In cybersecurity, there’s an urge to collect as much data as possible. Logs, alerts, metrics, everything. But more data doesn’t necessarily translate to better security.

SOCs deal with tens of thousands of alerts every day. It’s more than any person can realistically keep up with. When too much data comes in at once, things get missed. Responses slow down and, over time, the constant pressure can lead to burnout.

According to a Vectra AI survey, 71% of SOC practitioners worry they will miss a real attack buried in a flood of alerts, and 51% believe they cannot keep pace with the increasing number of security threats.

Focus on what matters

Most alerts don’t lead to anything serious. Some are noisy by design, others are badly configured. If you try to treat everything as urgent, you’ll miss what matters.

The trick is to start spotting patterns. Look at what helped in past investigations. Was it a login from an odd location? An admin running commands they normally don’t? A device suddenly reaching out to strange domains?

These are the kinds of details that stand out once you understand what typical system behavior looks like. At first, you won’t. That’s okay. Spend time reading through old incident reports. Watch how the team reacts to real alerts. Learn which ones actually spark investigations and which ones get dismissed without a second glance.

You’ll also notice that some alerts come back again and again. A survey from Devo Technology found that 84% of organizations say their analysts investigate the same incidents multiple times a month without realizing it. This happens because there’s too much noise, too little context, and not enough clarity about what matters.

This is about building judgment by seeing what matters in real life. You’ll start to recognize signals — the small signs that something’s off — and get better at filtering out the rest.

Staying curious helps. Ask why an alert was escalated, what made it important, what was ignored and why. The more you understand the context, the faster you’ll start spotting useful signals yourself.

Good analysts don’t know everything. They just know where to look.

Practicing data hygiene

Start by removing logs and alerts that don’t add value. Many logs are never looked at because they don’t contain useful information. Logs showing every successful login might not help if those logins are normal. Some logs repeat the same information, like system status messages. If a log doesn’t help find or investigate threats, it’s usually okay to stop collecting it.

Next, think about how long to keep different types of logs. Not all logs need to be saved for the same amount of time. Network traffic logs might only be useful for a few days because threats usually show up quickly. But login records or admin actions may need to be kept for months to help with investigations or to meet rules. Work with your team to decide how long each type of data should be kept. This helps keep your systems running smoothly and avoids storing too much data.

If a security incident happens, take time to review which logs and alerts helped find or stop the threat. Look for which sources showed unusual activity and which alerts led to real investigations. Mark these as important so you can pay more attention to them in the future.

Most security tools let you filter alerts or change how important they are. For example, if your system sends many alerts for failed login attempts, you can set it to alert only when there are several failures in a short time instead of every single one. This reduces false alarms and alert fatigue. Take time to learn how to use these features. They will help your team find real problems and avoid distractions.

The role of AI

AI tools are getting better at helping security teams. They can look at large amounts of data and spot patterns that a person might miss.

What AI does well:

• Processes data faster than humans
• Reduces alert fatigue by flagging high-risk patterns
• Finds anomalies that may be missed in manual reviews

What to watch out for:

• AI can still miss or misclassify threats
• Results depend on the quality of input data
• Human oversight and tuning are essential

AI is not magic. It only works well when the input is clean. If you improve the quality of data going in, AI will give you better results. Even small changes you make can improve how it performs.