Foreword by Chris Liapis xiii
Foreword by Will Burns xv
About the Author xvii
Acknowledgments xix
Introduction xxi

Chapter 1: Setting Sail 1
1.1 Mission 1
1.2 Understanding AI's Role in Intelligence 2
Bridging the Gaps 3
Embracing Fears and Shifting Mindset 6
How Are Bad Actors Using AI Technology? 10
1.3 Introduction to Workflow Development 17
Step 1: Document Your Current Analytical Process 17
Step 2: Identify Roadblocks and Friction Spots 18
Step 3: Pair AI with Your Workflow 18
Step 4: Build an AI Plan 18
The First 30 Days 19

Chapter 2: Mapping the Depths 21
2.1 Mission 21
2.2 Matching AI Capabilities to Analyst Problems 22
Scenario: Testing AI on Procurement Fraud 24
2.3 Matching Tools to Real Users 25
Tool Evaluation and Validation 27
No Code to Some Code 29
2.4 Think Like the Adversary 30

Chapter 3: Casting the Nets 33
3.1 Mission 33
3.2 The Data Problem 34
Data Isn't Neutral 35
But What Is Data? 35
Assuming Makes an Ass Out of You and Your Data 36
3.3 What Analysts Need to Know About Data 37
Can I Trace Data Back to Its Source? 38
What's Missing and Why? 38
3.4 Data Types and Sources 39
Structured Data 39
Semi-Structured Data 40
Unstructured Data 40
Putting It All Together 41
3.5 What Does Automating Collection Actually Mean? 42
Monitor Categories, Not Platforms 42
Clean It Like a Crime Scene, Not a Kitchen 44
3.6 Designing an AI Collection Workflow 46
Defining Source Categories 47
Defining Relevant Data 47
Defining Data Cleaning Rules 48
Defining Review Triggers 49
Common Automated Workflow Mistakes 49
So What Do You Actually Do with This Information? 50
No-Code to Low-Code Workflow Automation 51

Chapter 4: Diving Beneath the Surface 55
4.1 Mission 55
4.2 Organizing the Noise 56
4.3 What Is Machine Learning? 58
4.4 How Machine Learning Works 58
4.5 Types of Machine Learning 60
Deep Learning 61
4.6 Why Machine Learning Matters to Analysts 63
Key Applications of ML in OSINT 63
Remember: You're Still the Analyst 70

Chapter 5: Interpreting the Sea 71
5.1 Mission 71
5.2 Decoding the Message 72
5.3 What Is Natural Language Processing? 73
How Natural Language Processing Works 73
Types of Natural Language Processing 80
5.4 Why Natural Language Processing Matters to Analysts 83
NLP Limitations 84
Run Your First NLP Analysis 85
5.5 NLP Toolbox 85Contents ix

Chapter 6: Steering the Ship 89
6.1 Mission 89
6.2 What Is a Prompt? 89
6.3 Why Prompts Matter in Intelligence Work 90
6.4 How AI Interprets Prompts 92
Step 1: Model Breaks Your Prompt Down 92
Step 2: Model Searches for Patterns 93
Step 3: Model Generates a Response Token by Token 93
Why Do I Need to Understand the Process? 94
Types of Prompts 95
6.5 What Makes a Prompt Work 96
Fire-Bending Your Task: Give It Direction and Force 96
Earth-Bending Your Scope: Set Hard Limits 97
Water-Bending Your Format: Shape the Output to Fit the Task 99
Air-Bending Your Controls: Make the Invisible Gaps Visible 100
Putting It Together 101
6.6 Advanced Prompting Techniques 102
Role-Based Prompting: Tell the Model What Disguise to Wear 103
Chain-of-Thought: Make the Model Show Its Work 104
Few-Shot Prompting: Show the Model What Good Looks Like 105
Iterative Refinement: Treating AI Like a Junior Analyst 106
Combining Techniques 107
Using Agents for Automation 108
When Prompts Go Wrong 109

Chapter 7: Adjusting the Sails 111
7.1 Mission 111
7.2 What Is Predictive Analysis? 112
Analyst Approach to Predictive Analysis 113
Technical Approach to Predictive Analysis 114
7.3 How AI Enhances Predictive Analysis 116
Scale and Speed 116
Pattern Discovery 116
Actionable Insights 117
Adaptation to Emerging Threats 118
Organizing Unstructured Data 118
7.4 Key Steps in Predictive Analysis for Intelligence Work 119
Step 1: Collect 119
Step 2: Prepare 120
Step 3: Model 121
Step 4: Train 122
Step 5: Test 123
Step 6: Predict 124
Step 7: Act 125
7.5 Using Predictive Analysis to Assess Risk and Forecast Threats 125
Turning Observations into Prompts 126x Contents
Developing Early Warnings from AI Scenarios 127
Risk Assessments with AI 130
7.6 Case Studies in Predictive Analysis 132
The Wagner PMC Rebellion and March on Moscow 132
Russia's Invasion of Ukraine 133
GameStop/Reddit Wall Street Bets Short Squeeze 134

Chapter 8: Staying the Course 137
8.1 Mission 137
8.2 Critical Thinking in AI Times 138
What Is Argument Analysis? 139
8.3 Ethical Issues in AI for Intelligence Work 143
Bias and Discrimination 143
Lack of Transparency 147
Privacy Concerns 150
Job Displacement 153
Autonomous Decision-Making 155
Manipulation and Misinformation 158
Accessibility and Inequality 164
Environmental Impact 166
Intellectual Property and Creativity 169
Misaligned Goals 172
8.4 The Human Cost of AI 174
AI Mental Health Concerns 174
Consent and Data Ownership 175

Chapter 9: Anchoring Down 179
9.1 Mission 179
9.2 The Vibe Coding Problem 180
Vibe-Coded Intelligence Tools 180
9.3 Why Python? 182
Where to Write Python Code 182
What's a Library? 183
APIs: Your Gateway to Automation 185
9.4 Building Your First Tool: Domain Analyzer 186
Step 1: Install Your Libraries 186
Step 2: Import Your Libraries and Create Your Domain List 187
Step 3: Build the Lookup Function 188
Step 4: Run It Against Your List 189
Step 5: Visualize and Export Your Results 190
9.5 Poof, You Are Now a Developer (Not a Vibe Coder) 193
With Great Power Comes Great Responsibility 193

Chapter 10: Captain's Log 197
10.1 Mission 197
10.2 The Gunslinger's Hallway: Same Skills, Different Worlds 197
Door 1: Cyber-Threat Intelligence 198
Door 2: Disinformation and Influence Operations 199Contents xi
Door 3: Geopolitical and Military Intelligence 201
Door 4: Law Enforcement 203
Door 5: Humanitarian and Crisis Response 205

Chapter 11: Scanning the Horizon 207
11.1 Mission 207
11.2 Agentic AI 208
AGI for Analysts 209
Is AGI Even Feasible? 211
11.3 Multimodal AI 213
11.4 Large World Models 213
11.5 Quantum Computing and Security 214
Example 215
What Does Quantum Computing Have to Do with AI? 215
Limits and Risks of AI-Quantum Systems 216
11.6 Attribution and Contamination 217
The Rot You Can't See 217
Everything Is Fine 218

Chapter 12: Plotting the Next Course 219
12.1 Mission Wrap-Up 219
12.2 What You Actually Built 220
12.3 Next Steps 221
If You Want to Specialize 221
If You Want to Build 221
If You Just Want to Do the Job Better 222
How to Stay Current Without Losing Your Mind 222

Index 225