AI Threat Assessment: How Leaders Read Global Hotspots

A small phrase from Gen. Dan Caine’s recent public remarks should make every defense technologist sit up: the Chairman of the Joint Chiefs said his job is to present military options with “secondary and tertiary considerations.” That’s not rhetoric. It’s an admission that modern conflict isn’t about picking an option—it’s about forecasting everything that follows.

Right now, the U.S. military is staring at simultaneous pressures: drone mass in Ukraine, instability in parts of the Middle East, China’s scaling combat capacity, and a Western Hemisphere focus that’s creeping back into force posture. Add the domestic trust problem swirling around controversial strikes and oversight, and you get the real operating environment: leaders don’t just need better weapons. They need better decision support.

That’s where AI in defense & national security stops being a lab project and becomes a command problem. If senior leaders are expected to surface second- and third-order effects faster than adversaries can create dilemmas, then AI-driven threat assessment isn’t optional—it’s infrastructure.

“Secondary and tertiary considerations” is the real battlefield

Second- and third-order effects are where wars get messy: escalation ladders, blowback, civilian harm, alliance fracture, logistics strain, legal exposure, and information warfare consequences that outlive the strike itself. Caine’s framing is a useful truth: the hard part isn’t generating options; it’s mapping consequences under uncertainty.

Why the options model breaks down in 2025

Traditional staff processes were built for a world where:

• Intelligence cycles were slower
• The battlespace was geographically bounded
• Public information moved at news speed, not algorithm speed
• “Non-kinetic” effects were treated as supporting fires, not primary terrain

Now, the information environment is continuous. Commercial imagery updates rapidly. Open-source narratives mutate hourly. Drones compress the kill chain. Cyber and EW create reversible effects that still trigger real-world retaliation.

So when Caine emphasizes downstream considerations, he’s also hinting at a capability gap: humans can’t manually integrate all the relevant signals fast enough—not across multiple theaters.

What AI should do (and what it shouldn’t)

Here’s the stance I’ll take: AI shouldn’t “decide” military action. It should shrink uncertainty and surface tradeoffs. The best AI decision-support systems are the ones that make leadership conversations sharper:

• What assumptions are baked into this option?
• Which indicators would tell us we’re wrong?
• What collateral risks are rising—legal, political, humanitarian, alliance?
• What’s the adversary’s cheapest counter-move?

This is “secondary and tertiary considerations” made operational.

Hotspot reality check: what leaders are actually watching

Caine’s remarks (notably cautious about policy) still sketch the main pressure points where AI-enabled intelligence analysis and mission planning are most relevant.

Western Hemisphere: a posture shift with oversight friction

Caine flags the idea that the U.S. hasn’t had much combat power “in our own neighborhood” and that may change. Whatever one thinks of that shift, it implies an operational demand: persistent maritime and air domain awareness, plus clearer accountability mechanisms when strikes happen.

Controversial maritime strikes—especially ones tied to public reporting of casualties—create a trust deficit that can degrade freedom of action. AI can’t fix politics, but it can improve the underlying competence:

• Better target characterization from multi-source fusion (ISR + AIS + SIGINT + HUMINT)
• Stronger “pattern-of-life” analysis to reduce misidentification
• Faster after-action evidence packaging for oversight (what can be shared, what must remain classified)

If Congress and the public are asking “how did you know?” then the system needs an answer that’s more rigorous than “trust us.”

Europe/Ukraine: the drone economy meets the mass problem

Caine points to Ukraine’s production of “tens of thousands and hundreds of thousands” of drones. That matters because it reframes a procurement truth: mass is back, and it’s increasingly software-defined mass.

Ukraine’s battlefield has taught three hard lessons that AI can support directly:

1. Attrition at scale is normal again. You need systems you can afford to lose.
2. The kill chain is a contest of detection, classification, and timing. AI sits in the middle of that.
3. EW and counter-UAS are continuous adaptation loops. Models must update quickly or become liabilities.

Indo-Pacific/China: “multiple simultaneous dilemmas” is a systems problem

Caine uses a phrase that defense planners repeat for a reason: creating “multiple simultaneous dilemmas” forces adversaries to spread resources and hesitate. But dilemmas don’t come from a single exquisite platform. They come from coordinated sensing, targeting, deception, logistics, and resilience.

AI contributes by:

• Fusing sensor feeds across domains (space, air, maritime, cyber)
• Flagging anomalous mobilization patterns
• Running fast campaign-level simulations to test deterrence options
• Stress-testing logistics networks under disruption

The operational implication is blunt: if China is building capacity “at scale,” the U.S. needs not only capacity—but decision velocity that doesn’t collapse under complexity.

Middle East: uncertainty, escalation risk, and ISR saturation

Caine calls the region “critical” and “undecided,” watches Gaza through CENTCOM, and remains concerned about Iran’s intentions. That’s a concise description of an environment where:

• Indicators are noisy
• Escalation pathways are numerous
• Tactical actions can trigger strategic consequences

AI-enabled surveillance and intelligence analysis can help prioritize attention (what matters most today), but the bigger value is in risk forecasting: identifying which combinations of events correlate with escalation—rocket activity + militia communications changes + air defense posturing + financial network signals.

Getting “ahead of the technology curve” means fixing acquisition and data

Caine’s most actionable point isn’t about a specific adversary. It’s about the U.S. system being good at buying behind the technology development curve—and needing to get in front of it.

That statement lands differently in December 2025 because the private sector’s AI cycle is measured in weeks, while defense procurement is often measured in years. The gap isn’t just speed. It’s fit.

The real bottleneck: data readiness, not model quality

Most defense AI failures aren’t because the model is weak. They’re because:

• Data is siloed by program, classification, and vendor
• Labels aren’t consistent across commands
• Ground truth arrives late or never
• Edge conditions (EW, degraded comms, adversarial deception) aren’t represented

If you want AI threat assessment that leaders can trust, you need a pipeline that treats data like a weapon system: governed, tested, monitored, and improved.

A practical “high-low mix” for AI-enabled systems

Caine mentions a “high-low mix” and more “attritable things.” Translate that into AI procurement and you get a realistic portfolio approach:

• High-end models and sensors for contested environments (expensive, protected, limited quantity)
• Low-cost, mission-specific models that run at the edge (cheap, replaceable, rapidly updateable)
• Human-centered workflows so analysts and operators can interrogate outputs, not just receive them

This matters because AI systems that require pristine connectivity and perfect data will fail exactly when needed.

What AI threat assessment looks like in practice (a workable blueprint)

“AI in national security” can sound abstract. Here’s a concrete blueprint I’ve seen work best conceptually—because it aligns with how senior leaders actually think.

1) Build an AI-enabled indications & warning layer

Start with a system that answers one question: What changed since yesterday that should worry me?

Capabilities that belong here:

• Multi-source fusion (classified + open-source intelligence)
• Anomaly detection tuned to each theater
• Confidence scoring and “why this alert fired” traceability

Output: a prioritized list of signals, each tied to a decision-relevant hypothesis.

2) Add consequence mapping for second- and third-order effects

This is the part Caine implicitly demands. You don’t need a crystal ball; you need structured forecasting.

Useful approaches include:

• Scenario trees (escalation ladders with probabilities that can be updated)
• Agent-based models for adversary reaction patterns
• Narrative risk monitoring (how information operations exploit events)

Output: “If we do X, here are the top 5 ways it turns into Y.”

3) Put a governance wrapper around trust and oversight

If the military is trying to “sustain and scale trust” through Congress, then AI systems must be built for accountability.

Minimum viable governance:

• Audit logs of data sources and model versions
• Clear policy on human authorization points
• Red-team testing for deception and bias
• Post-operation review packages that can be downgraded and shared appropriately

Output: decision support that doesn’t collapse when oversight shows up.

Common questions leaders ask about AI in defense

Can AI predict the next conflict hotspot?

AI can’t reliably “predict wars,” but it can improve early warning by detecting patterns humans miss across large datasets and by updating probability estimates as signals change.

Will AI reduce civilian harm?

It can—if it’s paired with better data, stricter positive identification workflows, and conservative thresholds. AI that speeds targeting without improving identification can increase harm.

What’s the fastest path to value?

Start with analysis triage and sensor fusion. These are high-impact, lower-risk applications that directly support threat assessment without delegating lethal decisions.

Where this goes next for the “AI in Defense & National Security” series

Caine’s remarks are a reminder that the Chairman’s real product isn’t a strike plan—it’s decision clarity under pressure. The U.S. doesn’t have a shortage of platforms. It has a shortage of time, attention, and trust.

If you’re responsible for intelligence, operations, acquisition, or the defense industrial base, the immediate opportunity is straightforward: build AI systems that make second- and third-order effects legible, auditable, and fast. That’s how you get ahead of the technology curve without gambling on hype.

If your team is evaluating AI for national security—threat assessment, intelligence analysis, autonomous systems, cybersecurity, or mission planning—I’d pressure-test one question before anything else: Can your AI explain what it’s seeing in a way a commander can act on and a reviewer can audit?

What would change in your organization if AI could surface the real tradeoffs—before a crisis, not after?