AI Intelligence Reading List for Security Leaders

You can learn a lot about national security by tracking budgets, capabilities, and battlefield lessons. But if you want to understand how intelligence actually works when the stakes are real—how people are recruited, how networks fail, how diplomacy becomes an operational tool—books still do something briefs and slides can’t.

That’s why I like The Cipher Brief’s annual “recommended reads” list as more than a holiday shopping guide. It’s a snapshot of what practitioners are thinking about: HUMINT tradecraft, great-power competition, counterterrorism investigations, proliferation risks, and the uncomfortable ethics of distance warfare. For those of us working in the AI in Defense & National Security space, it’s also a helpful map: these narratives point directly to where AI in intelligence analysis, AI in cybersecurity, and autonomous systems are already changing the mission—and where they can go wrong.

Below is a curated, practical reading roadmap inspired by the list, with a specific goal: connect each theme to how AI is being used in defense and national security today, and what security leaders should do about it in 2026.

Why these books matter to AI in defense right now

The simplest truth: AI doesn’t replace intelligence work; it shifts the bottlenecks. The hard part used to be collecting enough information. Now it’s:

• deciding what’s real in a flood of content
• connecting weak signals across domains (cyber, financial, logistics, HUMINT)
• moving faster than adversaries who also use automation
• doing all of it under law, policy, and oversight

The Cipher Brief’s list spans fiction and non-fiction, but the common thread is decision-making under uncertainty. That’s the exact environment where AI promises speed—and where it can amplify failure.

Here’s the stance I’ll take: If your AI strategy in national security is mostly “better dashboards,” you’re underinvesting in the real problem: trust, tradecraft, and governance.

Fiction that teaches tradecraft (and what AI changes)

Spy fiction is underrated training data—just not for models. For humans.

The Cipher Brief highlights several novels that consistently get tradecraft right, including The Persian (David McCloskey), Daniel Silva’s An Inside Job, Yariv Inbar’s Behind the Trigger, and The Moldavian Gambit (Brad Meslin).

HUMINT is still human—AI just changes the surface area

A strong HUMINT story typically revolves around four things: spotting, assessing, recruiting, and handling. AI touches all four, but not in a magical way.

• Spotting: AI can sift travel, finance, social, and professional traces to identify potential access. That increases speed—but also increases the risk of “false-access” targets that look promising statistically and fail operationally.
• Assessing: Models can summarize a target’s digital exhaust, but motivation still requires human judgment. A dataset can tell you what someone did; it rarely tells you why.
• Recruiting: AI-enabled influence can shape narratives at scale. That creates new recruitment vectors, but it also creates new counterintelligence traps.
• Handling: Secure communications, anomaly detection, and operational pattern analysis are increasingly machine-assisted. That’s great—until your adversary trains on your patterns too.

Actionable takeaway: Treat AI as a counterintelligence factor from day one. Every automated workflow creates a signature: timing, routing, phrasing, metadata. If you don’t model your own signature, someone else will.

Proliferation thrillers mirror a real 2026 problem: tracking rare events

The Moldavian Gambit centers on a missing man-portable nuclear device during Soviet collapse—a fiction scenario that maps cleanly to a real analytical challenge: rare, high-impact events. AI systems tend to be best at frequent-pattern problems. Proliferation is the opposite.

Security teams should design AI around:

1. weak-signal fusion (customs anomalies + procurement patterns + human reporting)
2. human-in-the-loop escalation (clear thresholds for “wake someone up” alerts)
3. adversarial robustness (procurement networks are designed to deceive)

If your model can’t explain why it flagged a rare event, it won’t survive contact with policy, lawyers, or oversight.

Non-fiction themes that map directly to AI missions

The Cipher Brief’s non-fiction picks span diplomacy, history, terrorism cases, and future intelligence challenges. Read them as “mission categories” where AI already shows up.

Diplomacy is a force multiplier—and AI can strengthen or weaken it

Two books in the list emphasize statecraft: After Escobar and Great Power Diplomacy.

Here’s the unglamorous reality: diplomacy is an information system. It’s built on relationships, credibility, and the ability to negotiate under incomplete information.

AI helps when it:

• summarizes long-running negotiation histories and prior commitments
• detects inconsistencies across public statements and private cables
• models second-order impacts (sanctions, export controls, supply chains)

AI hurts when it:

• produces overconfident “policy-sounding” text that masks uncertainty
• encourages decision-makers to skip human context and cultural nuance
• accelerates messaging cycles so fast that credibility erodes

Practical move for 2026: Build a “diplomatic traceability” standard for AI outputs: any AI-generated brief should carry (a) confidence levels, (b) cited source categories (not links), and (c) what evidence would change the assessment.

WWII intelligence history is a warning about AI-driven deception

Books like The Spy and the Devil, The Traitor’s Circle, and Rain of Ruin aren’t just history. They’re a reminder that intelligence failures often come from:

• motivated reasoning
• institutional incentives
• deception that exploits what you want to believe

Now translate that into 2026 terms: synthetic media, narrative warfare, and automated influence operations.

If your analytic workflow uses LLMs to summarize open-source reporting, you must assume adversaries will seed the environment with content designed to be summarized.

Here’s what works in practice:

• provenance gating: separate “origin-verified” sources from “unknown provenance” content
• cross-modality checks: don’t validate text with more text; corroborate with imagery, telemetry, or transaction data
• red-team prompts: task a separate team (or model) to generate plausible alternative explanations for the same evidence

A clean-looking summary is not the same thing as a validated assessment.

Counterterrorism cases highlight the real AI use case: triage

Race Against Terror focuses on prosecutors and investigators chasing a terrorist across years. That’s not Hollywood fast. It’s document-heavy, time-bound, and reliant on coordination.

That’s exactly where AI helps: triage at scale.

• entity extraction across reports, warrants, interviews
• timeline reconstruction
• link analysis between people, phones, accounts, locations
• prioritization of leads with transparent scoring

But watch the trap: triage models become policy. If the system consistently downranks a certain region, language, or network structure, you’ve embedded bias into operations.

Operational safeguard: Require quarterly “lead auditing” where investigators review a random sample of low-ranked leads to measure what the model is missing.

The future-intelligence books point to one big shift: dominating the workflow

One of the most directly relevant picks for this series is The Fourth Intelligence Revolution (Anthony Vinci), which argues that intelligence organizations must adapt to a confluence of geopolitical contest and technological acceleration.

Whether you agree with the framing or not, the execution lesson is solid: whoever controls the workflow controls the mission outcome.

In 2026, “AI in defense” isn’t only about model quality. It’s about who owns:

• data pipelines (collection to labeling to retention)
• tasking interfaces (what gets asked, when, and by whom)
• evaluation (what “good” means and how it’s measured)
• governance (approvals, audit logs, and oversight hooks)

If a unit buys a model but can’t integrate it into operational rhythms—brief cycles, watch floors, command decision loops—it becomes shelfware.

Security clearances, insider risk, and the human constraint

The list also includes Trust Me (Lindy Kyzer), a guide to secrets and the clearance process. That might feel like a tangent in an AI post. It isn’t.

AI programs in national security fail for predictable human reasons:

• not enough cleared talent to deploy and maintain systems
• insufficient training for operators to challenge model outputs
• unclear accountability when AI recommendations influence decisions

Your AI roadmap should include:

1. clearance pipeline planning (time-to-clear is a schedule driver, not an HR footnote)
2. role-based training (analysts need different AI literacy than engineers)
3. audit-by-design (logs that show prompts, outputs, and downstream actions)

If you can’t explain how a decision was influenced, you can’t defend it to leadership—or to the public.

A 2026 reading plan for AI and national security teams

If you’re leading a defense tech team, an intel unit, or a security program, you’ll get more value by reading with intent. Here’s a simple way to do it.

Pick one book per mission function

• HUMINT & tradecraft mindset (fiction): The Persian
• Diplomacy & statecraft: Great Power Diplomacy
• Historical intelligence & deception: The Traitor’s Circle
• Future of intelligence & technology: The Fourth Intelligence Revolution
• Workforce, secrets, and governance: Trust Me

Run a 60-minute “AI implications” discussion after each

Use the same five prompts every time:

1. What was the key decision point—and what data shaped it?
2. Where would AI speed things up?
3. Where would AI create new failure modes (deception, bias, overconfidence)?
4. What guardrails would you require (human checks, provenance, audits)?
5. What would you change in your current workflow next week?

That last question is the one that turns reading into operational improvement.

Where this series goes next

This post sits in our AI in Defense & National Security series for a reason: the industry spends plenty of time on models, not enough on mission design. The Cipher Brief’s holiday reading list is a reminder that national security outcomes are built from tradecraft, judgment, and institutional discipline—and AI only improves those if we engineer it into the work carefully.

If you’re planning for 2026, focus less on whether a model is impressive in a demo and more on whether it’s trusted in a watch floor at 2 a.m. Trust comes from evaluation, governance, and trained operators who know when to say, “Show me the evidence.”

If your team had to pick one intelligence workflow to augment with AI in the next 90 days—collection management, cyber threat triage, OSINT validation, or insider-risk detection—where would you start, and what would you refuse to automate?