Key Cybersecurity Metric Concepts

brencronin
May 15
12 min read

The Importance, and Challenge of Cybersecurity Metrics

Metrics are foundational to driving and refining business processes, and cybersecurity is no exception. However, the cybersecurity industry continues to struggle with developing effective metrics due to several persistent challenges. These include limited or inaccurate data, low confidence in measurement accuracy, the time-intensive nature of producing metrics, metrics that unintentionally drive counterproductive behaviors, and the constantly evolving IT landscape, such as the emergence of cloud computing and AI, introducing new and shifting risk profiles.

This article aims to explore practical frameworks for defining and using effective cybersecurity metrics.

At a high level, cybersecurity metrics can be grouped into four primary categories:

Efficiency Metrics – Measure how well cybersecurity operations are functioning in terms of speed, resource use, and consistency (e.g., mean time to detect/respond).
Implementation Metrics – Evaluate whether security controls are deployed properly and functioning as intended (e.g., patch coverage, MFA adoption rate).
Impact Metrics – Capture the outcomes of cybersecurity efforts, such as incidents avoided, cost savings from risk mitigation, or reductions in reputational damage.
Proxy Metrics – Indirect indicators that suggest trends or conditions relevant to cybersecurity (e.g., phishing click rate as a proxy for user awareness).

Why It’s Critical to Understand Different Types of Cybersecurity Metrics

Understanding cybersecurity metrics can be confusing, especially when acronyms like KPI, KRI, and KCI are used interchangeably or incorrectly. Yet, clearly distinguishing between these metric types is essential for accurately measuring performance, risk, and control effectiveness.

Here are the key metric types commonly used in cybersecurity:

KPI – Key Performance Indicator: Measures operational performance or efficiency (e.g., average time to patch vulnerabilities).
KRI – Key Risk Indicator: Highlights areas of potential or emerging risk (e.g., number of unpatched critical systems).
KCI – Key Control Indicator: Assesses whether specific controls are implemented and functioning correctly (e.g., percentage of users with MFA enabled).

In practice, many cybersecurity programs inadvertently group all metrics under the "KPI" label, even when they’re better classified as KRIs or KCIs. This lack of clarity can lead to misaligned priorities, ineffective reporting, and poor decision-making.

A mature cybersecurity program should define and use each of these metric types appropriately. They serve different, but complementary, purposes:

KPIs track how well the cybersecurity function is operating.
KRIs identify where the organization is most vulnerable or exposed.
KCIs validate that protective controls are in place and working.

Understanding and applying these distinctions ensures that metrics are meaningful, actionable, and aligned with organizational goals.

Metric Best Practices and Common Pitfalls

Before diving into specific cybersecurity metrics, it’s essential to understand both the best practices and potential pitfalls of using metrics. When poorly designed or misused, metrics can do more harm than good, wasting time, driving bad behaviors, or obscuring the real story.

Best Practices for Cybersecurity Metrics

1. Understand What You’re Measuring and Why

The first and most important step is to ensure the metric is meaningful. Ask: What is the business outcome this metric supports? Metrics should trace directly back to business objectives. Start with:

Business Goals: What are we trying to achieve?
Business Implications: What risks or priorities relate to those goals?
Cyber Policies/Standards: What are we requiring to mitigate those risks?
Metrics: How will we measure our performance against those requirements?

Metrics are not just measurements, they are decision-making tools.

2. Use the GQMR Method (Goal–Question–Metric–Refinement)

A proven framework for metric design is the GQMR model:

Goal: What are you trying to achieve? (e.g., Reduce unauthorized access incidents)
Question: What do you need to ask to assess that goal? (e.g., How many unauthorized access attempts occur per month?)
Metric: What can be measured to answer the question? (e.g., Number of access violations logged per month)
Refinement: Could the metric be misleading? (e.g., Does an increase mean more attempts or better detection?)

3. Use Metrics Comparatively

Metrics become most useful when placed in context. Compare:

Current vs. Past: Are things improving or deteriorating?
Current vs. Target: Are we meeting expectations?
Current vs. Peers/Benchmarks: How do we compare externally?

These comparisons help identify trends, gaps, and areas needing attention.

4. Prioritize Simplicity and Feasibility

A good metric must be easy to collect, interpret, and act on. Consider:

How easily can this data be captured and visualized?
Does the cost (time and resources) of gathering this data outweigh its benefit?
Is it worth measuring manually, or should it be automated?

Align metrics with existing cybersecurity frameworks like CIS Controls or NIST CSF to ensure completeness and relevance.

Common Pitfalls and “Gotchas” in Cybersecurity Metrics

1. Metrics That Are Feared or Weaponized

Metrics should inform, not intimidate. If a metric is used to assign blame or punish, teams will often game the system. For example:

Call Center Trap: If agents are judged solely on ticket closure time, they may falsely categorize tickets as “waiting on customer” or close and reopen tickets to reset the clock, misleading data that undermines real performance and degrades customer experience.

2. Misleading or Context-Free Metrics

Metrics like “number of firewall rules” or “number of threat alerts” can be misleading without context. Ask:

Is the metric actionable?
Does it reflect improvement or degradation?
Could it be misinterpreted?

Avoid metrics that are only meaningful if they are 100% or 0%, as they rarely reflect reality and tend to penalize incremental progress.

3. The Illusion of Measurement: Pointless Metrics

Some metrics measure activity, not outcome. For instance:

Number of alerts reviewed ≠ improvement in detection
Number of blocked threats ≠ reduction in risk

Focus on metrics that reflect value, not just volume.

The Economics of Bad Metrics: Goodhart’s and Campbell’s Laws

Goodhart’s Law:

“When a measure becomes a target, it ceases to be a good measure.”

Overemphasis on metrics leads to behavior that optimizes for the metric, not the outcome—often resulting in unintended consequences.

Campbell’s Law:

“The more any metric is used for decision-making, the more it will be subject to corruption pressures.”

This explains why overreliance on single metrics can lead to manipulation or distortion—often undermining the original intent.

Building an Effective Cybersecurity Metrics Framework

Too often, organizations attempt to “boil the ocean” when it comes to cybersecurity metrics, spending years building overly complex systems that ultimately produce little value. The key to success is simplicity: metrics should be easy to collect, meaningful to interpret, and actionable for driving improvements. The goal is not to measure everything, but to measure what matters.

A streamlined approach begins by organizing metrics into a few critical domains. A simplified structure might include:

Security Solution Coverage Metrics
Vulnerability & Compliance Management Metrics
Identity Security Metrics
Incident Detection & Response Metrics

Security Solution Coverage Metrics

This category begins with foundational asset awareness: knowing what hardware and software exists in your environment. That’s easier said than done, organizations often face the “unknown unknowns” problem. After all, how can you measure assets that you don’t know exist?

The solution: shift the metric toward coverage, e.g., the percentage of devices known and tracked in your asset management system, rather than assuming full visibility.

Other valuable metrics in this category include:

% of Devices Managed via MDM: Measures how many devices are enrolled in Mobile Device Management platforms, which enforce security policies beyond simple asset tracking.
% of Systems Protected with EDR: Indicates endpoint visibility and protection coverage through Endpoint Detection and Response tools.
% of Systems Sending Log Telemetry: Reflects how many systems are producing actionable security logs, critical for detection and investigation.
% of Systems Covered by Network Detection and Response (NDR): Demonstrates whether systems and subnets are being monitored for network-based threats.
% of Systems Assessed for Vulnerabilities: Not measuring what vulnerabilities exist, but whether a system is actively scanned and assessed.
% of Systems Evaluated for Secure Configuration (Compliance): Tracks whether systems are checked for configuration baselines such as CIS Benchmarks or STIGs.

By starting with core metrics across these categories, organizations can establish a focused, scalable cyber metrics framework. These metrics offer early insights into posture, highlight coverage gaps, and guide decision-makers toward more informed, risk-based improvements.

Vulnerability & Compliance Management Metrics

Vulnerability and compliance metrics are foundational to any cybersecurity program. Fortunately, many organizations already deploy vulnerability and compliance management tools that generate useful tracking data. However, while the data is often available, it's frequently misunderstood or misused.

Rethinking Common Vulnerability Metrics

A typical starting point in vulnerability tracking is counting the number of vulnerabilities:

Total number of vulnerabilities
Count of critical/high vulnerabilities

While intuitive, these counts can be misleading. Many tools assign default scores based on the CVSS base score, which does not account for your organization’s specific environment or threat exposure. Simply tracking critical vulnerability counts doesn’t accurately reflect real risk.

Instead, organizations should focus on context-aware metrics, such as:

Vulnerabilities on the Known Exploited Vulnerabilities (KEV) list
Vulnerabilities with a high Vulnerability Priority Rating (VPR) (a risk-based score offered by some vulnerability management platforms)

These are more meaningful because they account for factors like exploitability, asset importance, and active threat intelligence.

Moving Beyond Counts: Measuring Responsiveness

In addition to “how many” vulnerabilities exist, you should also measure how effectively you respond to them. One commonly used metric is Mean Time to Remediate (MTTR), but this term is often misunderstood or misapplied.

Clarifying MTTR (and Its Many Interpretations)

MTTR is a heavily overloaded term in cybersecurity, depending on the context:

In vulnerability management: Mean Time to Remediate (e.g., how long it takes to patch or mitigate a vulnerability)
In network operations: Mean Time to Repair
In incident response: Could mean Mean Time to Respond, Recover, or Resolve

This ambiguity can lead to miscommunication across teams and confusion in reporting. To improve clarity, consider using more precise terminology:

Context	Preferred Metric Name	Description
Vulnerability Management	MTTP (Mean Time to Patch)	Time from detection to patching
	MTTM (Mean Time to Mitigate)	Time to implement a mitigation control
	MOVA (Mean Open Vulnerability Age)	Average age of vulnerabilities
Incident Response	MTTA (Mean Time to Address)	Time to begin triage after detection
	MTTD (Mean Time to Detect)	Time from incident start to detection
	MTTC (Mean Time to Contain)	How long it took to contain the incident
	MTTR (Mean Time to Recover)	Time to full service recovery

Why Vulnerability Age is a Critical Metric

Whether you use MTTR (Mean Time to Remediate) or opt for more precise terms like MTTP (Mean Time to Patch) or MTTM (Mean Time to Mitigate), there's a hidden pitfall in relying solely on these metrics for measuring vulnerability management performance.

The core issue lies in the principle that the longer a vulnerability remains unpatched, the longer your systems are exposed to potential exploitation. This makes time-based metrics critical, but they must be interpreted carefully.

The Pitfall of Mean Time Metrics Alone

Let’s say your team prioritizes patching newer vulnerabilities first. This approach might cause your MTTR/MTTP/MTTM to look better (i.e., trend downward), but older, potentially more dangerous vulnerabilities may remain untouched, leaving a false sense of security.

This is why it’s important to supplement MTTR-like metrics with another, often overlooked indicator: MOVA.

What Is MOVA?

MOVA stands for Mean Open Vulnerability Age. It measures the average age of unresolved vulnerabilities from the moment they are first detected. In other words, it answers the question:

"How long, on average, have our open vulnerabilities been sitting unaddressed?"

Tracking MOVA helps identify if your organization is accumulating risk by allowing older vulnerabilities to persist. A decreasing MOVA trend is a strong indicator that you're not just patching fast—you’re patching smart by addressing your backlog.

This concept is well illustrated in the Balbix article, “Measure What Matters: Why MTTR is an Incomplete Cybersecurity Metric and What You Can Do About It.”

MTTR vs. MOVA: Behavior Scenarios

Remediation Approach	MTTR / MTTP / MTTM Trend	MOVA Trend	Interpretation
Patch oldest vulnerabilities first	Increases at first, then declines	Declines	You're clearing backlog and improving long-term posture
Patch only newest vulnerabilities	Declines	Increases	Surface metrics look good, but backlog is growing
Patch only recent critical vulns	Declines	Increases	You're addressing high severity, but aging risk remains
Patch both new and old efficiently	Declines	Declines	Optimal: backlog and exposure are both under control

Compliance Metrics

Compliance metrics often overlap with vulnerability metrics but focus more on configuration standards and policy adherence. Examples include:

Percentage of systems assessed for secure configuration
Compliance drift over time
Audit coverage rates by system category

When possible, track compliance status by asset criticality. A non-compliant setting on a low-risk device may not pose the same risk as the same issue on a high-value system.

Identity Security Metrics

Identity management is a cornerstone of cybersecurity, so much so that it warrants its own category of metrics. With most modern attacks involving compromised credentials, weak identity controls often become the easiest path for threat actors.

It's essential to consider not only "identity at rest" (stored identities and their configurations) but also "identity in transit", when identities are actively being used for authentication and access. Many organizations have strong controls for identity at rest but lack sufficient safeguards for how identities are used dynamically in day-to-day operations.

Key Identity Security Metrics

Here are some practical metrics to evaluate the security posture of your identity and access management (IAM) program:

% of privileged users with separate administrative accounts
- Target: 100%
- Users with elevated access should never use their regular accounts for privileged tasks.
% of privileged activities performed using non-privileged accounts
- Target: 0%
- This metric catches policy violations where admin tasks are performed with standard accounts.
% of authentications using generic or break-glass accounts (e.g., root, admin)
- Target: 0%
- Generic accounts should only be used in emergencies, and usage should be tightly monitored and audited.
Number of orphaned accounts
- Accounts without an active owner pose a major risk and should be regularly reviewed and removed.
% of applicable systems protected by a Local Administrator Password Solution (LAPS)
- This helps ensure that local admin credentials are rotated, unique, and securely stored.

Operational vs. Security Identity Metrics

Other IAM-related metrics, such as onboarding/offboarding time, frequency of password resets, or service ticket volume, are useful for IT operations but are less impactful from a security risk perspective. When building your cyber metrics dashboard, it’s important to differentiate between operational efficiency and security effectiveness.

Incident Detection & Response Metrics

When evaluating your organization's cybersecurity readiness, it’s critical to distinguish between incident detection and incident response. While both are related to managing cyber threats, they are distinct disciplines that require separate metrics and ownership.

Often, these functions are mistakenly seen as solely the responsibility of the SOC. In reality:

Detection involves ensuring the environment is instrumented properly with telemetry, log collection, and detection rules. This is typically a shared responsibility across system administrators, security engineers, and tool owners.
Response involves the SOC (or IR team) investigating and taking action on alerts, guided by documented procedures and playbooks.

Incident Detection Metrics

These metrics assess how well threats are being observed across the environment:

Detection Coverage Across MITRE ATT&CK
- % of MITRE ATT&CK Tactics/Techniques with at least one mapped detection.
Detection Coverage of Organizational Threat Profile
- % of known, relevant attacker behaviors (e.g., from threat intel or red team exercises) covered by detection logic.
Detection-to-Response Coverage During Exercises
- % of simulated red team techniques that triggered actionable alerts and were responded to.
False Positive Rate
- High false positive (FP) rates waste analyst time and lead to alert fatigue. This metric should trend downward as detections are tuned and instrumentation matures.
Telemetry and Data Feed Health
- % of required logging sources reporting into SIEM/XDR. Poor or missing telemetry undermines detection fidelity.

Tip: Track the time delta between when malicious activity occurred and when an alert is received by the SOC. Some tools, especially cloud-native ones, perform delayed analysis on large datasets. These delays can lead to confusion or incorrect assumptions about SOC performance when, in fact, the alert simply wasn't received in near-real time.

Incident Response (IR) Metrics

These metrics evaluate how effectively and efficiently your team responds once a threat is detected:

Mean Time to Contain (MTTC)
- The most important IR metric—how quickly the team can isolate or neutralize a threat after detection.
Mean Time to Address (MTTA)
- How long it takes for an analyst to pick up and begin working an incident after alert generation.
Time to Block
- Duration between detection and preventive action (e.g., blocking IPs, disabling accounts, isolating hosts).
Time to Investigate an IOC
- Efficiency metric that reflects how quickly analysts can triage or rule out indicators of compromise.
Audit Trail Review Time
- Time taken to review logs of privileged user actions. Helps validate administrative behavior and detect abuse.
# of Procedure Deviations
- Number of times SOC analysts deviated from documented response playbooks—can indicate gaps in training or process usability.

Beware of Misleading or Abused Metrics

Some commonly used IR metrics can incentivize bad behavior if misapplied:

MTTR (Mean Time to Remediate/Respond/Recover)
- Vague and often overloaded term. Define precisely or use more specific alternatives like MTTC, MTTA, etc.
# of Incidents Handled / # of Alerts Processed
- While useful for measuring workload, these can be gamed. Analysts may prioritize easy alerts or prematurely close incidents to improve metrics.
# of Data Feeds / # of SIEM Rules
- Can indicate tooling coverage but do not inherently reflect security value or effectiveness.

Metrics should support truth-seeking, not vanity. Avoid weaponizing metrics that pressure teams into manipulating outcomes. Focus on those that improve situational awareness, promote transparency, and drive corrective action.

Cyber Metrics Final Thought

Metrics should act as a compass, not a scoreboard. The best metrics inform decisions, guide improvements, and highlight where interventions are needed, not where blame should fall. Design with care, question often, and always align to business outcomes.

Raw volume-based metrics don't express reliability, user impact, or value delivered, which can create a disconnect between the SOC and business stakeholders. Just as Site Reliability Engineering (SRE) best practices aim to make software systems reliable and resilient, modern SOCs are adopting the same SRE approaches to security metrics. This includes:

Detection reliability (how often does the SOC detect what it should?)
Response timeliness (how fast does the SOC respond to real threats?)
Tool uptime and telemetry completeness (are log sources healthy and consistent?)

SLIs and SLOs Promote Outcome-Driven Operations

SLIs (Service Level Indicators) are measurable signals of how a service is performing (e.g., time to detect, percent of critical alerts acknowledged within 5 minutes).
SLOs (Service Level Objectives) define the target goals for those indicators (e.g., 99.9% of critical alerts must be acknowledged within 5 minutes over a 30-day window).
This approach focuses on service quality, not just activity, aligning SOC success with user and business needs.

Example SOC SLIs/SLOs:

SLI	SLO
% of critical alerts acknowledged within X minutes	≥ 99.5% per month
% of log sources reporting telemetry without gaps	≥ 99% per day
% of P1 incidents contained within 1 hour	≥ 95% per quarter
% of false positives for high-fidelity alerts	≤ 5% monthly

SOCs adopting SRE-style metrics are maturing from reactive alert factories into reliability-focused, outcome-oriented teams that treat detection and response as services, held to measurable, trackable standards. This evolution brings security closer to the modern engineering and business mindset, making it more agile, transparent, and effective.