AI in Trump’s 2025 Security Strategy: What’s Missing

A national security strategy usually tries to sound like it will outlive the president who signs it. The December 2025 strategy doesn’t. It reads as personal, culturally charged, and tightly scoped around “core interests,” with immigration and hemispheric control pushed to the top of the list.

For people building, buying, or governing AI in defense and national security, the document is useful for a different reason: it signals where demand will surge (border enforcement, missile defense, supply-chain security) and where execution will likely fail without modern AI capabilities (data fusion, cyber defense at machine speed, resilient autonomy). It also reveals a deeper tension—when strategy becomes partisan identity, AI governance becomes harder, not easier.

Below is a practical read of the strategy through an AI lens: what aligns, what’s absent, and what defense leaders should do next if they want outcomes rather than slogans.

The strategy centers on politics—AI needs institutions

The most immediate implication is not technological; it’s organizational. When a national security strategy elevates one leader as the protagonist, agencies and allies start planning for discontinuity.

From an AI perspective, personalization is a problem because AI programs succeed through repeatable institutional processes:

• Stable requirements and funding across fiscal years
• Shared data standards across agencies and combatant commands
• Model validation, red-teaming, and safety approvals that survive leadership churn

AI readiness is governance readiness. If your strategy is effectively campaign messaging, your AI portfolio risks becoming a set of disconnected pilots instead of an operational capability.

Practical implication: build “continuity layers”

If you’re in DoD, DHS, IC, or an allied ministry, the play is to design AI initiatives with built-in durability:

1. Mission-aligned metrics (e.g., time-to-detect, false positive rate, analyst workload reduction)
2. Interoperable data products (schemas, APIs, labeling guidance)
3. Model cards + audit trails that allow a program to be defended under changing political oversight

This matters because the strategy demands major shifts (hemisphere first, burden-shifting, Golden Dome). Those only work if execution is institutional, not charismatic.

Immigration and border security: AI can help, but it can also backfire

The strategy elevates immigration to the “primary element of national security.” That instantly increases the importance of AI-driven surveillance, identity resolution, anomaly detection, and logistics optimization.

But border AI is where operational need and civil liberties collide fastest. Get it wrong and you create:

• Overbroad watchlists and mistaken identity at scale
• Biased risk scoring tied to incomplete or skewed historical data
• Security theater: more alerts, less true interdiction

What works: AI as triage, not judgment

The best-performing pattern I’ve seen is treating AI as a triage layer that prioritizes human attention, not a final decision-maker.

Use cases that fit that pattern:

• Multi-sensor fusion (tower cameras + radar + ground sensors) to cut nuisance alarms
• Computer vision for object detection in fixed corridors (with strict retention limits)
• Language AI to accelerate document processing and case triage (with human review)

A simple operational metric that keeps programs honest: “minutes of human attention per confirmed event.” If AI reduces that without raising error costs, it’s working.

What’s missing in the strategy

The strategy is loud about border control but quiet about the hard parts:

• Data-sharing rules between federal, state, and partner nations
• Model accountability when errors affect lawful travelers
• Counter-AI threats (spoofing cameras, adversarial patches, synthetic IDs)

If immigration enforcement becomes the organizing lens for national security, then AI assurance becomes a top-tier mission, not a compliance afterthought.

“Trump Corollary” and hemisphere-first: AI reshapes force posture

Prioritizing the Western Hemisphere implies a different operational map: more maritime domain awareness, more counter-illicit logistics, more infrastructure protection, and more partner capacity building.

This is tailor-made for AI—especially wide-area sensing and analytics:

• Tracking suspicious vessel patterns (AIS gaps, rendezvous behavior, route anomalies)
• Detecting illicit supply-chain manipulation (shipping anomalies, vendor risk scoring)
• Monitoring critical infrastructure threats (cyber + physical signals)

The hidden dependency: allied and partner data

Hemisphere-first only works if the U.S. can collaborate with partners quickly. In AI terms, that means:

• Shared data standards and labeling conventions
• Federated analytics where data can’t cross borders
• Joint model testing to ensure tools behave the same across environments

A real risk: a more transactional approach to alliances can reduce willingness to share data—the fuel AI needs. If data-sharing dries up, AI performance drops, and leaders compensate with more intrusive collection at home. That’s a bad cycle.

Culture war framing: the fastest way to poison AI governance

The strategy’s cultural focus (traditional values, DEI as institutional decay, “spiritual health”) matters for AI because the AI workforce and AI oversight processes depend on trust.

Here’s the blunt truth: AI programs fail when people stop trusting how decisions are made. Not because the model is weak, but because adoption collapses.

Where culture-war logic becomes operationally dangerous:

• AI-enabled personnel screening and insider-threat monitoring
• Social media analysis for “stability” signals
• Diaspora politics framed as a security threat

These areas require careful legal and policy guardrails. If they’re pulled into partisan framing, you get overcollection, weaker oversight, and less cooperation from the very communities and tech talent the mission depends on.

A better approach: separate mission risk from identity politics

If agencies want to deploy AI in sensitive domestic-adjacent missions, they should insist on:

• Narrowly scoped features (behavioral signals over demographic proxies)
• Independent testing for disparate impact
• Clear escalation paths (AI flags → human review → documented decision)

Put differently: operational legitimacy is a capability.

Golden Dome missile defense: AI is required, and it’s the hardest kind

The strategy’s “Golden Dome” concept implies a layered homeland missile defense ambition far beyond limited rogue-state defense.

Whether or not the architecture is feasible, one piece is non-negotiable: AI-enabled sensor fusion and decision support. Missile defense compresses time. The system must integrate space-based sensors, ground radar, tracking, discrimination, and intercept planning—often under seconds-to-minutes constraints.

Where AI fits—and where it must be constrained

AI can improve:

• Target discrimination (decoys vs real warheads) using multi-modal sensor patterns
• Track correlation across radars and space sensors
• Interceptor allocation optimization under uncertainty

But missile defense is also where “automation bias” can be catastrophic. The right design principle is human-commanded, machine-assisted—with automation reserved for narrowly tested sub-tasks.

A practical procurement test: require vendors to demonstrate performance under degraded conditions (sensor outages, spoofing attempts, GPS denial, comms latency). If a model only works in perfect lab conditions, it’s not a defense capability.

Burden-shifting to allies: AI interoperability becomes the real burden

The strategy emphasizes allies spending 5% of GDP (per its description of the Hague pledge) and frames the U.S. as done “propping up” the order.

If allies spend more but can’t operate AI-enabled systems together, that money buys friction.

The new readiness metric: “interoperable AI”

Interoperability used to mean radios, fuel, munitions compatibility. Now it also means:

• Shared data formats for ISR products
• Compatible model assurance standards
• Secure cross-domain solutions that allow data to move from classified to operational systems
• مشترک (shared) procedures for human oversight in autonomy

If you want a single sentence to brief leadership: Allied deterrence will increasingly depend on allied data-sharing and AI compatibility, not just force size.

Economic nationalism and reindustrialization: AI for the defense industrial base

The strategy puts reindustrialization and supply-chain independence at the center. That’s one of the most AI-compatible elements—because industrial resilience is largely a data problem.

High-value AI use cases for the defense industrial base:

• Predictive maintenance for machine tools and depot lines
• Demand forecasting for munitions, spares, and microelectronics
• Supplier risk analytics that blends cyber posture, financial health, and geopolitical exposure
• Counterfeit detection using computer vision on components and packaging

The tradeoff nobody likes to say out loud

If the U.S. wants reshored production and a larger defense budget, it needs productivity gains. AI is the productivity lever, but only if data is clean and processes are modernized.

Buying AI without fixing data pipelines is a tax, not an investment.

Three gaps the strategy leaves open—and how AI can fill them

The document is clear on priorities and vague on execution. Three gaps stand out.

1) No explicit AI doctrine for intelligence and surveillance

The strategy’s worldview implies expanded surveillance and faster decision cycles, but it doesn’t articulate an AI approach to:

• Analyst-machine teaming n- Managing classification barriers
• Model drift, bias, and adversarial deception

What to do: create an AI doctrine that treats deception and data integrity as first-order threats.

2) Cybersecurity is implied, not operationalized

Any sovereignty-first approach increases cyber retaliation risk. AI is central to cyber defense now because adversaries already automate recon, phishing, and exploit chaining.

What to do: prioritize AI-enabled security operations that reduce mean-time-to-detect and mean-time-to-respond, with strong controls against model poisoning.

3) Autonomy is referenced conceptually, not governed

Autonomous systems matter across border operations, maritime domain awareness, and contested theaters. The strategy doesn’t address safety thresholds, escalation risks, or how humans stay meaningfully in charge.

What to do: standardize autonomy assurance: testing, simulation, and clearly defined “no-go” conditions.

What leaders should do in Q1 2026

If you’re responsible for capability delivery—program office, defense tech firm, systems integrator, or policy team—these are the moves that map strategy to execution:

1. Stand up a mission data layer: catalog sources, set retention rules, define labeling standards
2. Measure operational value: pick 3 metrics per use case (speed, accuracy, workload)
3. Harden against adversarial AI: test spoofing, poisoning, synthetic identities, and sensor deception
4. Bake in auditability: decisions must be explainable enough to survive oversight and litigation
5. Plan for interoperability: assume coalition operations and cross-agency data needs from day one

If the strategy is going to be narrower and more coercive, AI systems have to be more disciplined and more accountable—otherwise they’ll produce noise, scandals, and strategic surprise.

Where this leaves the “AI in Defense & National Security” story

This strategy signals demand for AI in surveillance, intelligence analysis, autonomous systems, and cybersecurity—but it doesn’t provide the scaffolding required to deploy those capabilities responsibly at scale.

For the U.S. and its partners, the real test in 2026 won’t be whether AI exists in prototypes. It’ll be whether AI-enabled operations can deliver measurable results under scrutiny: contested data, legal constraints, public oversight, and adversaries who actively try to fool the models.

If you’re planning your roadmap now, the guiding question isn’t “How do we add AI?” It’s: Which missions will break first without trustworthy AI, and what governance do we need before we automate anything?