AI-Ready National Security Strategy: Fix the Gaps

A sitting member of Congress calling a new National Security Strategy “1930s foreign policy” isn’t just a spicy soundbite—it’s a warning about strategic drift. Rep. Don Bacon, a retired Air Force brigadier general, didn’t merely disagree with the direction of the Trump administration’s latest strategy; he argued it undercuts alliances and relies on assumptions that don’t survive contact with today’s threat environment.

Here’s why this matters to anyone building, buying, or governing AI in defense and national security: when strategy is contested, budgets get weird, priorities whipsaw, and programs stall. AI initiatives are especially vulnerable because they depend on data access, interoperable networks, and stable mission requirements—all things that unravel when policy becomes a political tug-of-war.

This post uses Bacon’s critique as a jumping-off point to talk about what a 21st-century National Security Strategy actually needs to be: measurable, auditable, and built for AI-assisted decision-making—without letting algorithms become unaccountable power centers.

Why “1930s foreign policy” is a modern AI problem

A National Security Strategy isn’t just a document; it’s a coordination mechanism. It tells the department, the services, allies, industry, and Congress what to optimize for. When a strategy is perceived as turning inward—or weakening alliance commitments—the impacts cascade into the technical layer where AI lives.

Bacon’s argument (as reported from his interview) centers on two points that AI programs can’t ignore:

• Alliances and credibility are part of deterrence. If allies doubt US staying power, information-sharing and joint planning become harder.
• A one-year plus-up doesn’t fix a multi-year modernization gap. AI capability isn’t a one-time purchase; it’s a lifecycle—data, models, compute, deployment, retraining, evaluation.

The reality? A strategy that’s vague or politically brittle creates a vacuum. And vacuums get filled—by inertia, by lowest-common-denominator compliance, or by fragmented “pilot projects” that never scale.

Strategy volatility breaks the AI delivery pipeline

AI in defense is tightly coupled to mission definitions. If the strategy shifts from forward defense to a narrower posture, for example, the priorities for ISR, cyber, space resilience, logistics, and contested communications change immediately.

That volatility breaks:

• Data strategy (what data you can collect, share, retain)
• Model strategy (what you train for and how you evaluate)
• Acquisition strategy (what you can justify, contract, and sustain)

A strong defense AI program isn’t “build a model.” It’s build a system that still works when priorities shift.

Alliances are an AI advantage, not a diplomatic nice-to-have

Answer first: Alliances make military AI better because they expand training data, increase operational coverage, and improve validation against real-world conditions.

If you’re serious about AI-enabled defense planning, you should treat NATO-style cooperation as a technical force multiplier, not just a political preference.

Shared data is shared deterrence

Coalitions generate diverse operational data: different terrains, sensors, doctrines, adversary behaviors, and electronic environments. That diversity is exactly what reduces overfitting—one of the silent killers of operational AI.

But data sharing doesn’t happen on vibes. It requires:

• Interoperable data standards (so “common operating picture” means the same thing)
• Cross-domain solutions and clean-room workflows
• Governance for who can see what, when, and why

A strategy that signals diminished alliance commitment tends to produce the opposite behavior: partners hedge, restrict sharing, and build parallel systems. AI suffers immediately.

Combined operations need combined model assurance

The hard part isn’t building a computer vision model; it’s proving that it behaves safely and predictably across contexts. In coalition settings, you need to answer questions like:

• Does the model maintain performance with allied sensor variants?
• Do confidence scores mean the same thing across systems?
• Can an ally reproduce the evaluation results, or are they locked behind proprietary tooling?

If your National Security Strategy doesn’t prioritize alliance integration, you’re choosing a future where AI is less testable, not just less diplomatic.

Budgets, reconciliation plus-ups, and the AI sustainment trap

Answer first: A one-year funding surge doesn’t create enduring AI capability unless it explicitly funds sustainment—data operations, model monitoring, retraining, and security.

Bacon’s warning that a large plus-up “is not enough” lands hardest in AI. Traditional procurement thinking still dominates: buy hardware, field it, move on. AI is different.

AI readiness is a recurring cost center

If you’re leading a defense AI program, your real cost drivers look like this:

1. Data engineering and labeling (often 40–60% of effort in practice)
2. Model evaluation at operational edges (EW conditions, low bandwidth, degraded GPS)
3. MLOps / model operations (monitoring drift, retraining cadence, rollback plans)
4. Cybersecurity for models and pipelines (poisoning, exfiltration, model theft)
5. Compute and accreditation (including controlled environments)

A budget that spikes for a year but doesn’t lock in these recurring lines sets you up for a predictable failure mode: great demos, weak fielding.

What Congress should demand: capability metrics, not pilot counts

If lawmakers want accountability—especially amid leadership disputes—the best move is to require AI programs to report operationally meaningful metrics. Not “number of AI projects.” Metrics like:

• Time from data collection to deployable model update (days/weeks)
• Percentage of models with continuous monitoring in production
• Measured reduction in analyst time for specific workflows
• False positive/false negative rates under contested conditions
• Cyber red-team results against the model supply chain

These are “boring” numbers. They’re also the only numbers that protect taxpayers and warfighters.

AI can improve policy oversight—if it’s designed for auditability

Answer first: AI-assisted accountability works when models are auditable, reproducible, and bounded by clear human decision authority.

Leadership criticism—like Bacon’s sharp view of Secretary Hegseth’s tenure—creates a political dynamic that’s common in defense: people argue about outcomes, but can’t agree on the facts. AI can help, but only if it’s used as decision support, not a black box.

Where AI actually helps: “show your work” at scale

The highest-value applications are often unglamorous:

• OSINT triage and entity resolution to reduce missed signals
• Predictive maintenance to increase aircraft/vehicle readiness rates
• Supply chain risk analytics to flag single points of failure
• Cyber anomaly detection across multi-cloud and tactical networks
• Course-of-action simulations that expose assumptions in planning

These tools don’t replace commanders or policymakers. They do something more important: they make assumptions explicit and traceable.

The non-negotiables for responsible defense AI

If you want AI to increase trust instead of inflaming politics, build in these guardrails:

• Data lineage: every model output should trace back to sources and transforms
• Evaluation transparency: clear test sets, edge cases, and failure modes
• Human-in-the-loop triggers: when confidence drops, the system escalates
• Model rollback: field units can revert to last-known-good versions
• Adversarial testing: red-team models like you’d red-team software

Here’s a line I’ve found useful internally: “If it can’t be audited, it can’t be operational.”

What an AI-ready National Security Strategy should include in 2026

Answer first: An AI-ready strategy treats data, interoperability, and resilience as strategic assets—on par with platforms and munitions.

A lot of strategies read like priorities and slogans. The AI era demands something closer to an executable plan. If I were marking up an AI-ready National Security Strategy, I’d look for commitments in five areas.

1) Data as a strategic resource

Not a paragraph—an operating model:

• Who owns mission data?
• How is it shared across services and allies?
• What’s the retention policy for training and evaluation?

2) Interoperability as a default

Coalition operations demand it. So do joint operations.

• Common data formats and APIs
• Cross-domain patterns that don’t take years to accredit
• Procurement requirements that prevent vendor lock-in for critical data

3) AI assurance as a core readiness measure

Readiness isn’t just fuel and parts anymore.

• Continuous evaluation under EW/degraded comms
• Model monitoring in the field
• Clear doctrine for when AI advice is used and when it’s ignored

4) Cybersecurity for the model supply chain

If the model is compromised, deterrence is compromised.

• Secure training pipelines
• Tamper-evident datasets
• Controls for insider risk and model exfiltration

5) Budgeting that matches the lifecycle

Multi-year appropriations and sustainment lines where possible.

• Fund MLOps like you fund maintenance
• Tie pilots to transition criteria (or kill them)
• Require measurable outcomes for renewals

Practical next steps for defense leaders and industry teams

Answer first: You can reduce strategy volatility risk by building AI programs that are modular, measurable, and coalition-ready.

If you’re trying to turn AI into real operational capability—amid shifting strategies and headlines—these moves help right now:

1. Write mission metrics before you write model requirements. If you can’t define success operationally, you’re buying demos.
2. Design for degraded operations. Assume low bandwidth, contested GPS, and sensor outages from day one.
3. Treat data access as the critical path. Most schedule slips are data slips.
4. Build an evaluation harness you can rerun every release. Reproducibility beats “expert judgment” when politics heats up.
5. Plan for coalition constraints early. Classification, releasability, and interoperability aren’t add-ons.

Where this leaves the political fight—and why AI teams should care

Bacon’s critique is a reminder that national security strategy is never purely technical. It’s a set of bets about allies, adversaries, and resources. When those bets are contested, AI becomes either a stabilizer or a casualty.

If AI is built as a transparent decision-support layer—auditable, measurable, resilient—it can lower the temperature. People can still disagree, but they’ll argue over shared evidence and explicit assumptions.

If AI is built as a collection of opaque pilots with fragile data pipelines, it will amplify distrust and waste money. And the next strategy shift will wipe it out.

Defense leaders heading into 2026 should ask one forward-looking question: Are we building AI that survives political change—and still earns operational trust?