In this section
TH0.2 The Dwell Time Gap
Eleven days
Mandiant's M-Trends 2024 report — the industry benchmark for intrusion statistics — reported a global median dwell time of 10 days. That number has improved dramatically from the 416-day median reported a decade ago. Security operations have gotten better. Detection has gotten faster. But 10 days is the median, which means half of all investigated intrusions had dwell times longer than that. And the number only includes intrusions that were eventually detected. The ones nobody found are not in the dataset.
Ten days does not sound catastrophic. It is. Here is what a competent attacker accomplishes in a Microsoft 365 environment in ten days of undetected access.
Hours 0–24: the persistence window
// Your dwell time baseline — measure it, do not assume it
SecurityIncident
| where TimeGenerated > ago(180d)
| where Status == "Closed"
| extend EarliestEvidence = todatetime(
parse_json(tostring(AdditionalData)).firstActivityTimeUtc)
| extend IncidentCreated = CreatedTime
| where isnotempty(EarliestEvidence)
| extend DwellDays = datetime_diff(
'day', IncidentCreated, EarliestEvidence)
// Days between first attacker activity and incident creation
| where DwellDays >= 0 and DwellDays < 365
// Filter obvious data quality issues
| summarize
MedianDwell = percentile(DwellDays, 50),
P75Dwell = percentile(DwellDays, 75),
P90Dwell = percentile(DwellDays, 90),
IncidentCount = count()
// P90 is the number that matters — the long-tail intrusions
// where the attacker had extended accessTry it yourself
Exercise: Calculate your dwell time baseline
Run the query above against your Sentinel workspace. Record three numbers: median (P50), 75th percentile (P75), and 90th percentile (P90).
The P90 should concern you most. It represents the long-tail — the intrusions where the attacker had extended undetected access. If your P90 is above 30 days, your detection layer has a significant responsiveness gap that hunting directly addresses.
Compare your median to Mandiant's latest M-Trends benchmark (10 days global median in the 2024 report). If yours is higher, your detections are slower than industry average. If yours is lower, your detection engineering is effective for the threats it covers — but the undetected intrusions (the ones not in this dataset) may have dwell times far longer.
You will use this baseline in TH14 (Building a Hunt Program) to measure whether hunting is compressing dwell time over time.
The myth: Fast mean time to respond (MTTR) proves the SOC is effective. If we respond quickly to incidents, we are detecting threats effectively.
The reality: MTTR measures how fast you respond after an alert fires. It says nothing about how long the attacker was present before the alert fired. A SOC with a 2-hour MTTR and a 30-day median dwell time responds quickly to incidents it eventually detects — but the attacker had 30 days of unmonitored access before that response began. MTTR without dwell time is half the picture. The metric that matters is mean time to detect (MTTD), measured from the earliest attacker activity to first detection. MTTD can only be reduced by better detection rules or proactive hunting.
The intrusions you will never measure
There is a category of intrusion that never appears in any dwell time statistic: the ones where the attacker achieved their objective and left without being detected at all. The BEC operator who intercepted one wire transfer and disappeared. The data theft operator who exfiltrated a customer database and sold it on a dark web marketplace. The competitor who read executive emails about an upcoming acquisition and used the intelligence commercially.
These intrusions are discovered months or years later — if ever — through downstream consequences: the customer data appears in a breach notification from another source, the wire transfer is flagged during an audit, the competitor's suspiciously well-timed market move prompts an investigation. By then, the forensic evidence may be beyond your log retention window.
Hunting does not guarantee you will find these intrusions. But it is the only operational activity that looks for them. Detection rules wait for a pattern. Hunting goes looking. The intrusions you never discover are the ones you never looked for.
Extend this analysis
Dwell time benchmarks vary by industry and region. Financial services organizations typically report shorter dwell times than healthcare or manufacturing — regulatory pressure and security investment explain much of the difference. When presenting dwell time data to leadership, use sector-specific benchmarks from the Mandiant M-Trends report, the CrowdStrike Threat Hunting Report, or the IBM X-Force Threat Intelligence Index. A 15-day median in healthcare may be above average for that sector; the same number in financial services would be below. Context determines whether the number is alarming or acceptable — and hunting is justified in both cases because the undetected intrusions exist regardless of the benchmark.
References Used in This Subsection
- Mandiant. "M-Trends 2024 Special Report." Google Cloud. https://cloud.google.com/security/resources/m-trends — verify URL for 2024 edition
- FBI. "Internet Crime Complaint Center (IC3) — 2023 Internet Crime Report." https://www.ic3.gov/AnnualReport/Reports/2023_IC3Report.pdf — verify URL
- Microsoft. "Microsoft Sentinel — SecurityIncident Schema." Microsoft Learn. https://learn.microsoft.com/en-us/azure/sentinel/data-source-schema-reference
- MITRE ATT&CK Techniques referenced: T1078 (Valid Accounts), T1098 (Account Manipulation), T1564.008 (Email Hiding Rules), T1098.003 (Additional Cloud Roles), T1114 (Email Collection)
Your ATT&CK coverage analysis shows 45% coverage. The CISO asks: 'What is our target?' Do you say 100%?
No. 100% ATT&CK coverage is neither achievable nor meaningful — some techniques are inherently difficult to detect, some are irrelevant to NE's environment, and the cost of detecting the last 10% is disproportionate to the risk reduction. The target is based on NE's threat profile: 80% coverage of techniques observed in attacks against defense supply chain organizations (sourced from MDDR and CiSP intelligence). This threat-informed target focuses resources on the techniques NE is most likely to face, not on theoretical completeness.
You understand the detection gap and the hunt cycle.
TH0 showed you what detection rules fundamentally cannot catch. TH1 gave you the hypothesis-driven methodology that closes that gap. Now you run the hunts.
- 10 complete hunt campaigns — from hypothesis through KQL execution through finding disposition, each campaign based on a real TTP
- 70 production hunt queries — every one mapped to MITRE ATT&CK and tested against realistic telemetry
- Advanced KQL for hunting — UEBA composite risk scoring, retroactive IOC sweeps, and hunt management metrics
- Hypothesis-Driven Hunt Toolkit lab pack — 30 days of realistic M365 and endpoint telemetry with multiple attack patterns seeded in
- TH16 — Scaling hunts across a team — the operating model for a production hunt program