Keep reading to find out.
Mean Time to Detect (MTTD)
A SOC’s effectiveness is directly tied to its ability to detect potential security incidents. In cybersecurity, a matter of seconds can be the difference between a blocked threat and a data breach.
An AI-enabled SOC should see a significant reduction in MTTD when compared to your previous SOC iteration. This is because machine learning (ML) algorithms and AI-powered threat intelligence models can analyze patterns and flag anomalies that could indicate a threat much faster than any human analyst could.
You can calculate MTTD by collecting data on all security incidents detected during a defined measurement period, noting the time elapsed between incident occurrence and detection, adding up the detection times, and dividing by the total number of incidents. Here’s the formula:
You should calculate MTTD pre- and post-AI implementation and compare the two to ensure your implementation is effective.
Mean Time to Respond (MTTR)
SOCs should also be able to respond to security incidents quickly. It’s no use detecting a threat immediately if it takes an age to contain and remediate it – the longer you take to respond to a threat, the more damage it can cause.
AI-powered tools like automated playbooks and incident response platforms can significantly reduce MTTR in SOCs by suggesting or executing actions such as isolating affected systems, blocking malicious IPs, or applying patches.
To calculate MTTR, collect data from all resolved security incidents within a defined period, note the elapsed time between when the SOC first identified the incident and when the SOC considers the incident fully remediated, sum those response times, and divide by the total number of incidents. The formula is as follows:
By comparing MTTR before and after implementing AI, you can assess whether automation is effectively expediting response efforts.
False Positive Rate (FPR)
False positives plague SOC analysts, taking up valuable time and potentially resulting in critical threats being overlooked. Research published in Security Intelligence even revealed that SOC members spend nearly 32% of their day investigating incidents that don’t pose a real threat to the business.
AI can help reduce false positives by refining detection algorithms and cross-referencing historical attack patterns. This allows analysts to spend more time focusing on genuine threats and more complicated technical tasks.
Tracking FPR will help you determine the accuracy of your AI systems. If your implementation is working properly, your FPR should be lower than it was before you introduced AI into your SOC.
To measure FPR, you need to log and classify all triggered alerts as true negatives (legitimate events correctly identified as such) or false positives, add them up, and divide the sum by the number of false positives. Tools like SIEM platforms or incident management systems can help you log false positives. Again, here’s the formula for your reference:
Alert Fatigue Reduction
Modern SOCs handle an extraordinary number of alerts, many of them false positives, as mentioned above. Investigating all these alerts can result in SOCs suffering from alert fatigue, whereby analysts burn out and potentially miss threats. AI can help SOC analysts overcome alert fatigue by prioritizing alerts – identifying the highest-risk incidents that require immediate attention – and streamlining the investigation process.
To track alert fatigue reduction, you can use a combination of qualitative and quantitative metrics. The best way to find out if SOC staff have experienced a reduction in alert fatigue is, quite simply, to ask them. Overall, the alert volume should also decrease after AI implementation.
User Feedback and Satisfaction
Similarly, you can collect feedback from your staff to determine the overall effectiveness of your AI implementation. While quantitative metrics like those listed above are valuable, it’s important that you listen to SOC analysts and get their view of the implementation. Ask questions like:
- Do you feel more productive after the AI implementation?
- Were you happier in your role before or after AI implementation?
- How well do you feel you have adapted to the implementation?
Cost Savings
Implementing AI into your SOC can bring massive cost savings – but they won’t be immediate. Over time, monitor metrics like reductions in labor hours, third-party services, or incident recovery costs and weigh them against your previous and implementation costs to measure ROI.
Justifying Your Implementation
As a SOC manager, you’ll know how important it is to justify any investment into cybersecurity to the board. Measuring these metrics will help you do just that. Granted, you will have done the hard work by convincing decision-makers to grant you the budget necessary for AI implementation, but showing just how much that money has helped achieve will help you secure further investment in the future.
About the author
Josh is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.