Introduction: The Algorithmic Battlespace

Warfare is undergoing a profound revolution, not in explosive yield, but in the cognitive processes that select targets. The advent of Artificial Intelligence (AI) is transforming military targeting from a deliberate, human-intensive process into a high-velocity, data-driven enterprise. Israel stands at the forefront of this shift, leveraging technological innovation to address asymmetric threats (Cohen, Eisenstadt, & Bacevich, 1998).

This policy brief examines Israel’s use of AI for target identification through the lens of cyber conflict, defined as the intersection of digital attack, defense, and exploitation within sociotechnical systems that mediate kinetic force. The urgency of this inquiry intensified following the October 7, 2023 attacks and the ensuing Gaza war, during which Israel reportedly expanded its reliance on AI-driven decision-support systems, including The Gospel and Lavender, to generate targets at industrial scale (Abraham, 2024; The Guardian, 2024).

The central question guiding this analysis is: How does Israel’s integration of AI into targeting processes reshape the dynamics of cyber conflict, and what are the strategic, legal, and normative implications for international security?

The argument presented is that Israel’s pursuit of AI targeting represents a rational, Realist-driven quest for military superiority. However, by creating a targeting ecosystem of unprecedented speed, scale, and data-dependency, it inadvertently introduces critical cyber-physical vulnerabilities and strains International Humanitarian Law (IHL) to its conceptual limits, creating a responsibility gap that may strategically outweigh tactical gains.

Theoretical Framework: A Nested Approach

Understanding algorithmic warfare requires integrating multiple theoretical perspectives:

• Strategic Logic (Realism and Securitization)

From a Realist perspective, Israel’s investment in AI targeting offsets demographic and geographic vulnerabilities while maintaining a Qualitative Military Edge (Hammes, 2021). The October 7 attack constituted a strategic shock, accelerating AI deployment under a securitized narrative of existential threat (Buzan, Wæver, & de Wilde, 1998; Kas, 2025).

• Sociotechnical Systems (Actor-Network Theory)

AI targeting cannot be analyzed as a mere tool. Actor-Network Theory reveals the kill chain as a cyber-physical assemblage of sensors, algorithms, human analysts, and infrastructure (Latour, 2005; Lawson, 2022). Each node represents both a source of strength and a potential vulnerability.

• Normative Contestation (IHL and Critical Security Studies)

The automation of targeting challenges core IHL principles of distinction, proportionality, and precaution (ICRC, 2016). It also raises profound questions about accountability when decision-making is distributed across human and non-human actants (Sparrow, 2007).

This nested framework captures how strategic imperatives drive technological implementation, which in turn generates normative contestation that feeds back into strategic calculations.

The Israeli Model: Architecture and Strategic Logic

Israel’s AI targeting architecture has evolved into a two-tiered sociotechnical system, crystallizing in the post-October 7 period.

Tier 1: Infrastructure Targeting (Pre- and Post-2023)

This layer automates targeting of physical assets. The Gospel (Habsora) functions as an AI-enabled recommender system for buildings, tunnels, and command nodes, processing multi-source data to propose targets ranked by probability (Bergman & Rubin, 2021). Fire Factory automates strike package coordination, calculating munitions and estimated collateral damage (Ahronheim, 2021).

Tier 2: Person-Centric Targeting (Post-October 7)

This layer represents a qualitative leap. Lavender processes vast datasets, including intercepted communications, to flag individuals suspected of militant affiliation, assigning each a target suitability score (Abraham & Local Call, 2024). Reports indicate it generated lists of up to 37,000 potential human targets during the Gaza campaign (Sabbagh & McKernan, 2024). Ancillary tools, like Where’s Daddy? Track, flagged individuals to optimize strike timing (HRW, 2024).

Figure 1: The Israeli AI Targeting Stack: A Two-Tiered Actor-Network

Tier 1 (Foundational Infrastructure-Targeting Layer) and Tier 2 (Expanded Person-Centric Targeting Layer), connected through the Integrated Human Decision Layer to the Strike and Battle Damage Assessment Layer. Data flows bidirectionally between tiers, with human analysts positioned as validators under temporal pressure.]

• The Human Decision Layer

While humans remain formally in the loop, system design creates pressures that reshape their role. Reports describe analysts approving dozens of targets per hour, shifting from active deliberation to rapid validation under cognitive overload (Abraham & Local Call, 2024). This dynamic operationalizes automation bias, where algorithmic outputs receive deferential treatment.

• The Factory Logic

The integrated system operates as a target generation factory: Tier 1 produces infrastructure targets, Tier 2 produces human targets, human operators ratify at machine-compatible speeds, and the strike-BDA loop executes and learns. This factory logic served post-October 7 war aims by enabling unprecedented campaign intensity, but it also institutionalizes a proportionality threshold reportedly codified as 15-20 authorized civilian casualties per suspected operative (Sabbagh & McKernan, 2024).

Analysis: The Cyber-Conflict Paradox

The implementation of Israel’s AI targeting stack creates a new operational paradigm best understood through cyber conflict theory. The system’s defining characteristic, its unprecedented speed and scale, is also its primary source of fragility.

• Strategic Advantages

The efficiency gains are tangible. By automating observation and orientation phases across both tiers, the system compresses the OODA loop, enabling generation and prosecution of tens of thousands of targets. This creates a tempo designed to outpace adversary adaptation. The network acts as a profound force multiplier, transferring cognitive and moral risk from human intuition to algorithmic processing. The perceived objectivity of data-driven recommendations creates an epistemic shield, framing decisions as technical outputs rather than human judgments (Asaro, 2012).

• Systemic Vulnerabilities

However, each node and dataflow represent a potential failure point. An adversary aware of data signatures feeding Lavender could engage in strategic data poisoning, deliberately creating deceptive patterns to corrupt the model’s classification logic. The reported reliance on Lavender after it reached purported high accuracy demonstrates the peril of trusting models whose training data may be contaminated by adversarial deception (Abraham & Local Call, 2024).

Adversaries can also spoof sensor data feeding both tiers, including generating fake GPS signals or manipulating communications metadata. The person-centric focus of Lavender makes it uniquely vulnerable to adversarial AI attacks, where subtle manipulations of digital behavior could misclassify operatives as civilians or vice-versa (Huang et al., 2011).

Reliance on foreign cloud providers introduces geopolitical vulnerability. Changes in corporate policy or export controls could abruptly cut off access to essential computing power, directly degrading operational capability (AP, 2025).

• The Human Factor as Vulnerability

The system’s efficiency relies on human validators trusting its output at industrial pace, creating overwhelming automation bias. Under such conditions, human oversight becomes perfunctory, a cognitive vulnerability deliberately engineered by tempo requirements (Cummings, 2004). Over-reliance risks de-skilling intelligence analysts, eroding the contextual understanding and ethical deliberation that IHL requires.

• Adversary Adaptation

Adversaries have demonstrated rapid adaptation. Cyber camouflage involves employing disciplined operational security to reduce digital signatures below detection thresholds. Data flooding involves deliberately increasing noise by simulating militant-associated signals from protected civilian sites, triggering false positives and eroding system credibility. The deliberate entrenchment within civilian infrastructure exploits the AI’s greatest weakness: the inability to perform reliable distinction in dense urban terrain.

• The Core Paradox

The pursuit of hyper-efficiency through integration makes the system systemically fragile. The compressed OODA loop has no slack for error correction; a poisoned data stream can propagate through the network at machine speed. In cyber-conflict terms, the new center of gravity is the data fusion pipeline and the algorithmic model itself. Protecting this digital core becomes a continuous, silent war that determines the efficacy of every kinetic strike.

Normative and Legal Contestation

The deployment of Israel’s expanded AI targeting stack collides catastrophically with the contextual, subjective tenets of International Humanitarian Law.

• The IHL Compliance Crisis

The Principle of Distinction requires combatants to distinguish between civilians and combatants. AI systems excel at recognizing statistical correlations, but distinction often requires understanding legal status and intent, which are inherently contextual (ICRC, 2016). Lavender operationalizes distinction as a probabilistic score based on digital patterns, substituting statistical likelihood for the legal requirement of reasonable certainty, inherently increasing misidentification risk (HRW, 2024).

The Principle of Proportionality requires that anticipated civilian harm not be excessive relative to military advantage. This is an inherently subjective balancing act. Systems like Fire Factory automate Collateral Damage Estimation, but Lavender’s reported use is starker: pre-authorizing civilian casualty ratios reduces the profound moral balancing act to a pre-set, fixed ratio that cannot account for qualitative values or cumulative effects (Sparrow, 2007).

The Principle of Precautions obliges parties to take all feasible precautions to minimize civilian harm. The AI factory tempo eviscerates this obligation. When human operators must review dozens of targets per hour, verification shrinks to a cursory glance, rendering the precautionary step of exploring alternatives impractical.

• The Crystallized Responsibility Gap

If Lavender misidentifies a civilian, who is responsible? Programmers who designed the model? Intelligence officers who defined behavioral patterns? Commanders who set casualty ratios? Human operators who rubber-stamped recommendations? This diffusion makes traditional legal attribution functionally impossible, creating an accountability vacuum (Heyns, 2013). The black box nature of complex machine learning compounds the gap; if a strike causes excessive casualties and the military cannot explain why the AI assigned high probability, it cannot satisfy IHL obligations to investigate (Burrell, 2016).

• Strategic and Normative Backlash

The perceived violations generate tangible strategic costs. The Gaza war has become a central reference point in global legal challenges to military AI. The ICC investigation and UN Commission of Inquiry calls are directly fueled by allegations of AI-driven targeting (ICC, 2021; UN COI, 2024). This represents a direct conversion of technical efficiency into legal and diplomatic vulnerability.

Militant groups frame AI targeting as evidence of a dehumanizing enemy, a potent tool for recruitment and radicalization. Israel’s technological supremacy is thus transformed into a normative liability. A profound danger is that the veneer of high-tech precision anesthetizes publics to systemic civilian risk, potentially lowering the threshold for force and making protracted conflicts more politically sustainable for the user state (Moyn, 2023).

Case Study: The 2023–2025 Gaza War

The post-October 7 campaign revealed a more elaborate AI stack and intensified factory logic. Investigations identified The Gospel for infrastructure targeting and Lavender for person-centric targeting, supplemented by ancillary tools and increasingly reliant on transnational commercial cloud infrastructure (AP, 2025).

Operationally, the expanded stack enabled staggering scale. Intelligence personnel described an AI factory where systems continuously generated targets, with human operators approving strikes in seconds under immense volume (Abraham & Local Call, 2024). This dynamic exemplifies automation bias and de-skilling, where human review became cursory.

This factory logic translated into extensive destruction. Investigations link AI-mediated targeting to broad patterns of strikes on residential buildings, with commanders reportedly authorizing significant civilian casualty ratios per suspected operative (Sabbagh & McKernan, 2024). This demonstrates that AI does not mechanically produce restraint; rather, it amplifies underlying doctrine and policy thresholds.

The conflict has operationalized theoretical tensions, transforming Lavender from a conceptual challenge into the central artifact of a global crisis over IHL’s future. The theoretical responsibility gap has materialized in paralyzed accountability mechanisms, as the opacity of automated processes makes attributing specific strike decisions functionally impossible (UN COI, 2024).

The backlash has moved to concrete policy repercussions. Reports of Israel’s use of commercial cloud infrastructure have triggered shareholder activism and employee protests, illustrating how the normative crisis weaponizes global supply chains (AP, 2025). The term Lavender itself has become international shorthand for dehumanized, automated killing, representing a tangible loss of narrative control.

Conclusion: Implications for International Relations and Conflict Studies

The 2023–2025 Gaza war, particularly the operationalization of systems such as Lavender to generate automated kill lists, confirms that Israel’s AI targeting architecture is not a transient experiment but a deeply institutionalized, next-generation mode of warfighting (Abraham & Local Call, 2024; GRIP, 2025). This paper argued that this model, analyzed through a multi-layered framework synthesizing Realism, Securitization, Actor-Network Theory (ANT), and International Humanitarian Law (IHL), embodies a fundamental paradox. It is a rational, securitized response to asymmetric threats that achieves unprecedented tactical efficiency. Yet, by cyber-physicalizing the kill chain into a high-velocity, data-dependent actor-network, it simultaneously introduces critical new vulnerabilities and triggers a corrosive normative backlash that can eclipse strategic gains. The Gaza war crystallizes this trilemma: the system simultaneously magnifies operational tempo, exposes the kill chain to data-centric attacks and systemic error propagation, and intensifies legal and ethical contestation over responsibility and restraint (Project Ploughshares, 2025; HRW, 2024). As such, Gaza should be understood less as an anomaly than as an early, disturbing preview of how cyber conflict will be reconfigured wherever states embrace AI-mediated targeting as a core instrument of security policy.

Summary of Findings: The Institutionalized Trilemma

The empirical analysis validates the core argument across three interlocking dimensions:

1. Strategic Logic and Securitized Escalation: The post-October 7 environment, interpreted through Realist and Securitization lenses, accelerated the AI targeting model from a tool into a central warfighting doctrine. The existential framing of the threat legitimized the industrial-scale, person-centric targeting enabled by systems like Lavender, demonstrating how technological capability is shaped by and accelerates political-military aims.
2. The Cyber-Physical Actor-Network in Practice: ANT revealed the expanded stack, integrating Lavender, The Gospel, commercial cloud infrastructure, and tracking tools, as a fragile, hybrid assemblage. Its strength (speed, scale) is its critical vulnerability. The war demonstrated acute risks: automation bias in high-tempo validation, dependence on foreign tech infrastructure, and adversary counter-AI tactics that exploit the system’s data dependency.
3. The Normative Reckoning and Responsibility Gap: The conflict has become a global locus for challenging AI’s compatibility with IHL. Lavender epitomizes the “responsibility gap,” diffusing agency across programmers, data analysts, operators, and commanders, while algorithmic opacity undermines post-hoc accountability and the very feasibility of applying the principles of distinction and proportionality.

Theoretical Implications: Rethinking Power, Vulnerability, and Law

This case forces a re-evaluation of core theoretical premises in IR and Conflict Studies.

• For Realism and Security Studies: The pursuit of power and security via complex sociotechnical systems creates new internal logics of vulnerability. Power is no longer merely a function of material capability but of data integrity and network resilience. A state’s military strength can be compromised by data poisoning or a cloud service provider’s policy change, demanding a revised calculus that incorporates cyber-physical dependencies into core strategic assessments.
• For War and Technology Studies (ANT/Sociotechnical Systems): The battlefield must be analyzed as a contested cognitive and digital ecosystem. Victory requires dominance within the actor-network, securing data flows, hardening algorithms against deception, and maintaining the human capacity for critical oversight within accelerated loops. The “center of gravity” shifts from physical command hubs to data fusion centers and model training environments.
• For IHL and Normative Theory: The conflict suggests IHL is experiencing a crisis of enforceability and relevance. When the decision-making apparatus is a black-boxed network distributing agency, traditional accountability mechanisms fail. This necessitates either a significant adaptation of the law, through new protocols on meaningful human control, algorithmic audit trails, and explainability, or its gradual marginalization, creating a dangerous compliance grey zone.

Policy Implications: Navigating the Algorithmic Era

The Gaza precedent offers urgent lessons for militaries, governments, and the international community.

For Militaries Adopting AI Targeting:

• Cyber-Hardening is Core Warfare: Investment must pivot from acquiring AI capabilities to fortifying the entire data lifecycle, securing training data, developing defenses against adversarial AI, and creating redundant, resilient command networks.
• Guard Against Cognitive Hollowing: Doctrine must institutionalize mandatory “slow-thinking” checkpoints, adversarial red-teaming of AI recommendations, and continuous training to preserve human judgement and counteract automation bias.
• Invest in Explainability (XAI) as a Strategic Imperative: The ability to audit and explain targeting decisions is not just technical but essential for legal defense, legitimacy, and operational debugging.

For Adversaries and Civil Society:

The case predicts the proliferation of counter-AI warfare, including low-tech deception (decoys), cyber/data operations, and strategic entrenchment within civilian infrastructure to exploit algorithmic blind spots. Independent forensic investigation (via satellite imagery, OSINT) becomes even more critical as a counterweight to opaque military claims.

For the International Community and Regulators:

• Move Beyond the “Killer Robot” Debate: Regulation must address the existing reality of high-risk AI applications, such as automated kill-list generation and signature strikes. Diplomatic efforts should focus on transparency requirements, export controls on dual-use AI systems, and strengthening IHL implementation mechanisms.
• Leverage Normative and Economic Pressure: The legitimacy crisis surrounding AI targeting provides leverage. States and international organizations should condition military partnerships and technology transfers on adherence to explicit codes of conduct regarding human control, legal review, and casualty reporting.
• Engage the Private Sector: The reliance on commercial cloud and AI models implicates tech corporations in conflict outcomes. Developing frameworks for corporate due diligence in military contracts is an emerging frontier of security policy.

Avenues for Future Research

The Gaza war opens several critical research pathways:

1. Comparative Strategic Cultures: How do other militaries (e.g., the U.S., China, Russia) institutionalize AI targeting? Comparative analysis will reveal whether the Israeli model is a unique product of its asymmetric conflict environment or a template for future warfare.
2. Technical Security of Military AI: Interdisciplinary research is urgently needed on military-grade adversarial AI defenses, robust machine learning in deceptive environments, and secure, sovereign cloud architectures for combat systems.
3. The Evolving “Responsibility Gap”: Empirical legal and sociological studies are required to trace how militaries internally attribute blame for AI-assisted errors and how international courts (e.g., the ICC) approach cases involving algorithmic targeting.
4. Longitudinal Normative Impact: Will the normalization seen in Gaza lead to a broader erosion of IHL, or will it generate a counter-movement and new, robust norms governing autonomy in warfare?

Final Word: Gaza as Harbinger

Israel’s AI targeting systems, as stress-tested in Gaza, are a deployed present, not a speculative future. They exemplify a world where warfare is optimized for data-driven efficiency but becomes riddled with new fragilities and ethical fractures. For scholars and practitioners, this demands moving beyond viewing technology as a mere tool. It is a constitutive force reshaping the landscapes of power, vulnerability, and legitimacy. The ultimate lesson from Gaza is that in the algorithmic age, sustainable military advantage will not belong to the state with the fastest “factory,” but to the one that best resolves the trilemma of tactical efficiency, systemic resilience, and normative legitimacy. The 2023–2025 conflict stands as a stark harbinger: the future of conflict will be defined by our collective failure or success in governing this tension.

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Note: A complete academic version of this paper, including full methodology, extended case analysis, and comprehensive citations, is available upon request. Please contact the author at ahmadmohee@gmail.com

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