03 Threat Detection

Welcome to:

Module 03 — Threat Detection

In the previous module, you learned how enterprise SOC teams collect and monitor security telemetry from:

Identity

Endpoints

Networks

Cloud

Applications

Email

SaaS

Now you will learn how to turn that telemetry into:

Detections

A detection is not simply:

A SIEM Rule

A strong detection connects:

Threat Behavior
      ↓
Relevant Telemetry
      ↓
Detection Logic
      ↓
Alert
      ↓
Investigation

The goal of this module is to help you understand not only:

How to Write
a Detection

but:

Why the Detection
Should Exist

What Threat
It Addresses

Which Evidence
It Requires

How to Validate It

How to Improve It

Module Objectives

By the end of this module, you will understand how to:

• explain the purpose of threat detection.

• distinguish prevention from detection.

• understand detection engineering fundamentals.

• identify detection use cases.

• connect attacker behavior to telemetry.

• distinguish IOCs from behavioral indicators.

• understand signature-based detection.

• understand behavior-based detection.

• understand threshold-based detection.

• understand correlation-based detection.

• understand anomaly detection.

• understand sequence-based detection.

• understand identity detections.

• understand endpoint detections.

• understand network detections.

• understand cloud detections.

• understand email detections.

• map detections to MITRE ATT&CK.

• build detection coverage matrices.

• understand detection logic.

• understand detection fields and conditions.

• understand rule thresholds and time windows.

• understand alert severity and confidence.

• understand false positives.

• understand false negatives.

• understand benign true positives.

• tune detection rules.

• validate detections.

• test detections safely.

• identify telemetry dependencies.

• identify detection gaps.

• measure detection quality.

• document detection use cases professionally.

1 — What Is Threat Detection?

Threat detection is the process of identifying activity that may indicate:

Attack

Compromise

Abuse

Policy Violation

Suspicious Behavior

Threat detection attempts to answer:

Is Something
Potentially Malicious
Happening?

2 — Prevention vs Detection

Prevention tries to stop an attack.

Examples:

MFA

Firewall

Application Control

Least Privilege

Detection tries to identify:

Suspicious
or
Malicious Activity

Examples:

Impossible Travel

Credential Dumping

Suspicious PowerShell

Malicious Domain Access

The relationship is:

Prevent
Where Possible

Detect
What Prevention
May Miss

3 — Detection Is a Defensive Hypothesis

A detection can be viewed as:

If This
Behavior Occurs

It May Indicate
This Threat

Example:

IF

A User Has
20 Failed Logins

FOLLOWED BY

A Successful Login

WITHIN

5 Minutes

THEN

Investigate
Potential Credential Attack

4 — Detection Does Not Equal Incident

Remember:

Detection Match
      ↓
Alert

but:

Alert
      ≠
Confirmed Attack

The alert still requires investigation.

5 — Detection Use Case

A detection should begin with a:

Use Case

A good detection use case explains:

What Threat
Are We Trying
to Detect?

Example:

Detect Potential
Privileged Account
Compromise

6 — Detection Use Case Structure

A professional use case may contain:

Use Case ID

Threat Scenario

MITRE ATT&CK

Required Telemetry

Detection Logic

Severity

Confidence

Investigation Steps

Known False Positives

Validation Method

7 — Start With Threat Behavior

Do not begin with:

Which Logs
Do We Have?

Begin with:

What Threat
Do We Need
to Detect?

Then identify:

What Would
That Threat
Look Like?

Then:

Which Telemetry
Would Show It?

8 — Detection Development Workflow

Use:

Threat Scenario
      ↓
Attacker Behavior
      ↓
Required Telemetry
      ↓
Observable Events
      ↓
Detection Logic
      ↓
Alert
      ↓
Validation
      ↓
Tuning

9 — Detection Scenario Example

Threat:

Credential
Compromise

Behavior:

Repeated
Authentication Failure
      ↓
Successful Login
      ↓
Privilege Change

Telemetry:

Identity Logs

MFA Logs

Privilege Events

Detection:

Failures
+
Success
+
Privilege Change

10 — Indicators of Compromise

IOCs may include:

IP Address

Domain

Hash

URL

File Name

Registry Value

Example:

Known Malicious
SHA-256 Hash

11 — IOC-Based Detection

Conceptually:

IF

File Hash
=
Known Malicious Hash

THEN

Generate Alert

Advantages:

Simple

Precise

Fast

Limitations:

Easy for
Attackers to Change

12 — IOC Freshness

Indicators may become obsolete.

Example:

Attacker Uses
IP A Today

IP B Tomorrow

Therefore:

Old IOC
      ≠
Current Threat

13 — Behavior-Based Detection

Behavior detections identify:

What the
Attacker Is Doing

instead of only:

Which Artifact
They Use

Example:

Office Application
      ↓
PowerShell
      ↓
Network Download

14 — Why Behavior Detection Is Powerful

Attackers can change:

Hash

IP

Domain

File Name

but may still need to:

Execute

Persist

Escalate

Dump Credentials

Move Laterally

Exfiltrate

Behavior-based detection focuses on those actions.

15 — Signature-Based Detection

Signature detection matches known patterns.

Examples:

Malware Signature

Exploit Pattern

Known Network Sequence

Best for:

Known
Threat Patterns

16 — Threshold Detection

Threshold detections identify:

Unusual Volume

Example:

More Than
25 Failed Logins

Within
5 Minutes

17 — Threshold Design

A threshold should consider:

Normal Behavior

User Type

Asset Type

Environment

Business Process

A threshold that works for:

Employee Account

may not work for:

Service Account

18 — Correlation Detection

Correlation combines multiple events.

Example:

Failed Login
      +
Successful Login
      +
New MFA Method

within a defined period.

19 — Sequence Detection

Sequence detection evaluates:

Order

of activity.

Example:

Phishing Email
      ↓
Suspicious Login
      ↓
Mailbox Rule Creation

Order adds context.

20 — Anomaly Detection

Anomaly detection identifies behavior that differs from a baseline.

Examples:

New Country

New Device

Rare Process

Unusual Data Volume

Unexpected Login Time

21 — Anomaly Limitation

Remember:

Anomalous
      ≠
Malicious

A user may legitimately:

Travel

Change Device

Work Late

Transfer Large Files

22 — Risk-Based Detection

Risk-based detection combines multiple low-confidence signals.

Example:

New Country
      ↓
+20 Risk

MFA Denials
      ↓
+30 Risk

New Admin Role
      ↓
+50 Risk

Total:

100

may trigger a higher-priority alert.

23 — Detection Confidence

A detection may have:

Low

Medium

High

confidence.

Example:

Known malware hash:

High Confidence

Rare process execution:

Lower Confidence

depending on context.

24 — Severity vs Confidence

These are different.

Severity asks:

How Bad
Could This Be?

Confidence asks:

How Strongly
Does the Evidence
Suggest the Threat?

Example:

Potential Domain
Admin Compromise

may be:

Severity:
Critical

Confidence:
Medium

25 — Identity Detection

Identity detections may monitor:

Failed Logins

Successful Logins

MFA

Role Changes

New Accounts

Password Resets

Session Activity

26 — Password Spray Detection

Password spray typically involves:

One or Few
Passwords

Against

Many Accounts

Possible logic:

Same Source IP

Many Users

Authentication Failures

Short Time Window

27 — Brute Force Detection

Brute force may involve:

Many Password Attempts

Against

One Account

Possible logic:

Same User

Multiple Failures

Short Time Window

28 — Credential Stuffing

Credential stuffing uses:

Previously
Stolen Credentials

against multiple services or users.

Detection may require:

Authentication Pattern

IP Reputation

Device Context

Failure / Success Pattern

29 — Impossible Travel Detection

Conceptually:

User Login
Country A

10 Minutes Later

User Login
Country B

Potential alert:

Impossible Travel

But check:

VPN

Proxy

Mobile Network

GeoIP Error

30 — MFA Fatigue Detection

Potential pattern:

MFA Denied

MFA Denied

MFA Denied

MFA Approved

within a short period.

Questions:

Was User
Expecting Login?

Which Device?

Which Source IP?

What Happened
After Approval?

31 — New Administrator Detection

A high-value detection:

User
Added to
Privileged Group

Investigate:

Who Made Change?

Was It Approved?

Which User?

Which System?

What Happened Next?

32 — Dormant Account Login

Potential detection:

Account
Not Used
for 90 Days

      ↓

Successful Login

This may indicate:

Account Abuse

Return from Leave

Service Change

Context required.

33 — Endpoint Detection

Endpoint detections analyze:

Processes

Command Lines

Files

Registry

Services

Network Connections

User Behavior

34 — Suspicious PowerShell

PowerShell itself is legitimate.

Detection should focus on behavior such as:

Encoded Command

Download Cradle

Hidden Execution

Unusual Parent Process

Credential Access

35 — Parent-Child Detection

Example:

winword.exe
      ↓
powershell.exe

may be suspicious.

Another:

excel.exe
      ↓
cmd.exe

may also deserve investigation.

36 — Rare Process Detection

A process may be suspicious because:

It Rarely
Runs

in the environment.

But rare does not automatically mean malicious.

37 — Credential Dumping Detection

Potential signals:

LSASS Access

Credential Tool Execution

Suspicious Memory Access

Known Dumping Commands

This is usually high-value activity.

38 — Persistence Detection

Monitor:

Scheduled Tasks

Services

Startup Locations

Registry Run Keys

New Accounts

Cloud Persistence

39 — Scheduled Task Detection

Potential logic:

New Scheduled Task

Created by
Unusual User

Running
Suspicious Command

40 — Service Creation Detection

Attackers may create services for:

Persistence

Execution

Lateral Movement

Monitor:

New Service

Binary Path

Creator

Target System

41 — File Detection

Signals include:

Executable
Written to
Temporary Directory

Script Created
in Startup Folder

Known Malicious Hash

42 — Network Detection

Network detections may analyze:

IP

Domain

Port

Protocol

Bytes

Session Duration

Connection Frequency

43 — Command and Control Detection

Potential C2 indicators:

Periodic Beaconing

Rare Domain

Known Malicious IP

Long-Lived Connection

Unusual Protocol

44 — Beaconing

Beaconing may appear as:

Endpoint
      ↓
Same Destination
      ↓
Regular Interval

Example:

Every 60 Seconds

This pattern can be suspicious.

45 — DNS Detection

Potential detections:

Known Malicious Domain

Newly Registered Domain

High-Entropy Subdomain

Large DNS Volume

DNS Tunneling Pattern

46 — DNS Tunneling

Potential indicators:

Long DNS Queries

High Query Volume

Encoded-Looking Subdomains

Unusual Record Types

47 — Data Exfiltration Detection

Potential signals:

Large Outbound Transfer

Rare Destination

Unusual Time

Sensitive System

New External Service

48 — Firewall Detection

Examples:

Repeated Blocked Connections

Unexpected Inbound Access

Connection to Known C2

High-Risk Port Exposure

49 — Cloud Detection

Cloud detections may monitor:

IAM

API Calls

Storage

Network

Compute

Security Services

50 — Cloud Credential Misuse

Possible indicators:

API Calls
from New Country

Unusual User Agent

New Access Key

Privilege Escalation

Multiple Services Accessed

51 — Cloud Logging Disabled

High-value detection:

Audit Logging
Disabled

This may indicate:

Defense Evasion

Administrative Change

Troubleshooting

Investigation required.

52 — Public Storage Detection

Example:

Storage Resource
Changed
      ↓
Public Access Enabled

Investigate:

Who Changed It?

Was It Approved?

What Data?

Was It Accessed?

How Long?

53 — Cloud Privilege Escalation

Potential events:

Admin Role Assigned

Policy Modified

Access Key Created

Permission Boundary Changed

54 — New Cloud User Detection

Monitor:

New IAM User

New Service Principal

New Access Key

New Role

especially in:

Production

55 — Security Tool Disablement

Detect:

EDR Disabled

Logging Disabled

Security Agent Stopped

Cloud Security Service Disabled

These may indicate:

Defense Evasion

56 — Email Detection

Email detections may analyze:

Sender

Headers

URLs

Attachments

Authentication

Delivery

Mailbox Actions

57 — Phishing Detection

Potential indicators:

Spoofed Domain

Suspicious URL

Malicious Attachment

Failed DMARC

Lookalike Domain

58 — Business Email Compromise

Possible signals:

Suspicious Login

New Inbox Rule

External Forwarding

Unusual Payment Request

Mailbox Search

59 — Mailbox Rule Detection

Attackers may create rules to:

Hide Messages

Forward Messages

Delete Alerts

Detect:

New Rule

Rule Modification

External Forwarding

60 — Application Detection

Application detections may monitor:

Admin Actions

Authentication

Bulk Export

Privilege Changes

API Calls

Sensitive Transactions

61 — Bulk Data Export

Potential logic:

User Downloads
100,000 Records

When Normal
Daily Activity
Is 500

This may require context.

62 — Web Attack Detection

Potential patterns:

SQL Injection

Path Traversal

Command Injection

Web Shell

Repeated Exploit Attempts

63 — Web Attack Correlation

A single:

HTTP 404

is normal.

Thousands of unusual requests across:

Many Paths

may indicate scanning.

64 — MITRE ATT&CK Mapping

Each detection should map to relevant ATT&CK techniques where appropriate.

Example:

Detection:
Suspicious PowerShell

ATT&CK:
Execution
      ↓
Command and
Scripting Interpreter

65 — Why ATT&CK Mapping Helps

ATT&CK mapping supports:

Coverage Analysis

Threat Modeling

Purple Team Testing

Detection Prioritization

Reporting

66 — ATT&CK Mapping Limitation

Do not map every detection to:

10 Techniques

just because words seem similar.

Map:

Actual Behavior
the Detection
Can Observe

67 — Detection Coverage Matrix

Create:

ATT&CK Technique	Telemetry	Detection	Status
Credential Dumping	Endpoint	LSASS access	Covered
PowerShell	Endpoint	Suspicious PowerShell	Covered
Account Discovery	Endpoint	None	Gap
Cloud Account Creation	Cloud	New IAM user	Covered

68 — Coverage Does Not Equal Effectiveness

A detection may exist.

But:

Rule Exists
      ≠
Rule Works

You need:

Validation

69 — Detection Logic

A detection usually contains:

Fields

Conditions

Threshold

Time Window

Exclusions

Severity

70 — Detection Fields

Example authentication fields:

User

Source IP

Destination

Result

Country

Device

MFA Status

Timestamp

71 — Detection Condition

Example:

result = failure

is one condition.

A more useful detection may combine:

result = failure

AND

account_type = privileged

72 — Time Window

Many detections need:

Time Context

Example:

15 Failures
Within
5 Minutes

Time windows dramatically affect rule behavior.

73 — Grouping

A detection may group events by:

User

Source IP

Host

Device

Destination

Example:

Count Failures
By User

versus:

Count Failures
By Source IP

detects different attack patterns.

74 — Exclusions

Some activity may be expected.

Example:

Known Vulnerability
Scanner

might generate large connection volumes.

You may exclude it.

But exclusions must be:

Documented

Specific

Reviewed

75 — Dangerous Exclusions

Avoid:

Exclude All
Administrator Activity

This could hide real attacks.

76 — Detection Tuning

Tuning aims to improve:

Signal
to
Noise

Possible tuning:

Threshold Change

Scope Change

Known-Benign Exclusion

Asset Context

Identity Context

Sequence Logic

Risk Weighting

77 — False Positive

A false positive occurs when:

Detection
Alerts
Incorrectly

Example:

Legitimate backup activity triggers an exfiltration rule.

78 — Benign True Positive

The detection accurately identifies suspicious behavior.

But the behavior is:

Authorized

Example:

Red team credential dump during approved testing.

79 — False Negative

A false negative occurs when:

Malicious Activity
Occurs

but

No Detection
Alerts

These are especially important because:

You May
Never Know
They Happened

80 — Detection Blind Spot

A false negative may occur because:

Telemetry Missing

Rule Missing

Rule Incorrect

Threshold Too High

Exclusion Too Broad

Attack Technique Different

81 — Detection Validation

Every important detection should be tested.

Validation asks:

Does the
Required Behavior
Generate Telemetry?

Does the
Detection Match?

Does the
Alert Contain
Useful Context?

82 — End-to-End Detection Test

Simulate Behavior
      ↓
Generate Telemetry
      ↓
Collect Telemetry
      ↓
Parse Event
      ↓
Detection Executes
      ↓
Alert Generated
      ↓
SOC Receives Alert

83 — Detection Testing Safety

Testing should occur in:

Authorized

Controlled

Approved

environments.

Do not simulate potentially disruptive activity against unauthorized systems.

84 — Positive Test

A positive test checks:

Does Malicious-Like
Behavior Trigger
the Detection?

85 — Negative Test

A negative test checks:

Does Normal
Behavior Avoid
Unnecessary Alerting?

Both matter.

86 — Detection Test Case

Create:

Test Case

Expected Telemetry

Expected Detection

Expected Alert

Observed Result

Pass / Fail

87 — Detection Test Failure

If simulation produces logs but no alert:

Telemetry Exists
      ↓
Detection Failed

Investigate:

Parsing

Query Logic

Threshold

Time Window

Rule Status

88 — Telemetry Failure

If simulation occurs but required telemetry does not exist:

Detection Cannot
Work Reliably

The problem is:

Telemetry Coverage

not only rule logic.

89 — Detection Documentation

Create:

Detection_Use_Case.md

with:

Use Case ID

Detection Name

Threat Scenario

MITRE ATT&CK

Telemetry

Logic

Severity

Confidence

Investigation Steps

False Positives

Validation Method

Owner

Status

90 — Detection Ownership

Every detection should have:

Owner

Reviewer

Review Date

Otherwise detections may become outdated.

91 — Detection Lifecycle

Threat Need
      ↓
Design
      ↓
Build
      ↓
Test
      ↓
Deploy
      ↓
Monitor
      ↓
Tune
      ↓
Review
      ↓
Retire

92 — Detection Versioning

Track:

Rule Version

Logic Change

Reason

Date

Reviewer

especially when tuning changes detection behavior.

93 — Detection Change Risk

Changing:

Threshold
10
      ↓
100

may reduce alerts.

But it may also:

Create
False Negatives

94 — Detection Quality

A good detection should be:

Relevant

Reliable

Actionable

Explainable

Testable

Maintainable

95 — Actionable Alerts

An alert should provide enough information for the analyst to begin.

Useful fields may include:

User

Host

IP

Timestamp

Process

Detection Reason

Severity

Related Events

96 — Weak Alert

Avoid alerts that only say:

Suspicious Activity
Detected

without context.

97 — Detection Precision

Precision asks:

Of the Alerts
Generated

How Many
Are Useful?

98 — Detection Recall

Conceptually, recall asks:

Of the Threat
Activity That Occurred

How Much
Did We Detect?

High precision with very low recall may still miss important threats.

99 — Detection Engineering Trade-Off

Too sensitive:

Many Alerts

Many False Positives

Too strict:

Few Alerts

Potential False Negatives

The goal is balance.

100 — Detection Priority

Prioritize detections based on:

Threat Likelihood

Business Impact

Asset Criticality

Attack Stage

Existing Coverage

Incident History

101 — High-Value Detection Areas

Examples:

Privileged Access

Identity

Credential Theft

Persistence

Lateral Movement

Cloud IAM

Security Control Disablement

Data Exfiltration

102 — Detection Debt

Detection programs can accumulate:

Old Rules

Broken Rules

Duplicate Rules

High-Noise Rules

Unused Rules

This creates:

Detection Debt

103 — Rule Review

Periodically review:

Alert Volume

True Positives

False Positives

Rule Usage

Last Triggered

Telemetry Dependency

ATT&CK Mapping

104 — Detection Retirement

Retire rules when:

Technology Removed

Threat No Longer Relevant

Rule Replaced

Data Source Removed

but preserve history where required.

105 — Detection Gap

A detection gap exists when:

Threat Behavior
      ↓
Telemetry Exists
      ↓
No Detection

106 — Telemetry Gap

A telemetry gap exists when:

Threat Behavior
      ↓
Required Event
Not Available

107 — Visibility Gap

A broader visibility gap may involve:

Asset Not Monitored

Platform Not Integrated

Logs Missing

Retention Insufficient

108 — Prioritize Detection Gaps

Not every ATT&CK technique needs equal priority.

Use:

Threat Relevance

Environment

Assets

Business Risk

Attack History

109 — Threat-Informed Detection

Threat-informed defense asks:

Which Threats
Actually Matter
to Our Organization?

Then focuses detection coverage accordingly.

110 — Threat Intelligence and Detection

Threat intelligence may help identify:

Campaign

Malware

Infrastructure

Techniques

Targets

You can translate this into:

Detection Requirements

111 — Detection From Threat Intelligence

Example:

Threat report says attackers:

Use PowerShell

Create Scheduled Tasks

Access LSASS

Detection team can review coverage for:

PowerShell

Scheduled Tasks

Credential Dumping

112 — Detection From Incidents

Incidents are valuable detection sources.

After an incident ask:

What Did
the Attacker Do?

Which Telemetry
Captured It?

Which Alerts
Triggered?

What Did
We Miss?

113 — Incident-to-Detection Loop

Incident
      ↓
Attack Timeline
      ↓
Detection Gaps
      ↓
New Rules
      ↓
Validation
      ↓
Improved Coverage

114 — Threat Hunting and Detection

Threat hunts may uncover:

Undetected
Behavior

That hunt can become:

New Detection

115 — Purple Team and Detection

Purple team exercises answer:

Can Our
Defenses Detect
This Technique?

Workflow:

Simulate Technique
      ↓
Observe Telemetry
      ↓
Check Detection
      ↓
Investigate Alert
      ↓
Improve Rule

116 — Detection Engineering Mindset

Do not ask only:

Can I Write
a Query?

Ask:

What Threat
Does This Detect?

Which Telemetry
Does It Depend On?

What Will
the Analyst See?

How Will
We Validate It?

How Could
an Attacker Avoid It?

Practical Exercise 1 — Detection Use Case

Create:

DET-001
Privileged Account
Compromise

Document:

Threat Scenario

Telemetry

Detection Logic

ATT&CK Mapping

Severity

False Positives

Investigation Steps

Practical Exercise 2 — Password Spray

Design a detection using:

Source IP

Multiple Accounts

Failed Logins

Time Window

Explain why grouping only by:

User

may fail to detect password spraying.

Practical Exercise 3 — MFA Fatigue

Create logic for:

Multiple MFA Denials
      ↓
Successful MFA

within a defined window.

Document possible benign scenarios.

Practical Exercise 4 — Suspicious PowerShell

Create a behavior detection considering:

Parent Process

Command Line

Encoded Command

Network Activity

User

Do not classify all PowerShell as malicious.

Practical Exercise 5 — Cloud Logging Disabled

Create a detection for:

Cloud Audit
Logging Disabled

Document:

Telemetry

Severity

Investigation Questions

Potential Benign Cause

Practical Exercise 6 — Data Exfiltration

Design a candidate detection using:

User

Asset

Data Volume

Destination

Time

Baseline

Practical Exercise 7 — MITRE ATT&CK Coverage

Create a matrix for:

10 ATT&CK Techniques

including:

Telemetry

Detection

Validation Status

Gap

Practical Exercise 8 — False Positive Analysis

Take this detection:

Office
Spawns
PowerShell

Identify:

5 Potential
Benign Causes

and determine what context could reduce false positives.

Practical Exercise 9 — Detection Test Plan

Create a test plan containing:

Detection

Test Behavior

Expected Telemetry

Expected Alert

Negative Test

Observed Result

Practical Exercise 10 — Detection Gap Assessment

For a fictional environment, analyze:

Credential Dumping

Persistence

Lateral Movement

Cloud Privilege Escalation

Data Exfiltration

and classify each as:

Covered

Partial

Gap

Unknown

Knowledge Check

1. What is threat detection?

2. How is detection different from prevention?

3. Why should detection begin with a threat use case?

4. What is a detection use case?

5. What is an IOC?

6. What are the limitations of IOC-only detection?

7. What is behavior-based detection?

8. Why can behavior detection remain useful when attacker infrastructure changes?

9. What is signature-based detection?

10. What is threshold detection?

11. What is correlation detection?

12. What is sequence detection?

13. What is anomaly detection?

14. Why does anomalous not mean malicious?

15. What is risk-based detection?

16. What is detection confidence?

17. How is severity different from confidence?

18. How might password spraying differ from brute force?

19. What telemetry is useful for MFA-fatigue detection?

20. Why is a new administrator event important?

21. Why should PowerShell detections consider behavior?

22. What is parent-child process analysis?

23. What signals may indicate credential dumping?

24. What are common persistence signals?

25. What is beaconing?

26. What signals may indicate DNS tunneling?

27. What signals may indicate data exfiltration?

28. What cloud activities should have high-value detections?

29. Why is disabling logging suspicious?

30. What email events can indicate business email compromise?

31. Why should detections map to MITRE ATT&CK?

32. What is a detection coverage matrix?

33. Why does detection coverage not prove detection effectiveness?

34. What fields typically make up detection logic?

35. Why are time windows important?

36. What is event grouping?

37. Why must exclusions be carefully controlled?

38. What is a false positive?

39. What is a benign true positive?

40. What is a false negative?

41. What can cause a false negative?

42. What is detection validation?

43. What is a positive detection test?

44. What is a negative detection test?

45. Why should detections be tested end to end?

46. What is detection debt?

47. What is a detection gap?

48. What is a telemetry gap?

49. What is threat-informed detection?

50. How can incidents improve detection engineering?

Key Takeaways

Threat detection follows:

Threat
      ↓
Behavior
      ↓
Telemetry
      ↓
Detection Logic
      ↓
Alert
      ↓
Investigation

Detection engineering follows:

Use Case
      ↓
Design
      ↓
Build
      ↓
Test
      ↓
Deploy
      ↓
Monitor
      ↓
Tune

Remember:

IOC Match
      ≠
Confirmed Attack

Anomaly
      ≠
Malicious Activity

Rule Exists
      ≠
Rule Works

Detection Coverage
      ≠
Detection Effectiveness

Alert
      ≠
Incident

A strong detection program connects:

Threat Intelligence

Attack Behavior

Telemetry

Detection Logic

SOC Investigation

Incident Lessons

Career Connection

Threat detection knowledge is essential for:

SOC Analysts

Blue Team Analysts

Detection Engineers

Threat Hunters

Incident Responders

SIEM Engineers

Security Analysts

During interviews, you should be able to explain:

How You Design
a Detection

What Telemetry
You Need

How You Reduce
False Positives

How You Identify
False Negatives

How You Map
to MITRE ATT&CK

How You Validate
a Detection

The professional skill is not simply:

Writing Queries

It is being able to connect:

Threat
      ↓
Behavior
      ↓
Telemetry
      ↓
Detection
      ↓
Investigation

What’s Next?

➡️ Next: 04 — Threat Hunting

You now understand how Blue Teams create detections for known or anticipated attacker behaviors.

But detections can miss activity.

The next question is:

What If
the Attacker
Is Already Inside

and

No Alert
Has Triggered?

In the next module, you will move from:

Alert-Driven
Investigation

to:

Proactive
Threat Hunting

You will learn:

Threat Hunting Fundamentals

Hunt Hypotheses

Threat-Informed Hunting

MITRE ATT&CK-Based Hunting

Baseline Analysis

Anomaly Hunting

Identity Hunting

Endpoint Hunting

Network Hunting

Cloud Hunting

IOC and Behavioral Hunting

Hunt Queries

Evidence Collection

Hunt Documentation

Hunt-to-Detection Conversion

You will move from:

What Alerts
Do We Have?

to:

What Threat
Might Be Present
That Our Alerts
Did Not Detect?

➡️ Next: 04 — Threat Hunting