Detection Coverage Validation: What Purple Teaming Actually Proves

Key Takeaways

• A red team report only matters if someone maps every missed technique back to a specific, ownable fix.
• Detection coverage should be measured technique by technique against MITRE ATT&CK, not as one vague percentage.
• Purple teaming works because red and blue watch the same screen at the same time, instead of trading a report weeks later.
• Every detection gap needs an owner, a fix date, and a retest, or it just sits in a slide deck.
• Continuous correlation between offense and defense—not a once-a-year exercise—is what actually moves the mean time to detect.

Introduction

A security researcher at a mid-sized fintech company ran a red team test last year and got past every layer without a single alert firing. Nobody noticed until the final report landed on the CISO’s desk. That is the story behind almost every “how did we miss this” conversation in security operations: the tools were there, the logs were there, and still nobody could say for certain what the SOC would actually catch.

This is the exact question that detection coverage validation is built to answer. Not “did we buy the right tools,” but “when someone tries something specific, does it get seen?”

What Purple Teaming Actually Proves

A purple team workflow is not a new team you hire. It is a working session where your offensive testers and your defensive analysts sit in the same room, or on the same call, and run attacks one technique at a time while watching the SIEM together in real time. The red side executes. The blue side checks if anything lit up. If it did, they move to the next technique. If it did not, they stop right there and figure out why, together, before moving on.

That single design choice, watching in real time instead of waiting for a final report, is what makes purple teaming different from a standard red team engagement. A traditional red team stays hidden until the debrief. A purple team exercise trades secrecy for speed because the whole point is to find SOC blind spots while there is still time to fix them in the same session.

Here is what that actually looks like in practice:

• Joining red activity with blue detections. Every technique the red side runs gets tagged with a timestamp and a MITRE ATT&CK ID, so the blue team can search the SIEM for exactly that window and know precisely what they are looking for.
• Streaming red team actions into a SIEM. Some teams automate this further, piping attack telemetry straight into the SIEM as it happens via standardized formats like STIX/TAXII, so there is no delay between action and evidence.
• Correlating red actions with alerts, one-to-one. Every technique run should map to a specific alert, or the specific absence of one, so nobody is left guessing whether a related alert down the line actually came from the test.
• Finding out what your SOC missed, quickly. Instead of a 40-page report two weeks later, the analyst finds out mid-session that a technique produced a log entry but never triggered an alert. That is a very different fix from a technique that produced no log at all.
• Knowing which attacks went undetected, not just which ones fired. The session output should name every technique that produced zero trace anywhere, log, alert, or otherwise, since those are the ones a real attacker would walk straight through.

Using offense to strengthen the SOC only works if the defensive side treats every miss as data, not as a personal failure. The best purple team sessions end with a clear, specific list: this technique fired, this technique logged but did not alert, this technique never showed up anywhere. That list is the actual deliverable. Everything else is commentary.

How to Measure Detection Coverage That Actually Means Something

Here is an uncomfortable number. Academic research analyzing detection rule sets from major commercial platforms found that they cover between 48-55% of MITRE ATT&CK techniques. That number drops to roughly 25-26% once you filter out low-priority rules that teams rarely act on. Separate independent research puts average SIEM detection coverage closer to 21% once cloud, SaaS, and identity techniques are included – a critical gap since modern attacks increasingly target identity-based lateral movement and cloud misconfigurations rather than traditional endpoint exploits, not just endpoint activity.

So when someone tells you their coverage is “good,” ask which techniques, against which tactics, and how recently. Coverage by technique is the only version of this metric worth tracking, and red teaming is how you validate that number instead of just trusting a vendor claim or a rule count. A rule existing in your SIEM is not the same as that rule firing against a real attempt, and the only way to know the difference is to run the attempt and watch what happens.

A few things that make the number honest instead of decorative:

• Validate SIEM detection rules against the offense, not a checklist. A rule that looks correct on paper can fail silently if a log source stops feeding it or a field gets renamed upstream.
• Test EDR coverage with a red team, specifically, since endpoint tools and network tools tend to catch different halves of the kill chain.
• Score detections against red team activity using a simple scale: detected and alerted, detected but not alerted, logged but not correlated, or no visibility at all. That four-way split tells you exactly what kind of fix each gap needs.
• Test detection across the full kill chain, not just initial access. A lot of programs are strong at catching phishing and weak at catching lateral movement, because that is where testing usually stops.
• Prove a detection gap empirically by re-running the exact technique after a fix, in the same environment, so “we think it’s fixed” becomes “we watched it fire.”
• Validate that alerts actually fire, meaning a human or an automated response actually receives them, not just that a rule technically exists somewhere in the SIEM.

One idea to hold onto here: detection coverage is not a static score you calculate once. Cloud environments change weekly. New SaaS tools get connected. Every change is a chance for a previously solid detection to go quiet without anyone noticing, which is exactly why this needs to be a habit, not a project.

Turning Red Team Findings Into a Working Detection Backlog

A red team report that ends up as a PDF nobody opens again is a wasted engagement. The real value only shows up when every miss becomes a ticket with an owner and a date.

Building a detection engineering backlog from red team results starts with sorting gaps into three buckets:

• Logging gap. The event never reached the SIEM in the first place. This usually needs a log source or agent fix, not a new detection rule.
• Rule gap. The data was there, but no rule existed to catch it. This is a detection engineering task.
• Tuning gap. A rule existed, fired too often, and got disabled or filtered out somewhere along the way. This is often the most common gap, and the easiest one to miss, since technically “a detection exists.”

Once gaps are sorted, rank detection gaps by attacker reach, not by how easy they are to fix. A gap in a technique that opens the door to lateral movement across your whole environment deserves attention before a low-severity gap that only affects one edge case. Detection scoring should guide defense investment this way: spend the engineering hours where a real attacker would actually go, not where the fix happens to be quick.

From there:

• Use red teaming to tune detections, re-running the same technique after every fix until it reliably fires.
• Close detection gaps after a red team on a schedule, with a real date attached, not a someday.
• Turn red team misses into a detection backlog inside whatever ticketing system your team already uses daily. A gap that lives in a separate spreadsheet tends to get forgotten.

Most people don’t realize how much of this fails simply because nobody owns the follow-through. A gap without a named owner and a retest date is just a note, and notes get buried under the next incident.

FAQs

Conclusion

Buying tools is the easy part. Proving those tools catch what they are supposed to catch, on a recurring basis, is the part that actually reduces risk. Purple teaming gives you a real-time feedback loop. Detection coverage validation gives you the technique-by-technique proof. And turning every gap into an owned, dated backlog item is what keeps this from becoming a once-a-year fire drill.

If your last red team report is still sitting unread in a shared drive, that’s the clearest sign your detection coverage needs a second look. Secure.com’s Digital Security Teammates can help you keep that loop running continuously instead of once a quarter, so the next test confirms progress instead of repeating the same findings.