Alert Tuning Best Practices for Security Operations (SOC)

Introduction: Why Alert Tuning Matters in Today’s SOC

Security teams face unprecedented challenges in keeping up with the pace and complexity of modern cyber threats. As organizations deploy advanced detection tools, the volume of security alerts has skyrocketed, often overwhelming Security Operations Centers (SOCs). The result? Alert fatigue, missed threats, and inefficient use of analyst time. Effective alert tuning is essential for maintaining strong threat coverage while reducing noise and operational risk.

This guide shares actionable alert tuning best practices for SOC teams, focusing on how to use data-driven methods to systematically improve detection logic, minimize alert fatigue, and maximize security outcomes.

What Is Alert Tuning and Why Is It Critical?

Alert tuning is the process of adjusting detection rules and thresholds in your SIEM or security tools to reduce false positives and optimize analyst workload. Without regular tuning, SOC teams risk drowning in unactioned alerts or, worse, missing genuine threats due to information overload.

Key Benefits of Alert Tuning:

• Reduces false positives and alert fatigue
• Improves true positive rates and detection efficacy
• Enables analysts to focus on real threats
• Supports continuous improvement of security operations

Alert Tuning Best Practices

Step 1: Understand your alert universe

The first step to make better alert tuning decisions is to understand what’s consuming your team’s time and mental capacity, and weighing that against detection accuracy. At a high level, you’re trying to minimize the amount of time spent on detections that aren’t providing direct value to the team.

Collect metadata around your alerts that have been dispositioned over the last 90 days. This may be stored within a SIEM, case management solution, or individual security tools. 

Specifically:

1. Alert Name - What’s the name of the alert?
2. Alert Source - What tool or data source was used to generate this activity?
3. Alert Count - How many times did an alert fire?
4. Total Time Investigated - What’s the sum of time spent (from when the alert was acknowledged to resolved) on this alert?
5. Median Investigation Time - What’s the median time to investigate this alert? (Median metrics by nature are more resilient to outliers and will provide a more accurate picture of cognitive load)‍
6. Efficacy - What percentage of these alerts were true positives? (True Positive / Total Alert Count)

Understand that these 6 fields are just starting dimensions that you might expand upon, and it might not be easy to collect all of these metrics. As an example, gathering “Total Time Investigated” might be challenging for small teams that don’t retain a record of when triage started on an alert. In that case, you might use the time elapsed between “Alert Created” to “Alert Closed”, which most teams should be able to derive, to achieve a similar effect – with the caveat that your data will be skewed by Dwell Time.

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Step 2: Prioritize tuning actions based on alert analysis

Patterns become more apparent in this dataset when plotting the records on a chart. For this framework, I recommend using “Efficacy” as the Y-axis, “Total Time Investigated” as the X-axis, and scaling the data point size based on “Alert Count”.

In the event you don’t have your own data visualization tool like a SIEM, we’ve gone ahead and made a simple Python script to produce the chart like the one below.

After reviewing the visualization, you’ll notice a few patterns start to emerge:

1. The top half of the chart are your “winners” – detections that successfully identified a threat. Alerts in the upper right quadrant typically represent long-standing incidents that required extensive scoping following a malicious determination or might signal training opportunities if the “median investigation time” is significantly higher than others for a given data point. High “median investigation times” are good indicators for cognitive load, and you may be able to improve the outcome of a detection by drafting more complete playbooks or investigative steps to better support other analysts. Best practice would be for rule authors to provide next investigative steps in any custom detection your team builds to reduce this problem.
   
   
2. The bottom half of the charts are your “losers”, or detections that rarely identified a threat. Alerts in the lower right quadrant represent your most impactful false positive tuning opportunities. These alerts represent a dangerous recipe for alert fatigue: high median investigation time (cognitive load), low efficacy, and high total investigative time spent. Prioritize these.
   
   
3. The left side of the chart often highlights areas where simple automation may be able to ease workload. Humans are quickly able to make an accurate decision based on the information at hand, and a few programmatically added steps may significantly increase your response time (and eliminate manual review).

For tuning purposes, prioritize the top few alerts in the bottom right quadrant – especially those with high median investigation times – and proceed to the next step.

Step 3: Take action with second-order questions

Visualizing your alert management highlights the sore spots that impact daily analysis, but it won’t clearly articulate what action should be taken next.

For next steps, you should investigate the individual alerts composing a data point to deepen your understanding of the issue. Each alert may have its nuances, but ask these questions consistently:

1. Has there ever been a true positive for this alert?
   This should give you a sense of what yield you’re getting for the time spent investigating.
   
   
2. Can 90% or more of the alert volume be eliminated with a simple adjustment of rule logic?
   This should cover most of your “runaway” alert scenarios where there’s a new rule or faulty rule that exists that’s triggering a small subset of actions in your environment.
   
   
3. Is this detection uniquely capable of identifying a threat?
   It’s important to remember that threats don’t happen in isolation. Threat actors must perform a series of actions in order to infiltrate and achieve their objectives in your environment. As an analyst, ideally you’d want to disrupt an attack as early in the kill chain as possible, but that’s often not feasible given the volume of normal activity that happens in early stages of threat activity. Consider whether other detection signals would identify this type of threat during this part of the investigative lifecycle or shortly thereafter.

If the answer is “no” for all of these, disable the alert. If “yes” was answered for either #2 or #3, I would opt to tune the alert logic and review if the shift was effective during the next tuning review. Ideally for #2 , the goal would be to tune the alert to shift it to the lower left quadrant for automation.

Record your decisions in a standing team document to codify the decision. When possible, it’s best practice to shift alerts that are disabled to an “Informational” type severity for audit purposes. 

When auditing tuning, you’ll want to look at the total alert count for a rule over a 6 month period. A significant drop in alert volume (single digits) might represent vendor logic changes and warrant revisiting production use.

Step 4: Decisions at scale

With an understanding of your current alert management position, you can put requirements around what detections must look like in the SOC in order to be reviewed by the team. This transfers “gut feel” tuning into a system that’s documentable, scalable, and easy to implement across the team.

1. Are we missing coverage for this MITRE Tactic / Technique?
2. Does it meet our efficacy threshold?
3. Does it meet our threshold for maximum cumulative time for false positives?

By answering these questions and regularly performing the tuning process listed above (or your own version of it), you can evaluate historical investigative trends, prioritize addressing most impactful alerts, and make decisions that ensure holistic threat coverage..  

Closing Thoughts

Managing upstream detections is one of the core ways the security teams can combat alert fatigue and threat actors. Following best practices for alert tuning creates a natural balance between over-tuning and under-tuning alert signals so that SOCs can operate effectively, audit changes, and maintain their sanity.

While today alert tuning is a necessity to manage risk (and sanity) for security operations teams, we believe in a future for analysts that eliminates the need for tedious and repetitive tasks like alert tuning. That’s why we’re building Prophet AI for Security Operations to triage and investigate every alert on your behalf and avoid managing the upstream alert problem altogether.

Request a demo of Prophet AI to learn how you can triage and investigate security alerts 10 times faster.

Frequently Asked Questions (FAQ)

What is alert tuning in a Security Operations Center (SOC)?

Alert tuning in a SOC is the process of refining detection rules and thresholds within security tools like SIEMs to reduce false positives, minimize alert fatigue, and ensure analysts focus on true threats. Regular alert tuning helps SOC teams maintain high detection efficacy and operational efficiency.

Why is alert tuning important for SOC teams?

Alert tuning is crucial because it prevents analysts from being overwhelmed by unnecessary or irrelevant alerts. By reducing noise, SOC teams can respond faster to real threats, improve incident response times, and maintain a more effective security posture.

How often should SOC teams perform alert tuning?

SOC teams should review and tune their alerts at least quarterly, or more frequently if there are significant changes in the threat landscape, detection logic, or business operations. Regular tuning ensures that detection rules remain relevant and effective.

What are the best practices for alert tuning?

Best practices for alert tuning include collecting and analyzing alert data, prioritizing high-volume and low-efficacy alerts, disabling or adjusting ineffective rules, documenting changes, and continuously monitoring alert performance. Using a data-driven, repeatable framework helps standardize the process.

Can AI help with alert tuning in SOCs?

Yes, AI and machine learning can assist with alert tuning by automatically analyzing alert patterns, identifying false positives, and recommending rule adjustments. Advanced agentic AI platforms can even automate aspects of alert investigation and triage, further reducing analyst workload.

Further reading

• SOC metrics that matter
• What is MFA fatigue attack?
• Investigating geo-impossible travel alert
• Top 3 scenarios for auto remediation
• Automated incident response: streamlining your SecOps
• Key SOC tools every security operations needs
• Demystifying SOC automation
• Alert triage and investigation in cybersecurity: best practices
• SOC challenges for analysts and managers
• How to investigate Okta alerts
• AI SOC: Key to solving persistent challenges
• AI SOC Analyst: A comprehensive guide
• Will AI replace cybersecurity jobs?
• How to investigate AWS GuardDuty Finding