Defense AI Interoperability: Stop Siloed Systems Now

Stove-piped AI is the quiet failure mode nobody wants to own. A defense organization can spend years buying “AI capabilities,” field impressive pilots, and still end up with a force that’s slower—because its AI tools can’t share data, can’t coordinate actions, and can’t explain outputs in a way operators trust.

This matters most in the missions where seconds decide outcomes: drone defense, cyber incident response, contested logistics, time-sensitive targeting, and electronic warfare. If your machine learning model sees something, but your command-and-control workflow can’t ingest it automatically—or your autonomous system can’t act on it safely—you don’t have an AI advantage. You have a demo.

In this installment of our AI in Defense & National Security series, I’m going to be blunt: interoperability is the prerequisite for advanced AI in defense. Not a “nice to have.” A prerequisite. The fastest way to strangle AI in the cradle is to trap it inside closed, vendor-specific stacks that can’t collaborate.

Stove-piped AI is a battlefield liability, not an IT nuisance

Answer first: Stove-piped AI turns fast machine decisions into slow human workflows, and that delay is lethal in high-tempo operations.

The common misconception is that siloed systems are mainly an integration headache. The real cost is operational. When tools can’t talk, you force humans to be the integration layer—copying outputs between screens, reconciling conflicts, and building a coherent picture under pressure.

Here’s what that looks like in practice:

• A sensor-fusion model flags anomalous tracks consistent with a swarm approach.
• A separate threat library (maybe LLM-assisted) has likely tactics and countermeasures.
• A fires/engagement tool has available effectors and firing constraints.
• A common operating picture has the geometry of friendlies, civilians, and restricted zones.

If those are stove-piped, the “AI-enabled” defense becomes a manual relay race. Operators burn time translating alerts into tasks, retyping coordinates, and arguing about which output is authoritative. That’s how you lose to autonomy: not because your models are worse, but because your system-of-systems can’t behave like a system.

The hidden cost: AI that can’t be trusted at scale

Answer first: Closed, opaque AI systems reduce trust because they’re hard to audit, hard to compare, and hard to validate across missions.

Trust in defense AI isn’t abstract ethics talk—it’s the practical question operators ask: “If I follow this recommendation, will it get people killed?”

When systems are closed:

• You can’t easily inspect provenance: Which sensor? Which timestamp? Which model version?
• You can’t reproduce outcomes: Same inputs, different outputs becomes a recurring nightmare.
• You can’t benchmark fairly across vendors because outputs aren’t normalized.
• You can’t instrument safety controls consistently (rate limits, authority boundaries, fail-safes).

Interoperability isn’t just about passing data. It’s about making AI behavior legible, testable, and governable across the enterprise.

Why AI agents break on closed platforms

Answer first: AI agents only create operational advantage when they can orchestrate multiple tools—models, sensors, C2 systems, and effectors—through shared standards.

The RSS article uses a drone-swarm scenario to show what’s at stake. That’s a good example because swarms compress the timeline. You don’t get long analytic cycles. You get bursts of partial information and rapidly changing geometry.

AI agents matter here because they can do more than “analyze.” Properly designed agents:

• pursue commander’s intent (within constraints)
• sequence tasks (detect → classify → recommend → coordinate)
• call other models and services
• manage uncertainty (confidence, alternatives, escalation triggers)
• keep humans in the loop with usable explanations

But that only works if the agent can communicate with other systems through open interfaces.

A concrete “agentic” workflow for swarm defense

Answer first: The winning pattern is an orchestrator agent that calls specialized services and pushes decisions into the common operating picture.

A credible defensive stack (not science fiction) looks like this:

1. Early warning models scan radar/EO/IR/RF patterns for swarm signatures.
2. Classification services estimate platform types, likely payloads, and intent.
3. Tactics prediction models propose likely ingress routes and evasion behaviors.
4. Resource management algorithms match effectors to targets (cost, inventory, probability of kill).
5. C2 presentation pushes a coherent recommendation to the edge, including constraints and confidence.

If your early warning model can’t automatically cue the classification service, or your C2 system can’t ingest recommendations in a structured format, you’ve built a set of disconnected “smart parts.” Not a defense.

Snippet-worthy truth: AI that can’t coordinate is just automation that talks to itself.

Open standards are the force multiplier (and procurement strategy)

Answer first: Open standards turn AI into a plug-in capability instead of a vendor-locked product, enabling faster upgrades, safer integration, and real competition.

Defense leaders often talk about “modularity,” but the market reality is harsh: vendors optimize for renewal. Closed interfaces create switching costs. Switching costs create dependence. Dependence slows modernization.

Open standards change the incentives:

• You can swap models without rebuilding the UI.
• You can add sensors without re-architecting the workflow.
• You can field new agent behaviors without rewriting every integration.

That matters in late 2025 for a simple reason: AI cycles are moving faster than typical defense acquisition timelines. If your architecture can’t accept new models quickly, you’ll always be fielding yesterday’s AI.

What “open” should mean in defense AI (practically)

Answer first: “Open” means standardized data contracts, identity and authorization, observability, and safety controls—not just an API.

When teams say “we have APIs,” they often mean “we have a custom integration backlog.” The practical checklist is tighter than that.

At minimum, defense AI interoperability requires:

• Standard data schemas (tracks, detections, geospatial objects, messages)
• Event-driven interfaces (publish/subscribe for alerts, cues, tasking)
• Identity, access, and policy enforcement across domains (including coalition constraints)
• Model metadata and versioning (so outputs can be traced and reproduced)
• Observability (logs, metrics, audit trails, latency budgets)
• Human authority boundaries encoded into workflows (what can be automated vs. requires confirmation)

If you can’t answer “who/what produced this recommendation and under what policy,” you don’t have a deployable AI system for mission-critical operations.

Interoperability is also a cybersecurity requirement

Answer first: Siloed AI increases cyber risk because it encourages brittle integrations, inconsistent patching, and blind spots in monitoring.

Defense organizations don’t just fight kinetic threats. They fight persistent intrusion attempts—often aimed at data pipelines, identity systems, and supply chain dependencies.

Stove-pipes create predictable weaknesses:

• Inconsistent security baselines: Each system implements auth, logging, and patching differently.
• Shadow integrations: Teams build “temporary” bridges (scripts, exports, manual file transfers) that become permanent.
• Limited monitoring: SOC teams can’t correlate events across systems without common telemetry.

Open standards make security easier to standardize. They also make it easier to detect tampering (data drift, poisoned inputs, anomalous model behavior) because instrumentation can be consistent across tools.

The 3 integrity checks every defense AI pipeline needs

Answer first: If you want trustworthy AI in national security, enforce provenance, drift detection, and auditability.

1. Provenance: Cryptographically or procedurally track where data came from and how it was transformed.
2. Drift & anomaly detection: Alert when inputs or model outputs shift beyond expected bounds.
3. Auditability: Preserve model versions, prompts/parameters (where applicable), and decision context.

These aren’t academic. They’re how you prevent an adversary from “winning” by manipulating your AI’s perception.

A practical roadmap: how to un-stove-pipe without blowing up the mission

Answer first: Start by standardizing interfaces and governance at the seams—then migrate capabilities incrementally.

Most defense organizations can’t rip-and-replace C2, mission systems, and edge applications. The better approach is to create interoperability layers that let old and new coexist while you modernize.

Here’s what works in real programs:

1) Define mission threads and latency budgets

Pick 2–3 mission threads where AI coordination matters immediately (for example: base defense against UAS, cyber incident triage, or ISR-to-fires). For each, define:

• maximum acceptable latency per step
• minimum data fidelity requirements
• human decision points and authority limits

If you don’t define the timeline, you’ll end up “integrating” systems that can’t meet the operational tempo.

2) Standardize the “data contract” before you standardize the model

A lot of teams start by shopping for the best model. Start with data contracts and eventing.

• Normalize track objects, detections, and message formats.
• Define confidence scoring conventions.
• Require model/version metadata in every output.

Models will come and go. Your mission workflows shouldn’t.

3) Build an agent-ready orchestration layer

You don’t need full autonomy on day one. You need an architecture that can support it safely.

• Implement service discovery (what tools exist, what they do).
• Add policy checks (what actions are permitted).
• Add structured reasoning artifacts (why the recommendation was made).

This is how you scale from “AI suggests” to “AI coordinates.”

4) Procure interoperability, not just capability

Write requirements that force openness:

• Government-owned or government-controlled interface specs
• Non-proprietary schemas for core mission objects
• Exportability of logs, telemetry, and model outputs
• Clear rules for integrating third-party models

If you buy a closed stack because it’s faster this quarter, you’re prepaying years of integration debt.

Where this is headed in 2026: coordinated AI across domains

Answer first: The next competitive advantage is coordinated AI across air, sea, land, cyber, and space—driven by shared standards and mission-aligned agents.

As the AI in Defense & National Security space matures, the winners won’t be the organizations with the flashiest single model. They’ll be the ones that can:

• fuse sensor data across classification and coalition boundaries
• push decision support to the edge with consistent policy controls
• swap AI components quickly as threats adapt
• demonstrate trust through auditability and transparent behavior

A siloed architecture can’t do that, no matter how good the model is.

Closed systems had a moment because they were easy to procure and easy to demo. That moment is ending. The operational environment—especially under contested communications and mass autonomy—punishes anything that can’t coordinate.

If you’re responsible for defense AI strategy, architecture, or acquisition, the question worth sitting with is simple: Are your AI tools building a shared brain for the force—or a collection of smart gadgets that can’t cooperate when it counts?