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Ask any SOC analyst what keeps them up at night and you will hear the same theme: “Did we spot the breach in time?” Mean Time to Detect (MTTD) is the measure that turns that anxiety into a number. It’s one of the core SOC metrics that tracks the time between the first malicious activity and the moment your SOC knows about it. Cut that lag and you shorten the attacker’s window to move laterally, escalate privileges, or wipe logs. Let it grow and you risk becoming the next headline. This post walks through how seasoned SOCs measure, diagnose, and improve MTTD without adding more manual work.
Mean Time to Detect (MTTD) is a security operations metric measuring the average time between when malicious activity starts and when it is initially detected and alerted by security tools like SIEMs. Traditional definitions calculate MTTD as the interval from initial malicious activity to the moment the first alert fires.
The formula for MTTD = (∑(time when alert fired – time first activity)) ÷ total incidents
In other words, subtract the time the incident began ("Activity Started At") from the time the alert was triggered ("Alerted At") for each incident. Then, add up all those times and divide by the total number of incidents to calculate the average.
Traditional MTTD metrics, based solely on initial alert generation, often misrepresent actual SOC effectiveness. For example, generating an alert on every event would theoretically drive MTTD to zero. However, such an approach overwhelms teams, making the metric meaningless in practical terms. An alert sitting unreviewed for hours significantly diminishes real-world MTTD value.
MTTD should measure meaningful detection which is the moment when a threat is actively recognized and actioned by analysts, not just when an alert appears. Effective MTTD measurement focuses on the entire detection workflow, from alert generation to analyst confirmation.
Lack of complete visibility into environments creates blind spots. Attackers often exploit these gaps to remain undetected for prolonged periods, greatly inflating MTTD.
Using multiple disconnected security tools creates inefficiencies. Analysts waste valuable time switching between interfaces, slowing down the initial triage and subsequent investigation and response process.
Excessive false positives desensitize analysts, causing genuine threats to be overlooked. High false-positive rates could significantly increase actual detection times.
Understaffed or inadequately trained SOC teams struggle to quickly analyze and validate alerts. This inevitably leads to longer MTTD.
AI SOC Analysts significantly improve MTTD by continuously triaging and investigating every alert immediately upon generation. With human-level reasoning, these analysts automatically filter out false positives, prioritize critical threats, and present security teams with a concise, actionable list of incidents. This approach ensures threats are detected and validated instantly, drastically reducing real-world detection times.
AI SOC Analysts automatically gather and present relevant evidence alongside each alert, enabling human analysts to quickly validate their findings. By instantly providing detailed contextual information, AI-driven platforms dramatically cut down the time and effort required for manual verification.
AI SOC Analysts operate continuously around the clock without fatigue, ensuring consistent and reliable performance at machine speed. Unlike human analysts, they never tire, become overwhelmed by alert volumes, or lose efficiency due to context switching. This constant vigilance significantly improves the SOC’s ability to swiftly detect and respond to emerging threats.
Consistently define when detection begins (initial malicious activity or first alert) and when it ends (analyst confirmation of threat). Clear boundaries ensure meaningful and consistent MTTD metrics.
Measure different aspects separately:
Set realistic MTTD targets based on your organization's specific risk tolerance and threat landscape. High-risk industries like finance require significantly lower MTTD than less-sensitive sectors.
Focus detection efforts on techniques that commonly appear early in attacks, such as initial access and persistence, to maximize detection efficiency.
Ensure comprehensive visibility across endpoints, networks, cloud services, and applications. Robust monitoring coverage significantly enhances detection accuracy and reduces MTTD.
Establish feedback loops using incident data and threat intelligence to continuously update and refine detection rules. Regular refinement ensures detection capabilities remain relevant and effective.
Prophet AI addresses the gap between when an alert fires and when a threat is actually understood and actioned. While traditional MTTD metrics stop at alert generation, real detection happens only when analysts have the context they need to validate a threat.
Prophet AI shortens this gap by immediately investigating every alert and surfacing relevant evidence. Instead of waiting for human triage, the system delivers high-confidence findings that reflect true threat activity. This accelerates the path from alert to understanding, giving security teams a more accurate picture of detection performance and reducing the time it takes to catch what matters.
Request a demo of Prophet AI today to see it in action.
Mean Time to Detect (MTTD) in cybersecurity refers to the average amount of time between the start of malicious activity and the moment it is first detected by a security system, typically through an alert. It helps measure how quickly a security operations center (SOC) can identify threats.
To calculate Mean Time to Detect, subtract the time the activity started from the time the alert was generated for each incident. Add those times together and divide by the total number of incidents. The standard formula is:
MTTD = (Σ(Alert Time − Activity Start Time)) ÷ Number of Incidents
This is the traditional method used in most SOCs, though it does not always reflect how quickly a threat is meaningfully understood or actioned.
A good MTTD for most SOC teams falls between 30 minutes and 4 hours, depending on the organization’s risk profile. High-performing teams in critical sectors like finance aim for MTTD under 30 minutes.
Traditional MTTD metrics focus only on when the alert fires, not when the threat is understood. An alert could fire quickly but go unreviewed for hours, giving a false sense of effectiveness. True MTTD should reflect the time to meaningful detection.
AI SOC Analysts improve MTTD by investigating alerts immediately, filtering out false positives, and surfacing evidence with high-confidence findings. This reduces the time between alert generation and threat confirmation.
Common factors that increase MTTD include limited visibility into the environment, too many disconnected tools, alert fatigue from false positives, and understaffed or inexperienced SOC teams.
Wider detection coverage, especially across tactics like initial access and persistence, improves the chances of early detection. However, coverage must be balanced to avoid overwhelming analysts and degrading response speed.
Organizations should segment MTTD by incident type or severity. Measuring MTTD for categories like credential access or lateral movement helps identify gaps in specific parts of the detection strategy.
Ajmal Kohgadai
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As the Director of Product Marketing at Prophet Security, Ajmal drives marketing and growth strategies and helps security professionals see how AI is transforming security operations.
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