Algorithmic Warfare: What Gaza Signals for Democracies

A single number captures the direction of travel: Israel struck more than 15,000 targets in the first 35 days of the Gaza war. That tempo doesn’t come from bigger staffs or longer shifts. It comes from software.

For leaders working in the AI in Defense & National Security space, Gaza isn’t just another conflict to analyze—it’s a real-world stress test for what happens when AI-enabled targeting meets counter-insurgency. The core issue isn’t whether AI will touch lethal decision-making (it already does across major militaries). The issue is whether democracies can use AI for speed and scale without turning judgment into a rubber stamp.

The uncomfortable truth is that many Western institutions are already building the plumbing for algorithmic warfare: cloud-based intelligence systems, fused sensor networks, automated detection, and decision support tools that compress the time between “seen” and “hit.” Gaza shows what can go wrong when that compression becomes the goal.

Algorithmic counter-insurgency is a doctrine, not a tool

Algorithmic counter-insurgency isn’t “AI in war.” It’s a way of fighting that treats population-scale surveillance and high-tempo strikes as the primary method of control. The technology matters, but the doctrine matters more.

Israel’s approach to Gaza and the West Bank has historically leaned on coercive stability: blockades, surveillance saturation, and calibrated violence designed to deter and contain rather than reconcile. What’s changed since Oct. 7 is the automation of the pipeline—systems reported as Lavender and Gospel, associated with Israel’s intelligence ecosystem, that accelerate how targets are identified, nominated, and queued.

When AI mediates that process, counter-insurgency shifts in three practical ways:

• Suspicion becomes a score. Networks, metadata, and associations get converted into “risk” outputs.
• Tempo becomes a metric. Success is measured in targets processed and strikes executed.
• Deliberation becomes friction. Human review is still “in the loop,” but the loop gets thinner as operational pressure rises.

Here’s the stance I’ll take: If your organization measures speed as success, you’re quietly training your force to treat legal and ethical review as overhead. That’s not a software problem. That’s governance failing to keep up with procurement.

Why this matters to Western defense programs now

Western militaries are already investing in the same enabling layers—multi-source ISR fusion, automated object detection, sensor-to-shooter orchestration, and cloud-scale analytics. The U.S. institutionalized parts of this trajectory through efforts like Project Maven, while China and Russia pursue their own variations of accelerated kill chains.

Gaza adds a missing data point: speed can outpace judgment even when a “human-in-the-loop” policy exists on paper.

The promise of AI targeting is real—and it’s not the main risk

AI can improve targeting discipline under strict rules. That’s the strongest pro-AI argument, and it deserves to be taken seriously.

Human-led targeting has repeatedly produced high civilian tolls in counter-insurgency environments. Historical Gaza operations—Cast Lead (2008–2009) and Protective Edge (2014)—are often cited for heavy civilian harm. The point isn’t to litigate every figure; it’s to recognize that “humans only” has never been a clean solution, especially under stress, fear, and permissive rules of engagement.

AI systems have real advantages:

• Consistency under fatigue: algorithms don’t get angry, scared, or exhausted.
• Pattern processing: models can sift signals and anomalies at a scale no analyst team can match.
• Queue management: AI can triage, prioritize, and route tasks across distributed teams.

But the main risk isn’t “AI makes mistakes.” The main risk is AI changes what organizations consider acceptable evidence—and then scales that new threshold.

If a command accepts a loose proxy (for example, a demographic heuristic or a network association) as sufficient for nomination, AI will execute that policy with industrial efficiency.

AI doesn’t eliminate bad assumptions. It standardizes them—and then multiplies them.

Three failure modes that turn decision support into decision replacement

Gaza highlights predictable human-automation dynamics that show up in other national security AI deployments too (intelligence analysis, watchlisting, border screening, and domestic surveillance). Three patterns matter most for democratic militaries.

1) Compression risk: the review becomes a checkbox

As the detection-to-strike window collapses, human review often degrades into confirmation.

Reports referenced in the source material describe approval cycles that can be extremely short for AI-nominated targets. Whether those exact numbers hold across all units and periods, the organizational tendency is well-established: when tempo is rewarded, people conform to tempo.

Practical indicators you’re in compression risk territory:

• Review times trend downward month over month.
• Overrides become rare (and socially costly).
• Teams stop asking for underlying features/data because “the model already did that.”

If you’re building AI-enabled targeting workflows, you need to design mandatory friction for certain categories (residential structures, low-confidence nominations, stale data, or high-collateral environments). Without friction, you don’t have oversight—you have theater.

2) Scale risk: mass nomination normalizes thin evidence

Scale risk shows up when the system can nominate far more targets than humans can meaningfully validate.

This isn’t hypothetical. High-tempo strike numbers signal a pipeline optimized for throughput. Scale shifts the culture:

• The default becomes “process it” instead of “interrogate it.”
• Quality control becomes sampling rather than case-by-case evaluation.
• Legal review can drift into template logic.

The hard lesson: if the model can generate targets faster than your governance can audit them, your governance is already losing.

3) Error externalization: the machine becomes a moral buffer

When a model is involved, people can start treating harm as a technical artifact instead of a command choice.

That psychological shift is lethal to accountability. You’ll hear it in language:

• “The system flagged it.”
• “The model confidence was high.”
• “The data suggested…”

But international humanitarian law doesn’t evaluate model confidence. It evaluates distinction, proportionality, precautions, and the quality of the decision-making process.

If your system can’t explain what drove a recommendation—what features mattered, which sources contributed, what was missing, what was overridden—you’re not building decision support. You’re building plausible deniability.

Will Israel’s model proliferate to Western militaries?

Yes, parts of it will—because the incentives align. Procurement, alliance relationships, and operational demands push in the same direction: faster fusion, faster decisions, faster effects.

Several channels accelerate diffusion:

• Defense exports and vendor ecosystems: AI-enabled sensor-to-shooter systems are already marketed globally.
• Cloud and data infrastructure dependencies: Western tech stacks increasingly resemble each other, which lowers integration friction for targeting-relevant analytics.
• Shared intelligence environments: coalition operations normalize shared tooling, shared data standards, and shared doctrine language.

But there’s a second proliferation pathway that gets less attention: domestic security spillover.

The architectures that connect sensors to shooters in wartime can connect cameras to detention in peacetime. Once you have population-scale data ingestion, entity resolution, network analytics, and risk scoring, repurposing is often more a policy decision than a technical leap.

That’s why Gaza isn’t only a military ethics debate. It’s a national security governance debate—about what your institutions will permit when software makes mass processing possible.

A practical governance stack for AI-enabled targeting

Banning military AI isn’t realistic, and it’s not even desirable. The right goal is disciplined use: verifiable controls that survive operational pressure. Here’s what that looks like in practice—especially for democratic defense institutions that must answer to law, oversight bodies, and public legitimacy.

1) Export controls that target capabilities, not buzzwords

Export controls should focus on “targeting-relevant AI” capabilities: mass nomination tools, sensor-to-shooter orchestration, identity resolution at population scale, and automated target development workflows.

What works operationally is conditional licensing:

• auditable human-in-the-loop thresholds
• documented model risk management
• civilian harm mitigation requirements
• suspension triggers tied to verified violations

This matters for Western countries because supply chains are international. If you don’t set conditions early, you’ll inherit the consequences later.

2) Auditability as a non-negotiable system requirement

If AI influences lethal decisions, you need post hoc reconstructability. Not “we can explain the model in principle,” but “we can reconstruct this strike recommendation.”

Minimum audit artifacts should include:

• versioned model outputs and confidence bands
• feature/source provenance (what data contributed)
• structured decision logs (who approved, who overrode, why)
• red-team results under adversarial manipulation scenarios

If a system can’t be audited, it shouldn’t be allowed to nudge lethal action.

3) Proportionality tied to validated advantage, not operational tempo

Proportionality can’t be allowed to drift into throughput logic (“more targets per day” as the hidden objective).

A practical approach is to codify constraints such as:

• tighter collateral ceilings when model confidence is low
• extra legal review for strikes on residential structures
• explicit bans on relying solely on proxy variables or association-based suspicion
• required pauses when data recency thresholds aren’t met

These aren’t theoretical ideals. They’re the kinds of constraints that remain legible under pressure.

4) Standing civilian harm assessment with teeth

Civilian harm tracking should be a permanent capability, not an after-action PR effort.

A credible mechanism combines:

• forensic imagery and blast analysis
• casualty verification workflows
• access to strike logs and decision traces
• feedback loops that change tactics, not just reports

Democracies don’t maintain legitimacy by claiming precision. They maintain legitimacy by showing their homework.

5) Narrow international rules for AI in targeting (now, not later)

Waiting for a grand global agreement on autonomy is a recipe for drift. A narrower “AI in targeting” addendum is more achievable: minimum obligations when AI contributes to lethal decision-making.

The essentials are straightforward:

• meaningful human judgment (including time to review)
• limits on bulk personal data use for target development
• independent auditability requirements
• public reporting on civilian harm metrics and remediation

If these requirements sound demanding, that’s because they are. War is demanding. Democracies should demand more of themselves than “the model said so.”

What defense and security leaders should do in 2026 planning cycles

Budgets and program plans for the next cycle are where doctrine becomes real. If you’re responsible for AI in defense programs—targeting, ISR fusion, intelligence analysis, or mission planning—three moves pay off immediately.

1. Write “governance into the architecture.” Build logging, review friction, and audit hooks as core requirements, not add-ons.
2. Measure the right thing. If your KPIs are speed and volume, you’ll get speed and volume. Add KPIs for overrides, audit pass rates, false positive discovery, and civilian harm reduction.
3. Stress-test for proxy dependence. Identify where models might rely on crude correlates (demographics, geography, social associations) and force the system to prove it can operate without them.

The broader lesson from Gaza is strategic, not technical: automation makes it easier to win the spreadsheet while losing legitimacy.

Democracies can adopt AI-enabled targeting without importing algorithmic counter-insurgency as a default approach—but only if they treat governance as part of combat power.

If your organization is building or buying AI for defense operations, the question to ask internally is simple: When the tempo spikes, do our controls get stronger—or do they disappear?